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Query-Based Sampling of Text Databases.

1. INTRODUCTION

When many document databases are accessible, the first step of Information Retrieval is deciding where to search. Manual selection can be difficult when there are many databases from which to choose, so researchers have developed automatic content-based database selection algorithms. A content-based selection algorithm ranks a set of text databases by how well each database matches or satisfies the given query [Gravano et al. 1994a; Callan et al. 1995b; Voorhees et al. 1995; Gravano and Garcia-Molina 1995; Weiss et al. 1996; Baumgarten 1997; Yuwono and Lee 1997; Xu and Callan 1998; French et al. 1998; Meng et al. 1998; 1999; Hawking and Thistlewaite 1999; Xu and Croft 1999; French et al. 1999; Fuhr 1999]. Content-based database selection has a number of desirable properties, among them reasonable accuracy, scalability, low computational costs, and ease of use.

Content-based database selection algorithms need information about what each database contains. This information, which we call a resource description, is simply assumed to be available in most prior research. However, in practice, accurate resource descriptions can be difficult to acquire in environments, such as the Internet, where resources are controlled by many parties with differing interests and capabilities. Our interest in the research described here was in studying how accurate resource descriptions can be acquired in multiparty environments.

Recent standardization efforts, such as the proposed STARTS extension to Z39.50 [Gravano et al. 1997], illustrate the problem. STARTS requires every resource provider to provide accurate resource descriptions upon request. We call STARTS a cooperative protocol, because it only succeeds when each resource provider

--is able to provide resource descriptions,

--chooses to provide resource descriptions,

--is able to represent database contents accurately, and

--chooses to represent database contents accurately.

Cooperative protocols are appropriate solutions when all resources are controlled by a single party that can mandate cooperation.

In multiparty environments such as the Internet or large corporate networks, complete cooperation is unlikely. Older database systems may be unable to cooperate; some services will refuse to cooperate because they have no incentive or are allied with competitors; and some services may misrepresent their contents, for example, to lure people to the site. All of these characteristics can be found today on the Internet; some of them also occur in large corporate networks.

One of the most serious problems with cooperative techniques is the great variety in how resource descriptions are created. Most of the prior research is based on descriptions consisting of term lists and term frequency or term weight information [Gravano et al. 1994a; Callan et al. 1995b; Gravano and Garcia-Molina 1995; Voorhees et al. 1995; Meng et al. 1998; Hawking and Thistlewaite 1999]. However, differences in tokenizing, case conversion, stopword lists, stemming algorithms, proper name handling, and concept recognition are common, making it impossible to compare term frequency information produced by different parties, even if all parties are able and willing to cooperate.

For example, which database is best for the query "Apple": a database that contains 2000 occurrences of "appl", a database that contains 500 occurrences of "apple", or a database that contains 50 occurrences of "Apple"? The answer requires detailed knowledge about the stopping, stemming, case conversion, and proper name handling performed by each database. Each database could be required to reveal that information, too, but complying would be difficult. Researchers attempting to make identical indexing choices with two different IR systems often find it difficult to identify all of the deliberate choices, system quirks, and outright errors that lead to a particular term statistic. A resource selection algorithm that depended on that level of detail seems impractical.

Resource selection algorithms require accurate and consistent resource descriptions. However, the weaknesses of cooperative protocols make them an unsuitable solution for environments where resources are controlled by many parties. In these environments, a different solution is required.

Query-based sampling is a recently developed method of acquiring resource descriptions that does not require explicit cooperation from resource providers [Callan et al. 1999]. Instead, resource descriptions are created by running queries and examining the documents that are returned. Resource descriptions can be guaranteed to be compatible because they are created under the control of the sampling process, not each individual resource provider. Preliminary experiments suggested that query-based sampling is an effective and efficient method of acquiring resource descriptions.

The preliminary experiments studied how closely a resource description created by sampling (a learned resource description) matched the actual resource description for a text database [Callan et al. 1999]. The results were encouraging but inconclusive, in part due to a flawed experimental methodology. This paper reproduces the earlier experiments using an improved experimental methodology. It also extends the prior research by investigating the effects of learned resource descriptions on a resource selection task. The result is a comprehensive study of the efficiency, effectiveness, and robustness of query-based sampling under a variety of conditions.

The next section reviews prior research on resource selection and distributed information retrieval, emphasizing the information requirements of several representative algorithms. Section 3 describes query-based sampling in more detail. Sections 4, 5, and 6 describe experiments that test basic hypotheses about query-based sampling, its sensitivity to parameter settings, and the efficacy of the resulting resource descriptions for resource selection and multidatabase retrieval. Section 7 discusses the use of query-based sampling for summarizing database contents. Section 8 discusses other uses for query-based sampling, and Section 9 concludes.

2. PRIOR RESEARCH

Automatic selection among text databases has been studied since at least the early 1980's, when the EXPERT CONIT system was developed [Marcus 1983]. CONIT used a rule-based system to select among a small set of databases, but few details were published about how database contents were represented or matched to queries.

A variety of different approaches to database selection were developed beginning in the mid 1990's. The most common approach is exemplified by gGlOSS [Gravano et al. 1994a; Gravano and Garcia-Molina 1995], CORI [Callan et al. 1995b; Xu and Callan 1998], Cue Validity Variance (CVV) [Yuwono and Lee 1996], and Xu's language modeling approach [Xu and Croft 1999]. This family of algorithms represents database contents by the words contained in the database, and by statistics computed from word frequencies. These algorithms can be viewed as adapting document-ranking representations and algorithms to the database-ranking task. They are easily scaled to large numbers of databases, are computationally efficient, and no manual effort is required to create or update database descriptions. The main problem in applying this family of algorithms is obtaining accurate descriptions of each database.

Query clustering and RDD [Voorhees et al. 1995] are algorithms that rank databases using information about the distribution of relevant documents for similar queries seen in the past. These two algorithms represent databases by their prior effectiveness for past queries, which makes it easy to control their behavior relatively precisely without knowing anything about the contents of the database. They also make it easy to integrate databases served by different search engines [Voorhees and Tong 1997], because there is no need to compare representations or frequencies produced by different search engines. The main problem in applying these algorithms is that relevance judgments require manual effort, so it can be expensive to apply them when there are many databases or databases that are updated often.

FreeNet [Clarke et al. 2000] is a peer-to-peer search algorithm that passes queries from node to node in a network until either a search horizon is reached or the query is satisfied. FreeNet nodes keep track of which other nodes have been successful in satisfying past queries, where "satisfying" is defined as matching a Boolean query. That information is used later to route new queries. Although its architecture is rather different, the FreeNet search strategy is similar to the strategies of the RDD and Query Clustering algorithms under a weaker definition of relevance.

Query probing [Hawking and Thistlewaite 1999] is an algorithm that sends a two-word subset of a query (a "probe query") to each database to discover how often the words occur and cooccur in each database. Query probing requires no advance knowledge of the contents of each database, so it is easy to apply in environments that change often. However, query probing requires a method of generating good probe queries, requires that each database cooperate by providing the requested statistics, and it can entail significant communications costs in wide-area networks or when large numbers of databases are available.

There are other database selection algorithms (e.g., Weiss et al. [1996], Baumgarten [1997], and Craswell et al. [2000]), but they require similar information. Database selection algorithms may differ significantly in their architectures and assumptions, but they usually represent database contents in one of two ways: (i) with information about queries satisfied in the past, or (ii) with term frequency information. When using a database selection algorithm, one must have a strategy for obtaining the required information and keeping it current.

