Flying Into Intelligent Search.
Then the pilot glimpses a person working in a tall building and, through a small break in the fog, holds up a sign reading "Where am I?" The person responds with a sign reading "You are in an airplane." This immediately tells the pilot the precise coordinates so that navigation to SeaTac is easily and swiftly accomplished.
This feat of intelligence, the punchline of the story, is that the answer given by the person in the building was entirely correct but completely useless, proving that the building was Microsoft's and the person worked in customer support. Knowing that the aircraft was at the Microsoft building, the pilot had the intelligence needed to land safely at SeaTac.
That's one form of intelligence, although the story always makes me wonder why any pilot who wasn't instrument rated would be attempting a flight in such bad weather. Doing so just isn't smart. Is being smart the same as being intelligent? Practical knowledge can power street smarts that are more effective than implied intelligence. Another story comes to mind. The driver of a car listens to the voice of the GPS that says "Go straight ahead for 300 meters, then turn right." The driver, however, knows that you can take a shortcut through a store parking lot and get to the destination faster and without stopping at the light on the corner. Some of the newer systems incorporate this travel intelligence, probably infuriating the store owner through whose parking lot vehicles are transiting.
The trick to intelligent search is to blend the various forms of knowledge to achieve the desired result in the most efficient and effective manner. Most organizations have information stored in multiple locations and in multiple formats. To create an intelligent search, these diverse pieces of information need to be connected. At Sinequa, explains Scott Parker, Senior Product Marketing Manager, the focus is on helping companies become information-driven. Ubiquitous connectivity is a big part of this. One suggestion he has is to connect information along topical lines, which both brings out collective expertise and makes it transparent. This is particularly beneficial in geographically distributed organizations, allowing employees to tap into the expert knowledge available to them so they can learn new skills with internal expert guidance.
Language can be tricky, and this is another area where Sinequa can help. Determining the language being used, analyzing the lexical construction of words, automatically extracting entity types, and text mining are key to determining meaning. Just think about how Polish people polish resumes. Knowing the difference between Polish and polish is obvious to humans but might trip up a computer.
Entity extraction for concepts and names of people, places, and companies keeps apple the fruit from being confused with Apple the company. Text mining, when integrated with the indexing engine, normalizes words, terms, and phrases to see patterns.
Machine learning contributes to the information-driven process by analyzing and structuring content, modifying search results, and recommending additional content. It's self-learning on a massive scale. Most importantly, when looking at any search and analytics platform, to be successful, it must align with end user goals. It must present a user experience that is aesthetically pleasing and understandable by employees.
Evaluating Search Tools
As you're flying toward intelligent search, evaluating the myriad of search tools available is important, just as the pilot's ability to find SeaTac from the illusory Microsoft office was. Verint Global Consulting Services' John Chmaj, Sr. Practice Director, Knowledge Management, has four tips for evaluating search tools.
He starts with understanding, which has to do with relevancy. Behind the scenes processing that involves word stemming, synonym association, language patterns, metadata, and contextual analysis operate to respond to a user query with actionable information. Looking for the latest on jets? The search tool should know whether you mean jet airplanes, the New York Jets, or Jet's Pizza.
Reasoning is Chmaj's second evaluation point. This is related to understanding, in that search algorithms should have the ability to collate, assess, and prioritize the best responses to the query a user enters into a search box. If it's jets in the aircraft sense, does the query concern engines, aircraft, regulations, accidents, manufacturing, scheduling, or something else? The evolution of reasoning mechanisms also needs to be considered.
Third is learning. How does the search tool optimize over time based on its analysis of searches performed and results preferred? Machine learning can be applied differently. The depth of leveraging it can vary, and knowing the extent to which it is being applied and how much control and visibility is available are evaluative aspects to pursue.
Interactivity rounds out the four evaluation tips. As user interfaces move from the keyboard to voice and mobile apps, search is now conversational. Tools that interact with users in clear and flexible ways are the new face of intelligent search.
Search technologies are evolving rapidly. Following best practices and evaluation tips will keep you from flying blind. Implementing intelligent search leads to actionable information and insights derived from intelligent search results. I
By Marydee Ojala, Conference Program Director, Information Today, Inc.
Marydee Ojala is conference program director for Information Today, Inc. She works on conferences such as Enterprise Search & Discovery, which is co-located with KMWorld, and WebSearch University, among others. She is a frequent speaker at U.S. and international information professional events. In addition, she moderates the popular KMWorld webinar series.
Ojala is based in Indianapolis, Indiana and can be reached at firstname.lastname@example.org.