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TOPIC CENTRIC QUERY ROUTING

TOPIC CENTRIC QUERY ROUTING. Research Methods (CS689) 11/21/00. By Anupam Khanal. Introduction: What is query routing? Searching online can be both rewarding and frustrating. General search engines such as Yahoo, Lycos return many irrelevant information to users query.

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TOPIC CENTRIC QUERY ROUTING

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  1. TOPIC CENTRIC QUERY ROUTING Research Methods (CS689) 11/21/00 By Anupam Khanal

  2. Introduction: • What is query routing? • Searching online can be both rewarding and frustrating. • General search engines such as Yahoo, Lycos return many irrelevant information to users query. • In such context, query routing attempts dynamically route each users’ query to the appropriate specialized search.

  3. Problem Description • There are many general search engines such as Yahoo, Lycos, Alta Vista etc. • There are also many topic specific search engines such as VaccationSpot.com, KidsHealth.com etc. • However, many casual users are not familiar with all these topic specific search engines. • In such context, topic centric query expansion is important.

  4. Why Topic Centric Query Routing: • It is of utmost importance to analyze other query routing systems as well before we discuss the importance of Topic Centric routing. • Manual Query Routing Services: - provide the categorized list of specialized search engine - users have to choose the search engines - although keyword search interface is provided the terms that can be accepted as the keywords are limited. • Query Routing based on Centroids: - consist centroids which are summaries of databases - these summaries consists a complete list of terms and frequencies of the databases.

  5. - search engine is located by dividing which databases are relevant to a user query by comparing the query with each centroid. • - this technique cannot be applied to most of the topic specific search engines provided on the Web because of the restricted access to their internal database. • Query Routing Without Centroids: • - Instead of centroids this systems generate a short text to explain the centroids of databases. • - if the search keywords are contained is such text then only the search engine will be located.

  6. Research Objective: • In such context, Topic Centric Query Routing is • appropriate as it uses the routing model to expand • the query. • The general framework of the query routing model is as • follows: • Getting relevant terms from the Web: • - routing model does not use any special dictionaries, but it uses the Web as the source of relevant terms. • - finds the Web documents relevant to the user query • dynamically by submitting that query to a general search engine. • - the relevant terms are extracted from those documents.

  7. Co-occurrence based evaluation of term relevance. • - the mutual relevance of terms is evaluated on the basis • of their co-occurrences in the documents. • - the co-occurrences of the search keywords are counted • in all the documents retrieved by the general search engine. • - the routing model list all the distinct terms contained in • all documents and counts for each term the number of • documents that contain both the search key word and that • term. • Using a pseudo-feedback technique • - it is difficult to determine the term relevance from only the • results of a single document search on the general search • engine.

  8. -even relevant terms often have few co-occurrences in • the selected documents of the first search. • in such context, query routing model re-evaluates • such low co-occurrences terms selecting terms to be • re-evaluated from the first search results, formulating • new queries by adding the selected terms to the original • query and performing the co-occurrence based evaluation • for each formulated query.

  9. Query expansion procedure • Get a document set D0 relevant to a user query Q0, where search keywords are w01, ..., w0n, by sending Q0 to a general search engine. • Count co-occurrences of search keywords and other terms in the document set D0. • Let WH0 and WL0 be a set of terms whose co-occurrences exceed a certain threshold and a set of the other terms, respectively. WH0 is considered relevant to the query Q0and will be a part of the query expansion result. • Pick up at most four topic terms wt1-wt4 from WL0. • Formulate four queries QT1-QT4 by combining wt1-wt4 with Q0 (for example, QT1="w01 ... w0n wt1"). Figure 4: Query expansion procedure.

  10. 6. Clustering all terms in D0 to at most three clusters: W1={w11, ..., w1m}, W2={w21, ..., w2k} and W3={w31, ..., w3j}. • 7. Formulate three queries Q1-Q3 by combining W1-W3 with Q0 (for example, Q1="w01 ... w0nw11 ... w1m"). • 8. Get document sets DT1-DT4 and D1-D3 by sending QT1-QT4 and Q1-Q3 independently to a general search engine. • 9. Count co-occurrences in DT1-DT4 and D1-D3. Sets of high co-occurrence terms WTH1-WTH4 and WH1-WH3, as well as WH0 in step 3, are query expansion results.

  11. Query Routing Result Query: “python” User query • If you are looking for information about… • movie-monty python Phrase to explain topic [1600] Search/Go toSearch the Internet Movie Database [1600] Search/Go to The Roger Ebert Movie Files [1600] Search/Go to Horror Search Recommended topic Search Engines

  12. Other Topics…. Object oriented programming in python [7500] Search/Go toIndex to Object Oriented Information Sources [3600] Search/Go toUnix Programming jpython- python in java [6300] Search/Go tojava.sun.com –The Source for Java™ Technology [5641] Search/Go toGamelan- The official Java Directory [4921] Search/Go toJCentral – Search the web for Java [4266] Search/Go toIndex to Object Oriented Information Sources

  13. Importance ofTopic Centric Query Routing • Query Routing Model is used. • Query Routing model doesn’t generate centroids. • IT consists an off line pre-processing component and online interface. • Offline Query Routing Model takes as input a set of search engines and creates for each engine, an approximate textual model of that engine’s content or scope.

  14. Online Query Routing Model takes a user query as input and applies a novel query expansion technique to the query; • Then it clusters the output of the query expansion to suggest multiple topics that user may be interested in. • Each topic is associated with a set of search engines, eg., “Python”

  15. Query Expansion model has the ability to automatically obtain terms relevant to a query from the web. • Using Query Expansion model, it is not necessary to maintain a massive dictionary of terms in a wide range of fields.

  16. Conclusion • Topic centric query routing uses a query expansion model. • Query expansion model obtains all the information necessary in query routing form the web. • Thus Query routing model is an intelligent agent that uses the web as its knowledge and identifies topics of given queries dynamically by query expansion.

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