Our research interest is in database selection algorithms that represent database contents by term frequency information, because the algorithms are effective and easy to scale to large numbers of databases [Gravano and Garcia-Molina 1995; French et al. 1999; Powell et al. 2000]. The principal problem in applying these algorithms is determining the contents of each database.

One solution is for databases to exchange term frequency information periodically [Viles and French 1995]. This approach was formalized in STARTS [Gravano et al. 1997], a standard protocol for describing database contents. A STARTS description lists the indexing terms used in a database, their frequencies, and information about word stemming, stopwords, and other indexing choices that affect frequency information. STARTS was designed to be applied on a large scale, e.g., as part of the Z39.50 protocol for communicating with information retrieval systems [National Information Standards Organization 1995]. However, as described above (Section 1), STARTS assumes that it is possible to compare frequency information provided by different parties, which is rarely true in practice. The STARTS protocol may be the preferred solution when all databases are controlled by a single party, but solutions such as query-based sampling are required when databases are controlled by many parties.

3. QUERY-BASED SAMPLING

Our goal is a method of acquiring resource descriptions that is not overly complex, that does not require special cooperation from resource providers, that can be applied to older ("legacy") systems, that is difficult (but not necessarily impossible) to deceive, and that is not sensitive to indexing choices made by resource providers.

It is well known that the characteristics of a population can be determined to a desired degree of accuracy by random sampling. It is also well known that word occurrence patterns in a corpus are very skewed. Zipf's Law states that a word's rank multiplied by its frequency is approximately equal to a constant [Zipf 1949]. The skew described by Zipf's Law means that usually 75% of the unique words in a corpus occur 3 or fewer times [Heaps 1978]. A sampling technique might produce a very accurate indication of database contents even if fails to find a very large percentage of the words.

Heaps' Law provides further support for discovering database contents by sampling. Heaps' Law states that the size of a corpus vocabulary can be estimated by V [approximately equals] [KN.sup.[Beta]], where K [approximately equals] 20, N is the number of corpus word occurrences, and 0.4 [is less than or equal to] [Beta] [is less than or equal to] 0.6 [Heaps 1978]. As a corpus is scanned, the vocabulary initially grows very rapidly, albeit exhibiting the frequency skew described by Zipf. As scanning continues, the vocabulary growth rate tapers off. Heap's Law suggests that it is not necessary to examine much of a corpus in order to discover most of its vocabulary.

Random selection is a cooperative method of discovering database contents, because it depends upon the provider to select documents randomly from its database, which the provider might or might not do. Random selection is not a solution, but it suggests a solution.

Third parties can obtain biased samples of databases by running queries and examining the documents returned in response. We call this query-based sampling, to emphasize the biased nature of each sample. Query-based sampling satisfies all of the criteria outlined above, because it assumes only that database providers perform their usual service of running queries and returning documents.

Our central hypothesis is that a sufficiently unbiased sample of documents can be constructed from the union of biased samples obtained by query-based sampling.

Query-based sampling is implemented with a simple algorithm, outlined below.

(1) Select an initial query term.

(2) Run a one-term query on the database.

(3) Retrieve the top N documents returned by the database.

(4) Update the resource description based on the characteristics of the retrieved documents.

(a) Extract words and frequencies from the top N documents returned by the database; and

(b) Add the words and their frequencies to the learned resource description.

(5) If a stopping criterion has not yet been reached,

(a) Select a new query term; and

(b) Go to Step 2.

The algorithm involves several choices, e.g., how query terms are selected, how many documents to examine per query, and when to stop sampling. Discussion of these choices is deferred to later sections of the paper.

How best to represent a large document database is an open problem. However, much of the prior research is based on simple resource descriptions consisting of term lists, term frequency or term weight information, and information about the number of documents [Gravano et al. 1994a; Gravano and Garcia-Molina 1995; Voorhees et al. 1995] or number of words [Callan et al. 1995b; Xu and Callan 1998; Xu and Croft 1999] contained in the resource. Zipf's Law and Heap's Law suggest that relatively accurate estimates of the first two pieces of information--term lists and the relative frequency of each term--can be acquired by sampling [Heaps 1978; Zipf 1949].

It is not clear whether the size of a resource can be estimated with query-based sampling, but it is also not clear that this information is actually required for accurate database selection. We return to this point later in the paper.

The hypothesis motivating our work is that sufficiently accurate resource descriptions can be learned by sampling a text database with simple "free-text" queries. This hypothesis can be tested in two ways:

(1) by comparing resource descriptions learned by sampling known databases ("learned resource descriptions") with the actual resource descriptions for those databases and

(2) by comparing resource selection accuracy using learned resource descriptions with resource selection using actual resource descriptions.

Both types of experiments were conducted and are discussed below.

4. EXPERIMENTAL RESULTS: DESCRIPTION ACCURACY

The first set of experiments investigated the accuracy of learned resource descriptions as a function of the number of documents examined. The experimental method was based on comparing learned resource descriptions for known databases with the actual resource descriptions for those databases.

The goals of the experiments were to determine whether query-based sampling learns accurate resource descriptions, and if so, what combination of parameters produce the fastest or most accurate learning. A secondary goal was to study the sensitivity of query-based sampling to parameter settings.

The following sections describe the data, the type of resource description used, the metrics, parameter settings, and finally, experimental results.

4.1 Data

Three full-text databases were used:
CACM:       a small, homogeneous set of titles and abstracts of
            scientific articles from the Communications of the ACM;

WSJ88:      the 1988 Wall Street Journal, a medium-sized corpus of
            American newspaper articles;(1) and

TREC-123:   a large, heterogeneous database consisting of TREC
            CDs 1, 2, and 3, which contains newspaper articles,
            magazine articles, scientific abstracts, and government
            documents [Harman 1995].


These are standard test corpora used by many researchers. Their characteristics are summarized in Table I.
Table I. Test Corpora

           Size,               Size, in
           in      Size, in     Unique     Size, in
Name       Bytes   Documents     Terms     Total Terms    Variety

CACM         2MB       3,204       6,468       117,473   homogeneous
WSJ88      104MB      39,904     122,807     9,723,528   heterogeneous
TREC-123   3.2GB   1,078,166   1,134,099   274,198,901   very hetero-
                                                         genenous


4.2 Resource Descriptions

Experiments were conducted on resource descriptions consisting of index terms (usually words) and their document frequencies, df (the number of documents containing each term).

Stopwords were not discarded when learned resource descriptions were constructed. However, during testing, learned and actual resource descriptions were compared only on words that appeared in the actual resource descriptions, which effectively discarded from the learned resource description any word that was considered a stopword by the database. The databases each used the default stopword list of the INQUERY IR system [Turtle and Croft 1991; Turtle 1991; Callan et al. 1995a], which contained 418 very frequent and/or closed-class words.

Suffixes were not removed from words ("stemming") when resource descriptions were constructed. However, during controlled testing, suffixes were removed prior to comparison to the actual resource description, because the actual resource descriptions (the database indexes) were stemmed.

4.3 Metrics

Resource descriptions consisted of two types of information: a vocabulary, and frequency information for each vocabulary term. The correspondence between the learned and actual vocabularies was measured with a metric called ctf ratio. The correspondence between the learned and actual frequency information was measured with the Spearman Rank Correlation Coefficient. Each metric is described below.

4.3.1 Measuring Vocabulary Correspondence: Ctf Ratio. The terms in a learned resource description are necessarily a subset of the terms in the actual description. One could measure how many of the database terms are found during learning, but such a metric is skewed by the many terms occurring just once or twice in a collection [Zipf 1949; Heaps 1978]. We desired a metric that gave more emphasis to the frequent and moderately frequent terms, which we believe convey the most information about the contents of a database.

