Enterprise Search Sourcebook 2008

different from your traditional web search: “It’s not full text searching, it’s the ability to search a database for a particular concept, say ‘semantic web,’ and not only get the relevant results that you might get from a service such as Google, but find people working on semantic web, presentations about semantic web, upcoming events related to it, books, concepts—all of the different kinds of things that allow us to narrow in on the kinds of search strategies we are interested in.” Miller points out that, if you search for “semantic web” with a traditional web search tool, you are going to get results based on popularity—which might not always get you the results you are looking for. Ontologies Define and Organize One of the key technologies underlying semantic search is the notion of ontologies, which provide a way to map a term to multiple meanings. For example, if a search includes the word “cell”—which could be related to a prison, a biological entity, a part of a spreadsheet, and so forth—a conventional search wouldn’t automatically sort out just which one meaning is relevant to your specific needs, and popularity ranking certainly wouldn’t be of much help. Moulton defines an ontology as “an assembly of concepts ontologies for specific vertical markets. He explains that the purpose of these ontologies is to represent the way things work in the real world. “In general, our approach to semantic search is to have that form or representation have an ontology representing the real world in a variety of different industries. For example, we have an ontology for telecommunications, one In this search, users can refine results by author, coverage area, and user rating to narrow results, or they can pivot the result set to other related views for new insight into the data. Moreover, the tool can distinguish the difference between “(Jack) London” the author; and “London (England)” the city. Semantic search engines help generate meaningful results regardless of how the user enters the query. in which all possible relationships that might exist among concepts are explicitly mapped.” She adds that the goal of semantic search engines is to be able to understand the meaning based on the context and the ontology. “When there are ontologies for semantic search engines to consult, it’s like looking things up in the dictionary. Here’s the word ‘cell’ and what are all the possible ways in which cell could be related to other words.” She says, that armed with this knowledge, a search engine can deliver more accurate results. One of the problems with this approach, however, is the difficulty in building ontologies. It can be a time-consuming and expensive exercise, but Edwin Cooper, founder and chief scientist of semantic search vendor InQuira, says his company has helped minimize the pain by generating generic 44 ENTERPRISE SEARCH SOURCEBOOK 2008 in the automotive industry, and one for financial services.” He adds that this approach can help nail down the exact document even when the query is not spot-on. “When we are dealing with the entire web—everything out there—there is a pretty good chance something matches your query exactly and you might not need to understand any of the synonyms or relate it to a specific piece of content. On the other hand, when you are in a particular industry vertical, there may very well be a piece of content that exactly suits what you are looking for, but it may not be an exact match to the question and you might have to do more analysis and get a deeper understanding both of the content and of the question to be able to link those two together, especially in the limited content set of these enterprise verticals,” Cooper says. Tim Shettler, VP of marketing at InQuira, cites Honda’s website as a good example of using synonyms in an ontology to link car colors to search queries. Most car companies use unusual names to represent car colors, ones that the visitor might not know, so if a user enters “gold” into the InQuira search engine, the results come back with sunset metallic, the closest color to gold, rather than telling the user no such car color exists. By building some intelligence into the search process, it produces meaningful results, making the site far more usable. Metadata and Entities Facilitate Machines’ Communication Another factor in empowering semantic search is metadata, information about the content and entities, which helps break down information into meaningful categories, such as name, product, industry, and so forth.

Table of Contents Feed for the Digital Edition of Enterprise Search Sourcebook 2008

Enterprise Search Sourcebook 2008 Contents Editor’s Note Publisher’s Note Findings and Figures Why Enterprise Search Will Never Be Google-y Searching for Search Usability Your Users Are Talking to You What’s Your Search Story? Search Is Dead—Now What? Delivering on the Promise of Enterprise Search Taming Multiple Search Engines in Your Organization Enterprise Search: Trends for 2008 Enterprise Search Seen From the Inside Open Source Search: Elixir or Poison? Avoiding the Big Mistakes in Search Semantic Search Takes Root in the Enterprise E-Discovery Essentials: The Rules You Need to Know SharePoint Search: An Enterprise Contender? Integrating Security Into Your Enterprise Search Infrastructure Engineering a Better Search Infrastructure Letting End Users Ask the Questions, Stat! The Power of Knowledge Legal Research Using Enterprise Search: A Developer’s View From Treading Water to Full Steam Ahead Pulling Out All the Stops With Midas A Natural Search Solution An Incremental Approach to Improving Enterprise Search The Enterprise Search Sourcebook Showcase Directory Index to Advertisers and Companies Mentioned

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