Ctf ratio is the proportion of term occurrences in the database that are covered by terms in the learned resource description. For a learned vocabulary V' and an actual vocabulary V, the ctf ratio is

(1) [[Sigma].sub.i[element of]V'][ctf.sub.i]/ [[Sigma].sub.i[element of]V'][ctf.sub.i]

where [ctf.sub.i] is the number of times term i occurs in the database (collection term frequency, or ctf). A ctf ratio of 80% means that the learned resource description contains the terms that account for 80% of the term occurrences in the database.

For example, suppose a database consists of 4 occurrences of "apple", 1 occurrence of "bear", 3 occurrences of "cat", and 2 occurrences of "dog" (Table II). If the learned resource description contains only the word "apple" (25% of the actual vocabulary terms), the ctf ratio is 4/10 = 40%, because the word "apple" accounts for 40% of the word occurrences in the database. If the learned resource description contains both "apple" and "cat", the ctf ratio is 70%. ctf ratio measures the degree to which the learned resource description contains the words that are frequent in the actual resource description.
Table II. ctf Ratio Example

 Actual Resource
  Description       Learned Resource Descriptions

Vocabulary   ctf           Vocabulary   ctf ratio

apple         4    LRD 1   apple           40%
bear          1    LRD 2   bear            10%
cat           3    LRD 3   apple, cat      70%
dog           2


Note that the ctf ratios reported in this paper are not artificially inflated by finding stopwords, because ctf ratio was always computed after stopwords were removed.

4.3.2 Spearman Rank Correlation Coefficient. The second component of a resource description is document frequency information (df), which indicates the relative importance of each term in describing the database. The accuracy of frequency information can be determined either by comparison of learned and actual df values after appropriate scaling, or by comparison of the frequency-based term rankings produced by learned and actual df values. The two measurement methods emphasize different characteristics of the frequency information.

Direct comparison of df values has the undesirable characteristic that the comparison is biased in favor of estimates based on larger amounts of information, because estimates based on 10n documents enable only n digits of accuracy in scaled values. This characteristic was a concern because even relatively noisy df estimates based on small numbers of documents might be sufficient to enable accurate resource selection.

Term rankings produced by learned and actual df values can be compared by the Spearman Rank Correlation Coefficient, an accepted metric for comparing two orderings. The Spearman Rank Correlation Coefficient is defined [Press et al. 1992] as

(2) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

where [d.sub.i] is the rank difference of common term i, n is the number of terms, [f.sub.k] is the number of ties in the kth group of ties in the learned resource description, and [g.sub.m] is the number of ties in the mth group of ties in the actual resource description. Two orderings are identical when the rank correlation coefficient is 1. They are uncorrelated when the coefficient is 0, and they are in reverse order when the coefficient is -1.

The complexity of this variant of the Spearman Rank Correlation Coefficient may surprise some readers. Simpler versions are more common (e.g., Moroney [1951]). However, simpler versions assume a total ordering of ranked elements; two elements cannot share the same ranking. Term rankings have many terms with identical frequencies, and hence identical rankings. Variants of the Spearman Rank Correlation Coefficient that ignore the effects of tied rankings can give misleading results, as was the case in our initial research on query-based sampling [Callan et al. 1999].

The Spearman Rank Correlation Coefficient was computed using just the terms in the intersection of V and V'. Use of the intersection is appropriate because the Spearman Rank Correlation Coefficient is used to discover whether the terms in V' are ordered appropriately by the learned frequency information.

Database selection does not require a rank correlation coefficient of 1.0. It is sufficient for the learned resource description to represent the relative importance of index terms in each database to some degree of accuracy. For example, it might be sufficient to know the ranking of a term [+ or -] 5%. Although most database selection algorithms are likely to be insensitive to small ranking errors, it is an open question how much error a given algorithm can tolerate before selection accuracy deteriorates.

4.4 Parameters

Experiments with query-based sampling require making choices about how query terms are selected and how many documents are examined per query.

In our experiments, the first query run on a database was determined by selecting a term randomly from the TREC-123 vocabulary. The initial query could be selected using other criteria, e.g., selecting a very frequent term, or it could be selected from another resource. Several informal experiments found that the choice of the initial query term had minimal effect on the quality of the resource description learned and the speed of learning, as long as it retrieved at least one document.

Subsequent query terms were chosen by a variety of methods, as described in the following sections. However, in all cases the terms chosen were subject to requirements similar to those placed on index terms in many text retrieval systems: a term selected as a query term could not be a number and was required to be three or more characters long.

We had no hypotheses to guide the decision about how many documents to sample per database query. Instead, a series of experiments was conducted to determine the effect of varying this parameter.

The experiments presented below were ended after examining 500 documents. This stopping criteria was chosen empirically after running several initial experiments, and were biased by our interest in learning resource descriptions from small (ideally, constant) sized samples. Several experiments with each database were continued until several thousand documents were sampled, to ensure that nothing unusual happened.

4.5 Results

Four sets of experiments were conducted to study the accuracy of resource descriptions learned under a variety of conditions. The first set of experiments was an initial investigation of query-based sampling with the parameter settings discussed above. We call these the baseline experiments. A second set of experiments studied the effect of varying the number of documents examined per query. A third set of experiments studied the effect of varying the way query terms were selected. A fourth set of experiments studied the effect of varying the choice of the collection from which documents were picked. Each set of experiments is discussed separately below.

4.5.1 Results of Baseline Experiments. The baseline experiments were an initial investigation of query-based sampling. The goal of the baseline experiments was to determine whether query-based sampling produced accurate resource descriptions, and if so, how accuracy varied as a function of the total number of documents examined.

The initial query term was selected randomly from the TREC-123 resource description, as described above. Subsequent query terms were selected randomly from the resource description being learned.

The top four documents retrieved by each query were examined to update the resource description. Duplicate documents, i.e., documents that had been retrieved previously by another query, were discarded; hence some queries produced fewer than four documents.

Ten trials were conducted, each starting from a different randomly selected query term, to compensate for the effects of random query term selection. The experimental results reported here are averages of results returned by the 10 trials.

The variation in the measurements obtained from each trial on a particular database was large (10-15%) at 50 documents, but decreased rapidly. At 150 documents it was 4-5%, and at 250 documents it was 2-4%. The consistency among the trials suggests that the choice of the initial query term is not particularly important, as long as it returns at least one document. (The effects of different strategies for selecting subsequent query terms are addressed in Section 4.5.3.)

Figure 1(a) shows that query-based sampling quickly finds the terms that account for 80% of the nonstopword term occurrences in each collection.(2) After about 250 documents, the new vocabulary being discovered consists of terms that are relatively rare in the corpus, which is consistent with Zipf's law [Zipf 1949].

[GRAPHS OMITTED]

Figure 1(b) shows the degree of agreement between the term orderings in the learned and actual resource descriptions, as measured by the Spearman Rank Correlation Coefficient. A high degree of correlation between learned and actual orderings is observed for all collections after seeing about 250 documents. The correlation observed for the largest collection (TREC-123) is less than the correlations observed for the smaller collections (CACM and WSJ88). Extending the number of documents sampled beyond 500 does not substantially improve the correlation measure on this large collection.

Results from both metrics support the hypothesis that accurate resource descriptions can be learned by examining only a small fraction of the collection. This result is encouraging, because it suggests that query-based sampling is a viable method of learning accurate resource descriptions.

4.5.2 Results of Varying Sample Size. The baseline experiments sampled the four most highly ranked documents retrieved for each query. However, the sampling process could have retrieved more documents, or fewer documents, per query. Doing so could change the number of queries and/or documents required to achieve a given level of accuracy, which in turn could affect the costs of running the algorithm.

A series of experiments was conducted to investigate the effects of varying the number of documents examined per query. Values of 1, 2, 4, 6, 8, and 10 documents per query were tested. As in the prior experiment, 10 trials were conducted for each value, each trial starting from a different randomly selected query term, with subsequent query terms chosen randomly from the resource description being learned. Each experimental result reported below is an average of the experimental results from 10 trials.

Varying the number of documents per query had little effect on the speed of learning, as measured by the average number of documents required to reach a given level of accuracy. Indeed, the effect was so small that it is difficult to display the results of different values on a single graph. Figure 2 shows results for values of 1, 4, and 8 documents per query on each database. Results for values of 2, 6, and 10 were very similar.

[GRAPHS OMITTED]

Table III provides another perspective on the experimental results. It shows the number of documents required to reach a ctf ratio of 80%. Varying the number of documents examined per query from 1 to 10 caused only minor variations in performance for 2 of the 3 databases.
Table III. Effect of Varying the Number of Documents Examined per
Query on How Long It Takes a Sampling Method to Reach a ctf Ratio
of 80%

                  CACM               WSJ88             TREC-123

Documents   Total              Total              Total
per Query   Docs    Spearman   Docs    Spearman   Docs    Spearman

    1        257      0.80      113      0.76      183      0.70
    2        242      0.80      116      0.74      200      0.65
    4        232      0.80      126      0.75      239      0.68
    6        236      0.80      122      0.74      241      0.68
    8        236      0.81      111      0.74      244      0.69
   10        233      0.81      120      0.74      246      0.66


Careful study reveals that examining more documents per query results in slightly faster learning (fewer queries required) on the small, homogeneous CACM database; examining fewer documents per query results in somewhat faster learning on the larger, heterogeneous TREC-123 database. However, the effects of varying the number of documents per query are, on average, small. The most noticeable effect is that examining fewer documents per query results in a more consistent learning speed on all databases. There was greater variation among the 10 trials when 10 documents were examined per query ([approximately equals] 3-5%) than when I document was examined per query ([approximately equals] 1-3%).

In this experiment, larger samples worked well with the small homogeneous collection, and smaller samples worked well with the large heterogeneous collection. We do not find this result surprising. Samples are biased by the queries that draw them; the documents within a sample are necessarily similar to some extent. We would expect that many small samples would better approximate a random sample than fewer large samples in collections where there is significant heterogeneity. The results support this intuition.

4.5.3 Results of Varying Query Selection Strategies. The baseline experiments select query terms randomly from the resource description being learned. Other selection criteria could be used, or terms could be selected from other sources.

One hypothesis was that it would be best to select terms that appear to occur frequently in the collection, i.e., words that are nearly frequent enough to be stopwords, because they would return the most random sample of documents. We tested this hypothesis by selecting frequent query terms, as measured by document frequency (df), collection term frequency (ctf), and average term frequency (avg_tf = ctf/df).

One early concern was that learned resource descriptions would be strongly biased by the set of documents that just happened to be examined first, and that this bias would be reinforced by selecting additional query terms from the learned resource description. A solution would be to select terms from a different, more complete resource description. This hypothesis was named the other resource description, or ord hypothesis, and was compared to the default learned resource description or lrd approach used in the other experiments. The complete TREC-123 resource description served as the "other" resource description.

The choice of TREC-123 as the "other" resource description might be challenged, because WSJ88 is a subset of TREC-123. It is possible that TREC-123 might be a biased, or an unrealistically good, "other" resource description from which to select terms for sampling WSJ88. We were aware of this possible bias, and were prepared to conduct more thorough experiments if the initial results appeared to confirm the "other" resource description hypothesis.

A series of experiments was conducted, following the same experimental methodology used in previous experiments, except in how query terms were selected. Query terms were selected either randomly or based on one of the frequency criteria, from either the learned resource description (lrd) or the "other" resource description (ord). Four documents were examined per query. Ten trials were conducted for each method that selected query terms randomly or from the learned resource description (lrd), to compensate for random variation and order effects. Experiments were conducted on all three collections, but results were sufficiently similar that only results for the WSJ88 collection are presented here.

In all of the experiments, selecting terms from the "other" resource description produced faster learning, as measured by the number of documents required to reach a given level of accuracy (Figure 3). The differences were statistically significant for all four term selection methods (t test, p [is less than] 0.01). However, the differences were relatively large for the avg_tf and random selection methods, and were statistically significant after only 20 documents were observed; the differences were small for the ctf and df selection methods, and required 130 and 190 documents respectively to achieve statistical significance (Table IV). There might be some value to using an other resource description for avg_tf and random term selection methods, but there appears to be little value for the ctf and df selection methods.

[GRAPH OMITTED]
Table IV. The Differences between Selecting Query Terms from an Other
Resource Description (ord) or Learned Resource Description (lrd).
"Significant At & Above" is the point on the curves in Figure 3 at
which the difference between selecting from ord and lrd resources
becomes statistically significant (t test, p < 0.01). Values for
learned resource descriptions and the random selection method are
averages of 10 trials.

                                   ctf ratio

          Signi-
Selec-    ficant     100 Documents     200 Documents     300 Documents
tion      At &
Method    Above      ord      lrd      ord      lrd      ord      lrd

avg_tf    20 docs   0.8651   0.8026   0.8989   0.8552   0.9130   0.8779
random    20 docs   0.8452   0.7787   0.8859   0.8401   0.9067   0.8678
ctf      190 docs   0.7920   0.7774   0.8412   0.8310   0.8625   0.8558
df       130 docs   0.7895   0.7641   0.8374   0.8234   0.8580   0.8511


One weakness of selecting query terms from an other resource description is that it can provide terms that do not appear in the target resource ("out-of-vocabulary" query terms). This characteristic is particularly noticeable with avg_tf and random term selection. Avg_tf and random selection from an other resource description produced the most accurate results (Table IV), but required many more queries to retrieve a given number of unique documents due to "out-of-vocabulary" queries (Table V). Recall also that the "other" resource description (TREC-123) was a superset of the target database (WSJ88). The number of failed queries might have been higher if the "other" resource description had been a less similar database.
Table V. The Number of Queries Required to Retrieve 300 Documents Using
Different Query Selection Criteria

            Ran-   Ran-
Selection   dom,   dom,   avg_tf,   avg_tf,   df,   df,   ctf,   ctf,
Strategy    ord    lrd      ord       lrd     ord   lrd   ord    lrd

No. of      378     84     6,673      112     78    154    77    154
  queries


The experiments demonstrate that selecting query terms from the learned resource description, as opposed to a more complete "other" resource description, does not produce a strongly skewed sample of documents. Indeed, random and avg_tf selection of query terms from the learned resource description provided the best balance of accuracy and efficiency in these experiments. The worst-case behavior, obtained with an other resource description that is a poor match for the target resource, would also favor selecting terms from the learned resource description.

The experiments also demonstrate that selecting query terms randomly from the learned resource description is more effective than selecting them based on high frequency. This result was a surprise, because our hypothesis was that high-frequency terms would either occur in many contexts, or would have relatively weak contexts, producing a more random sample. That hypothesis was not supported by the experiments.

4.5.4 Results of Varying the Databases Sampled. The results of the experiments described in the preceding sections support the hypothesis that database contents can be determined by query-based sampling. However, they do not rule out a competing hypothesis: that a relatively random sample of documents from nearly any American English database would produce an equally accurate description of the three test databases. Perhaps these experiments merely reveal properties of American discourse, e.g., that certain words are used commonly.

If the competing hypothesis is true, then query-based sampling is not necessary; a partial description from any relatively similar resource would produce similar results at lower computational cost. More importantly, it would cast doubt on whether partial resource descriptions distinguish databases sufficiently to enable accurate database selection. If the partial resource descriptions for most American English databases are very similar, a database selection algorithm would presumably have great difficulty identifying the databases that best match a specific information need.

A series of experiments was conducted to test the hypothesis that relatively random samples of documents from different American English database would produce equally accurate descriptions of the three test databases.

The experimental method consisted of comparing the resource descriptions created by query-based sampling of various databases to the actual, complete resource description for the test databases. For example, resource descriptions created by query-based sampling of CACM, WSJ88, and TREC-123 databases were compared to the actual description for the CACM database (Figures 4(a) and 4(b)). The hypothesis would be supported if each of the learned resource descriptions were roughly comparable in how well they matched the actual, complete resource description of a particular database.

[GRAPH OMITTED]

Experiments were conducted with the CACM, WSJ88, and TREC-123 databases. Comparisons were performed over 300-500 examined documents. The experimental results are summarized in Figure 4.

The experimental results indicate that a description learned for one resource, particularly a large resource, can contain the vocabulary that occurs frequently in other resources. For example, the resource descriptions learned for the TREC-123 database contained the vocabulary that is frequent, and presumably important, in the WSJ88 and CACM databases (Figures 4(a) and 4(c)). The results also suggest that prior knowledge of database characteristics might be required to decide which descriptions to use for each database. The CACM resource description, for example, lacked much of the vocabulary that is important to both the WSJ88 and TREC-123 resources (Figures 4(c) and 4(e)).

The problem with using the description learned for one resource to describe another, different resource is more apparent when relative term frequency is considered. Relative term frequency is important because it indicates which terms are common in a database, and most database selection algorithms prefer databases in which query terms are common. In these experiments, the relative frequency of vocabulary items in the three test databases was rarely correlated (Figures 4(b), 4(d), and 4(f)). For example, neither the WSJ88 nor the TREC-123 databases gave an accurate indication of relative term frequency in the CACM database (Figure 4(b)). Likewise, neither the CACM nor the TREC-123 database gave an accurate indication of term frequency for the WSJ88 database (Figure 4(d)). The one exception to this trend was that the WSJ88 database did appear to give a relatively accurate indication of relative term frequency in the TREC-123 database (Figure 4(f)).(3)

These experiments refute the hypothesis that the experimental results of the earlier sections are based upon language patterns that are common across different collections of American English text. There may be considerable overlap of vocabulary among the different databases, but there are also considerable differences in the relative frequencies of terms in each database. For example, the term "computer" occurs in all three databases, but its relative frequency is much higher in the CACM database than in the WSJ88 and TREC-123 databases.

Postexperiment analysis indicates that an improved experimental methodology would provide even stronger evidence refuting the alternate hypothesis. The ctf ratio does not measure the fact that the description learned for TREC-123 contains many terms not in the CACM database (Figure 4(a)). Hence, the ctf ratio results in Figures 4(a), 4(c), and 4(e) can overstate the degree to which the learned vocabulary from one database reflects the actual vocabulary of a different database. A large dictionary of American English would yield a ctf ratio close to 1.0 for all three of our databases, but few people would argue that it accurately described any of them.

5. EXPERIMENTAL RESULTS: SELECTION ACCURACY

The experiments described in the previous section investigate how quickly and reliably the learned resource description for a database converges upon the actual resource description. However, we do not know how accurate a resource description needs to be for accurate resource selection. Indeed, we do not even know that description accuracy is correlated with selection accuracy, although we presume that it is.

The second group of experiments investigated the accuracy of resource selection as a function of the number of documents examined. The experimental method was based on comparing the effectiveness of the database-ranking algorithm when using complete and learned resource descriptions. Databases were ranked with the INQUERY IR system's default database-ranking algorithm [Callan 2000; Callan et al. 1995b].

The following sections describe the data, the type of resource description used, the metrics, parameter settings, and finally, experimental results.

5.1 Data

The TREC-123 database described above (Section 4.1) was divided into 100 smaller databases of roughly equal size (about 33 megabytes each), but varying in the number of documents they contained (Table VI). Each database contained documents from a single source, ordered as they were found on the TREC CDs; hence documents in a database were also usually from similar time frames. CD 1 contributed 37 databases. CD 2 contributed 27 databases, and CD 3 contributed 36 databases.
Table VI. Summary Statistics for the 100 Databases in the Testbed

            Documents Per Database            Bytes Per Database

Resource   Mi-
Descrip-   ni-    Ave-    Maxi-
  tion     mum    rage     mum      Minimum      Average      Maximum

 Actual    752   10,782   39,723   28,070,646   33,365,514   41,796,822
Learned    300    300      300      229,915      2,701,449   15,917,750


Queries were based on TREC topics 51-150 [Harman 1994]. We used query sets INQ001 and INQ026, both created by the UMass CIIR as part of its participation in TREC-2 and Tipster 24-month evaluations [Callan et al. 1995a]. Queries in these query sets are long, complex, and have undergone automatic query expansion.

The relevance assessments were the standard TREC relevance assessments supplied by the U.S. National Institute for Standards and Technology [Harman 1994].

5.2 Resource Descriptions

Each experiment used 100 resource descriptions (one per database). Each resource description consisted of a list of terms and their document frequencies (df), as in previous experiments. Terms on a stopword list of 418 common or closed-class words were discarded. The remaining terms were stemmed with KStem [Krovetz 1995].

5.3 Metrics

Several methods have been proposed for evaluating resource selection algorithms [Gravano et al. 1994b; Gravano and Garcia-Molina 1995; Callan et al. 1995b; Lu et al. 1996; French et al. 1998]. The most appropriate for our needs is a recall-oriented metric called R [French et al. 1998; 1999] that measures the percentage of relevant documents contained in the n top-ranked databases.(4) R is defined as

(3) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

where n is the number of databases searched, N is the total number of databases, and [R.sub.i] is the number of relevant documents contained by the ith database.

R is a cumulative metric; R (2) [is less than or equal to] R(3), because searching the top three databases always returns at least as many relevant documents as searching just the top two databases.

R is a desirable metric when the accuracy of the database-ranking algorithm is to be measured independently of other system components, and when the goal is to rank databases containing many relevant documents ahead of databases containing few relevant documents.

5.4 Parameter Settings

The experiments in Section 4 suggested that any relatively small sample size is effective, and that different choices produce only small variations in results. We chose a sample size of four (four documents per query), to be consistent with the baseline results in previous experiments. Query terms were chosen randomly from the learned resource description, as in the baseline experiments.

It was unclear from the experiments in Section 4 when enough samples had been taken. We chose to build resource descriptions from samples of 100 documents (about 25 queries), 300 documents (about 75 queries), and 700 documents (about 175 queries) from each database, in order to cover the space of "reasonable" numbers of samples. If results varied dramatically, we were prepared to conduct additional experiments.

The collection-ranking algorithm itself forces us to set one additional parameter. The collection-ranking algorithm normalizes term frequency statistics [df.sub.i,j] using the length, in words, of the collection ([cw.sub.j]) [Callan et al. 1995b]. However, we do not know how to estimate collection size with query-based sampling. In our experiments, term frequency information (df) was normalized using the length, in words, of the set of sampled documents used to construct the resource description.

5.5 Experimental Results

The experimental results are summarized in the two graphs in Figure 5 (one per query set). The baseline in each graph is the curve showing results with the actual resource description ("complete resource descriptions"). This is the best result that the collection-ranking algorithm can produce when given a complete description for each collection.

[GRAPH OMITTED]

Our interest is in the difference between what is achieved with complete information and what is achieved with incomplete information. Both graphs show only a small loss of effectiveness when resource descriptions are based on 700 documents. Losses grow as less information is used, but the loss is small compared to the information reduction. Accuracy at "low recall," i.e., when only 10-20% of the databases are searched, is quite good, even when resource descriptions are based on only 100 documents.

These results are consistent with the results presented in Section 4. The earlier experiments showed that term rankings in the learned and actual resource descriptions were highly correlated after examining 100-300 documents.

These experimental results also demonstrate that it is possible to rank databases without knowing their sizes. The size of the pool of documents sampled from a database was an effective surrogate for actual database size in these tests. Our testing did not reveal whether this result is, in general, a characteristic of the CORI database selection algorithm, or a quirk due to the 100-database testbed. The distribution of database sizes in the testbed ranged from 752 documents to 39,723 documents, and from 28 megabytes to 42 megabytes (Table VI). A more thorough study of this characteristic would require testbeds with a wider variety of size distributions.

6. EXPERIMENTAL RESULTS: RETRIEVAL ACCURACY

The experiments described in the previous section demonstrate that resource descriptions learned with query-based sampling enable accurate resource ranking. Accurate resource ranking is generally viewed as a prerequisite to accurate document retrieval, but it is not a guarantee. The final document ranking depends upon how results from different databases are merged, which can be influenced by the quality of the resource descriptions for each database.

A third group of experiments investigated the accuracy of document retrieval in the presence of learned resource descriptions. The experimental method was based on comparing the accuracy of the final document rankings produced by a distributed IR system when it uses complete and learned resource descriptions to make decisions about where to search. Databases were ranked, selected, and searched, and results were merged into a final document ranking by the INQUERY IR system's default database-ranking and result-merging algorithms [Callan 2000].

6.1 Data

The data consisted of the same 100 databases that were used to test database selection accuracy. Section 5.1 provides details.

6.2 Resource Descriptions

Each database was described by a learned resource description created from a sample of 300 documents, as done in other experiments (four documents per query, query terms chosen randomly from the learned resource description). A sample size of 300 documents was chosen because in previous experiments it provided reasonably accurate resource descriptions at a relatively low cost (about 75 queries per database).

Each of the 100 resource descriptions (one per database) consisted of a list of terms and their document frequencies (df), as in previous experiments. Terms on a stopword list of 418 common or closed-class words were discarded. The remaining terms were stemmed with Kstem [Krovetz 1995].

6.3 Metrics

The effectiveness of archival search systems is often measured either by Precision at specified document ranks, or by precision at specified recall points. Precision at specified recall points (e.g., "11-point Recall") was the standard for many years, because it normalizes results based on the number of relevant documents; results for "easy" queries (many relevant documents) and "hard" queries (few relevant documents) are more comparable. However, when there are many relevant documents, as can be the case with large databases, Precision at specified Recall points focuses attention on results that are irrelevant to many search patrons (e.g., at rank 50 and 100).

Precision at specified document ranks is often used when the emphasis is on the results a person would see in the first few screens of an interactive system. Precision at rank n is defined as

(4) P(n) = [R.sub.r]/n

where [R.sub.r] is the number of retrieved relevant documents in ranks 1 through n.

Precision in our experiments was measured at ranks 5, 10, 15, 20, and 30 documents, as is common in experiments with TREC data [Harman 1994; 1995]. These values indicate the accuracy that would be observed at various points on the first two or three screens of an interactive system.

6.4 Parameter Settings

All INQUERY system parameters were set to their default values for this experiment. The only choices made for these experiments were decisions about how many databases to search, and how many documents to return from each database.

INQUERY searched the 10 databases ranked most highly for the query by its database selection algorithm. The number 10 was chosen because it has been used in other recent research on distributed search with the INQUERY system [Xu and Callan 1998; Xu and Croft 1999]. The database selection algorithm ranked databases using either the learned resource descriptions or the complete resource descriptions, as determined by the experimenter.

Each searched database returned its most highly ranked 30 documents. The number 30 was chosen because precision was measured up to, but not beyond, rank 30.

The returned documents (10 x 30) were merged, using INQUERY's default algorithm for merging "multidatabase" search results. The algorithm for merging results from multiple searches is based on estimating an idf-normalized score D' for a document with a score of D in a collection with a score of C as

(5) [D.sub.s] = (D - [D.sub.min])/([D.sub.max] - [D.sub.min])

(6) [C.sub.s] = (C - [C.sub.min])/([C.sub.max] - [C.sub.min])

(7) D' = ([D.sub.s] + 0.4 x [D.sub.s] x [C.sub.)/1.4

where [D.sub.max] and [D.sub.min] are the maximum and minimum possible scores any document in that database could obtain for the particular query, and [C.sub.max] and [C.sub.min] are the maximum and minimum scores any collection could obtain for the particular query. This scaling compensates for the fact that while a system like INQUERY can in theory produce document scores in the range [0, 1], in practice the tf.idf algorithm makes it mathematically impossible for a document to have a score outside a relatively narrow range. [D.sub.min] and [C.sub.min] are usually 0.4, and [D.sub.max] and [C.sub.max] are usually about 0.6. Their exact values are query-dependent, and are calculated by setting the tf component of the tf.idf formula to 0.0 and 1.0 for every query term [Callan 2000].

Although the theoretical justification for this heuristic normalization is weak, it has been effective in practice [Allan et al. 1996; 1999; Callan 2000; Larkey et al. 2000] and has been used in INQUERY since 1995.

6.5 Experimental Results

Databases were ranked with either an index of complete resource descriptions (baseline condition) or an index of learned resource descriptions (test condition). The top 10 databases were searched; each returned 30 documents. The result lists returned by each database were merged to produce a final result list of 30 documents. (The scores used to rank the databases determined the value of C in Eq. (6).) Precision was measured at ranks 5, 10, 15, 20, and 30 documents. The experimental results are summarized in Table VII.
Table VII. Precision of a Search System Using Complete and Learned
Resource Descriptions for Database Selection and Result Merging.
TREC volumes 1, 2, and 3, divided into 100 databases. Ten databases
were searched for each query.

                Topics 51-100                   Topics 101-150
              (query set INQ026)              (query set INQ001)

Docu-    Complete         Learned        Complete         Learned
ment      Resource        Resource        Resource        Resource
Rank    Descriptions    Descriptions    Descriptions    Descriptions

  5        0.5960      0.6080 (+2.0%)      0.5920      0.5560 (-6.1%)
 10        0.5720      0.5960 (+4.1%)      0.5640      0.5580 (-1.1%)
 15        0.5613      0.5893 (+5.0%)      0.5547      0.5360 (-3.3%)
 20        0.5480      0.5880 (+7.2%)      0.5450      0.5230 (-4.0%)
 30        0.5240      0.5533 (+5.6%)      0.5107      0.5040 (-1.3%)


The experimental results indicate that distributed, or "multidatabase," retrieval is as effective with learned resource descriptions as it is with complete resource descriptions. Precision with one query set (INQ026, topics 51-100) averaged 4.8% higher using learned descriptions, with a range of 2.0 to 7.2%. Precision with the other query set (INQ001, topics 101-150) averaged 3.2% lower using learned descriptions, with a range of -1.1% to -6.1%. Both the improvement and the loss were too small for a person to notice.

These experimental results extend the results of Section 5, which indicated that using learned resource descriptions to rank collections introduced only a small amount of error into the ranking process. One might argue that the amount of error was too small to cause a noticeable change in search results, but there was no evidence to support that argument. These results demonstrate that the small errors introduced by learned resource descriptions do not noticeably reduce the accuracy of the final search results.

The accuracy of the document ranking depends also on merging results from different collections accurately. The experimental results indicate that learned resource descriptions support this activity as well. This result is important because INQUERY's result merging algorithm estimates a normalized document score as a function of the collection's score and the document's score with respect to its collection. The results indicate that not only are collections ranked appropriately using learned descriptions, but that the scores used to rank them are highly correlated with the scores produced with complete resource descriptions. This is further evidence that query-based sampling produces very accurate resource descriptions.

7. A PEEK INSIDE: SUMMARIZING DATABASE CONTENTS

Our interest is primarily in an automatic method of learning resource descriptions that are sufficiently accurate and detailed for use by automatic database selection algorithms. However, a resource description can also be used to indicate to a person the general nature of a given text database.

The simplest method is to display the terms that occur frequently and are not stopwords. This method can be effective just because the database is, in some sense, guaranteed to be about the words that occur most often. For example, the list of the top 50 words found by sampling the 1988 Wall Street Journal (Table VIII) contains words such as "market", "interest", "trade", "million", "stock", and "exchange", which are indeed suggestive of the overall subject of the database.
Table VIII. A Comparison of the 50 Most Frequent terms, as Measured by
Document Frequency, in a Text Database and in a Learned Resource
Description Constructed for that Database. 1988 Wall Street Journal
database. Three hundred documents examined, four documents per query.

Rank   Actual      Learned

  1    million     company
  2    new         million
  3    company     new
  4    make        make
  5    corp        corp
  6    base        base
  7    business    business
  8    two         market
  9    trade       co
 10    co          report
 11    market      president
 12    close       two
 13    president   billion
 14    stock       say
 15    early       concern
 16    wsj         early
 17    month       share
 18    u.s.        unit
 19    staff       plan
 20    report      expect
 21    plan        three
 22    say         trade
 23    time        interest
 24    expect      product
 25    york        month
 26    group       york
 27    concern     operate
 28    exchange    stock
 29    high        hold
 30    sale        executive
 31    operate     close
 32    price       group
 33    unit        international
 34    increase    increase
 35    hold        general
 36    billion     time
 37    end         exchange
 38    yesterday   sale
 39    product     change
 40    interest    result
 41    offer       service
 42    recent      manage
 43    america     made
 44    manage      work
 45    current     america
 46    part        buy
 47    three       national
 48    bank        official
 49    executive   end
 50    call        director


Table VIII also compares the top 50 words in the learned resource description with the top 50 words in the database. It demonstrates, that after 300 documents, the learned resource description is reasonably representative of the vocabulary in the target text database, and it is representative of the relative importance (ranks) of the terms; in this example, there is 76% agreement on the top 50 terms after seeing just 300 documents.

Controlled experiments are essential to understanding the characteristics of a new technique, but less controlled, "real-world" experiments can also be revealing. A simple database sampling system was built to test the algorithm on databases found on the Web. The program was tested initially on the Microsoft Customer Support Database at a time when we understood less about the most effective parameter settings. Accurate resource descriptions were learned, but at the cost of examining many documents [Callan et al. 1999].

We chose for this paper to reproduce the earlier experiment on a more easily accessible Web database, using sampling parameters that were consistent with parameter settings described elsewhere in this paper. The Combined Health Information Database,(5) which is published by several health-related agencies of the U.S. Government (National Institutes of Health, Centers for Disease Control and Prevention, and Health Resources and Services Administration) was selected. The database contains health-related information on 18 topics, which are summarized in Table IX.
Table IX. The 18 Topics Covered by the Combined Health Information
Database

AIDS education                   Disease Prevention/Health Promotion
Alzheimer's Disease              Epilepsy Education and Prevention
Arthritis; Musculoskeletal and   Health Promotion and Education
  Skin Diseases
Cancer Patient Education         Kidney and Urologic Diseases
Cancer Prevention and Control    Maternal and Child Health
Complementary and Alternative    Medical Genetics and Rare Disorders
  Medicine
Deafness and Communication       Oral Health
  Disorders
Diabetes                         Prenatal Smoking Cessation
Digestive Diseases               Weight Control


The initial query term was chosen randomly from the TREC-123 database. Subsequent query terms were chosen randomly from the resource description that was being learned. Four documents were examined per query. The experiment was ended after 300 documents were examined. Terms in the resource description were sorted by collection term frequency (ctf), and the top 100 terms were displayed. The results are shown in Table X.
Table X. The Top 100 Words Found by Sampling the U.S. National
Institutes of Health (NIH) Combined Health Information Database. Terms
are ranked by collection term frequency (ctf) in the sampled documents.
Three hundred documents were examined, four documents per query.

Term               ctf    df

hiv                1931   254
aids               1561   291
health             1161   237
prevention          666   195
education           534   293
information         439   184
persons             393   174
number              384   296
author              370   294
material            361   293
document            356   296
human               355   212
source              346   296
report              328    89
accession           323   296
public              323   156
update              317   296
community           313   107
language            310   296
services            310   129
descriptors         308   296
format              308   296
major               305   296
national            304   132
transmission        304   114
published           303   296
audience            302   293
availability        302   293
abstract            299   296
date                299   296
chid                297   296
subfile             297   296
ab                  296   296
fm                  296   296
lg                  296   296
mj                  296   296
ve                  296   296
verification        296   296
yr                  296   296
code                295   292
english             294   280
ac                  292   292
physical            292   267
print               281   257
treatment           280   127
cn                  279   279
corporate           279   279
description         278   266
pd                  266   266
programs            264   112
organizations       261   126
positive            254   150
care                248    83
virus               246   192
disease             241   120
service             241   133
discusses           226   152
provides            226   154
professionals       217   167
medical             212   117
immunodeficiency    193   180
drug                190    74
risk                185    99
issues              182    96
brochure            180    54
immune              179   144
examines            173   132
women               171    61
control             168    86
department          166    90
notes               163   163
nt                  163   163
state               160    64
program             158    80
video               148    32
acquired            144   140
deficiency          139   137
research            138    74
syndrome            138   138
factors             137    95
drugs               132    68
united              132    80
centers             131    67
world               131    55
box                 130   121
cdc                 128    75
children            122    45
patient             119    42
center              118    67
people              117    68
agencies            112    65
government          112    63
nations             112    41
describes           110    87
organization        109    51
sex                 108    60
std                 107    50
counseling          106    50
refs                103   103
surveillance        103    35


One can see easily that the database contains documents about health-related topics. Terms such as "hiv", "aids", "health", "prevention", "risk", "cdc", "transmission", "medical", "disease", "virus", "drug", and "immunodeficiency" show up high in the list.

Several of the most frequent words appear to indicate little about the database contents, such as "update", "published", "format", and "abstract". These terms could have been removed by using a larger stopword list. However, in general it is unclear which words in a multidatabase environment should be considered stopwords, since words that are unimportant in one database may be content words for others.

This particular resource description was based on a very simple approach to tokenizing, case conversion, and stopword removal. For example, all terms were converted to lower case; hence it does not distinguish among terms that differ only in case, such as "aids" and "AIDS". This distinction is important in this particular database, and illustrates some of the issues that a "real-world" system must address. Appropriate lexical processing is not necessarily a major barrier, but accuracy in "real-world" settings probably requires that it be addressed.

The Wall Street Journal and Combined Health Information databases are homogeneous to varying degrees, which may make it easier to summarize their contents with brief lists of frequent terms. This summarization technique may be less effective with larger, heterogeneous databases such as TREC-123. The top 50 words in the TREC-123 database (Table XI) provide some evidence that the database contains documents about U.S. national and business news, but it would be difficult to draw firm conclusions about the database contents from this list of words alone.
Table XI. The Top 50 Words Found by Sampling TREC-123 Terms Are
Ranked by Document Frequency (df) in the Sampled Documents. Five
hundred documents were examined, four documents per query.

Term          ctf    df

two            460   159
new            553   158
time           437   135
three          269   128
system        1609   122
base           421   115
high           585   115
make           254   115
state          446   114
report         336   104
product        549   103
part           371   101
group          513   101
work           256    98
relate         269    96
operate        396    95
follow         262    94
say            228    94
made           246    94
result         249    93
information    706    93
develop        525    91
accord         322    91
service        468    90
general        479    87
call           432    86
number         292    86
company        304    85
show           223    83
president      339    82
require        432    80
people         181    79
support        283    79
data           608    79
plan           163    79
million        199    79
end            556    78
allow          190    78
month          222    78
set            278    77
manage         302    77
national       209    77
change         311    76
long           153    76
problem        170    75
line           271    75
close          207    75
increase       173    75
second         882    75
order          236    74


Although simple word lists are effective for summarizing database contents in some situations, they are not necessarily the most effective techniques. Frequent phrases and common relationships can be better.

Indeed, one consequence of the sampling approach to creating learned resource descriptions is that it makes more powerful summarizations possible. The sampling process is not restricted just to word lists and frequency tables, nor is it restricted to just the information the database chooses to provide. Instead, it has a set of several hundred documents from which to mine frequent phrases, names, dates, relationships, and other interesting information. This information is likely to enable construction of more powerful and more informative summaries than is possible with the simple resource descriptions used by cooperative methods.

8. OTHER USES

The set of documents sampled from a single database reflects the contents of that database. One use of these documents is to build a resource description for a single database, as described above. However, other uses are possible.

One potential use is in a query expansion database. Recent research showed that query expansion significantly improves the accuracy of database selection [Xu and Callan 1998]. The state-of-the-art in query expansion is based upon analyzing the searched corpus for cooccurrence patterns, but what database(s) should be used when the task is database selection? This question has been unanswered.

If the documents sampled from each database were combined into a query expansion corpus, the result would be a set of documents that reflects the contents and word cooccurrence patterns across all of the available databases. It would require little additional effort for a database selection service to create a query expansion database in this manner.

Cooccurrence-based query expansion can be viewed as a form of data mining. Other forms of data mining could also be applied to the set of documents sampled from all databases. For example, frequent concepts, names, or relationships might be extracted and used in a visualization interface.

The ability to construct a single database that acts as a surrogate for a set of databases is significant, because it could be a way of rapidly porting many familiar Information Retrieval tools to environments containing many databases. Although there are many unanswered questions, this appears to be a promising direction for future research.

9. CONCLUSIONS

Our hypothesis was that an accurate description of a text database can be constructed from documents obtained by running queries on the database. Preliminary experiments [Callan et al. 1999] supported the hypothesis, but were not conclusive. The experiments presented in this paper test the hypothesis extensively, from multiple perspectives, and confirm the hypothesis. The resource descriptions created by query-based sampling are sufficiently similar to resource descriptions created from complete information that it makes little difference which is used for database selection.

Query-based sampling avoids many of the limitations of cooperative protocols such as STARTS. Query-based sampling can be applied to older ("legacy") databases and to databases that have no incentive to cooperate. It is not as easily defeated by intentional misrepresentation. It also avoids the problem of needing to reconcile the differing tokenizing, stopword lists, word stemming, case conversion, name recognition, and other representational choices made in each database. These representation problems are perhaps the most serious weakness of cooperative protocols, because they exist even when all parties intend to cooperate.

The experimental results also demonstrate that the cost of query-based sampling, as measured by the number of queries and documents required, is reasonably low, and that query-based sampling is robust with respect to variations in parameter settings.

Finally, and perhaps most importantly, the experiments described in this paper demonstrate that a fairly small partial description of a resource can be as effective for distributed search as a complete description of that resource. This result suggests that much of the information exchanged by cooperative protocols is unnecessary, and that communications costs could be reduced significantly without affecting results.

The demonstrated effectiveness of partial resource descriptions also raises questions about which terms are necessary for describing text collections. Query-based sampling identifies terms across a wide frequency range, but it necessarily favors the frequent, nonstopword terms in a database. Luhn suggested that terms in the middle of the frequency range would be best for describing documents [Luhn 1958]. It is an open question whether terms in the middle of the frequency range would be best for describing collections, too.

Several other open questions remain, among them whether the number of documents in a database can be estimated with query-based sampling. We have shown that this information may not be required for database selection, but it is nonetheless desirable information. It is also an open question how many documents must be sampled from a resource to obtain a description of a desired accuracy, although 300-500 documents appears to be very effective across a range of database sizes.

The work reported here can be extended in several directions, to provide a more complete environment for searching and browsing among many databases. For example, the documents obtained by query-based sampling could be used to provide query expansion for database selection, or to drive a summarization or visualization interface showing the range of information available in a multidatabase environment. More generally, the ability to construct a single database that acts as a surrogate for a large set of databases offers many possibilities for interesting research.

ACKNOWLEDGMENTS

We thank Aiqun Du for her work in the early stages of the research reported here. We also thank the reviewers for their many helpful suggestions, and a reviewer for the SIGIR conference for suggesting the experiments in Section 4.5.4.

(1) The 1988 Wall Street Journal data (WSJ88) is included on TREC CD 1. WSJ88 is about 10% of the text on TREC CD 1.

(2) Recall that stopwords were excluded from the comparison. If stopwords were included in the comparison, the rate of convergence would be considerably faster.

(3) This exception may be caused by the fact that about 10% of the TREC-123 database consists of Wall Street Journal data.

(4) The metric called R was called R in Lu et al. [1006]. We use the more recent and more widely known name, R, in this paper.

(5) National Institutes of Health. Combined Health Information Database. http://chid.nih.gov/. National Institutes of Health, Washington, D.C., 1999.

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Received: September 1999; revised: April 2001; accepted: April 2001

Callan's work was done in part while at the University of Massachusetts.

This material is based on work supported in part by the Library of Congress and Department of Commerce under cooperative agreement number EEC-9209623, and in part by NSF grants IIS-9873009, EIA-9983253, and EIA-9983215. Any opinions, findings, conclusions, or recommendations expressed in this material are the authors', and do not necessarily reflect those of the sponsors.

Authors' addresses: J. Callan, Language Technologies Institute, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, 4502 Newell Simon Hall, Pittsburgh, PA 15213-3890; email: callan@cs.cmu.edu; M. Connell, Center for Intelligent Information Retrieval, Computer Science Department, University of Massachusetts, Amherst, MA 01003-4610; email: connell@cs.umass.edu.
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