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SourceRank : Relevance and Trust Assessment for Deep Web Sources Based on Inter-Source Agreement

SourceRank : Relevance and Trust Assessment for Deep Web Sources Based on Inter-Source Agreement. Raju Balakrishnan , Subbarao Kambhampati Arizona State University. Funding from. Deep Web Integration Scenario.

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SourceRank : Relevance and Trust Assessment for Deep Web Sources Based on Inter-Source Agreement

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  1. SourceRank: Relevance and Trust Assessment for Deep Web Sources Based on Inter-Source Agreement RajuBalakrishnan, SubbaraoKambhampati Arizona State University Funding from

  2. Deep Web Integration Scenario Search engines have nominal access. We don’t Google for a “Honda Civic 2008 Tampa” Millions of sources containing structured tuples Uncontrolled collection of redundant information Mediator ←answer tuples ←query query→ answertuples→ ←answer tuples answer tuples→ answer tuples→ query→ ←query Web DB ←query Web DB Web DB Web DB Web DB Deep Web

  3. Why Another Ranking? Rankings are oblivious to result Importance & Trustworthiness Example Query: “Godfather Trilogy” on Google Base • Trustworthiness (bait and switch) • The titles and cover image match exactly. • Prices are low. Amazing deal! • But when you proceed towards check out you realize that the product is a different one! (or when you open the mail package, if you are really unlucky) Importance: Searching for titles matching with the query. None of the results are the classic Godfather

  4. Agenda • Problem Definition • SourceRank: Ranking based on Agreement • Computing Agreement • Computing Source Collusion • System implementation and Results

  5. Source Selection in the Deep Web Problem: Given a user query, select a subset of sources to provide important and trustworthy answers. Surface web search combines link analysis with Query-Relevance to consider trustworthiness and relevance of the results. Unfortunately, deep web records do not have hyper-links.

  6. Source Agreement Observations • Many sources return answers to the same query. • Comparison of semantics of the answers is facilitated by structure of the tuples. Idea: Compute importance and trustworthiness of sources based on the agreement of answers returned by different sources.

  7. Agreement Implies Trust & Importance. • Important results are likely to be returned by a large number of sources. • e.g. For the query “Godfather” hundreds of sources return the classic “TheGodfather” while a few sources return the little known movie “Little Godfather”. • Two independent sources are not likely to agree upon corrupt/untrustworthy answers. • e.g. The wrong author of the book (e.g. Godfather author as “Nino Rota”) would not be agreed by other sources. As we know, truth is one (or a few), but lies are many.

  8. Agreement is not just for the search Which tire?

  9. Agreement Implies Trust & Relevance Probability of agreement of two independently selected irrelevant/false tuples is Probability of agreement or two independently picked relevant and true tuples is

  10. Method: Sampling based Agreement where induces the smoothing links to account for the unseen samples. R1, R2 are the result sets of S1, S2. • Agreement is computed using key word queries. • Partial titles of movies/books are used as queries. • Mean agreement over all the queries are used as the final agreement. Link semantics from Si to Sj with weight w: Si acknowledges w fraction of tuples in Sj. Since weight is the fraction, links are unsymmetrical.

  11. Method: Calculating SourceRank How can I use the agreement graph for improved search? • Source graph is viewed as a markov chain, with edges as the transition probabilities between the sources. • The prestige of sources considering transitive nature of the agreement may be computed based on a markov random walk. SourceRank is equal to this stationary visit probability of the random walk on the database vertex. This static SourceRank may be combined with a query-specific source-relevance measure for the final ranking.

  12. Computing Agreement is Hard Computing semantic agreement between two records is the record linkage problem, and is known to be hard. • Example “Godfather” tuples from two web sources. Note that titles and castings are denoted differently. Semantically same entities may be represented syntactically differently by two databases (non-common domains).

  13. Method: Computing Agreement • Agreement Computation has Three levels. • Comparing Attribute-Value • Soft-TFIDF with Jaro-Winkler as the similarity measure is used. • Comparing Records. • We do not assume predefined schema matching. • Instance of a bipartite • matching problem. • Optimal matching is . • Greedy matching is used. Values are greedily matched • against most similar value in the other record. • The attribute importance are weighted by IDF. (e.g. same titles (Godfather) is more important than same format (paperback)) • Comparing result sets. • Using the record similarity computed above, result set similarities are computed using the same greedy approach.

  14. Detecting Source Collusion The sources may copy data from each other, or make mirrors, boosting SourceRank of the group. Observation 1: Even non-colluding sources in the same domain may contain same data. e.g. Movie databases may contain all Hollywood movies. Observation 2: Top-k answers of even non-colluding sources may be similar. e.g. Answers to query “Godfather” may contain all the three movies in the Godfather trilogy.

  15. Source Collusion--Continued • Basic Method: If two sources return same top-k answers to the queries with large number of answers (e.g. queries like “the” or “DVD”) they are likely to be colluding. • We compute the degree of collusion of sources as the agreement on large answer queries. • Words with highest DF in the crawl is used as the queries. • The agreement between two databases are adjusted for collusion by multiplying by • (1-collusion).

  16. Factal: Search based on SourceRank ”I personally ran a handful of test queries this way and gotmuch better results [than Google Products] results using Factal” --- Anonymous WWW’11 Reviewer. http://factal.eas.asu.edu

  17. Evaluation All experiments distinguish the SourceRank from baseline methods with 0.95 confidence levels. Precision and DCG are compared with the following baseline methods CORI: Adapted from text database selection. Union of sample documents from sources are indexed and sources with highest number term hits are selected [Callanet al. 1995]. Coverage: Adapted from relational databases. Mean relevance of the top-5 results to the sampling queries [Nieet al. 2004]. Google Products: Products Search that is used over Google Base

  18. Online Top-4 Sources-Movies Though combinations are not our competitors, note that they are not better: 1.SourceRank implicitly considers query relevance, as selected sources fetch answers by query similarity. Combining again with query similarity may be an “overweighting”. 2. Search is Vertical 29%

  19. Online Top-4 Sources-Books 48%

  20. Google Base Top-5 Precision-Books • 675 Google Base sources responding to a set of book queries are used as the book domain sources. • GBase-Domain is the Google Base searching only on these 675 domain sources. • Source Selection by SourceRank (coverage) followed by ranking by Google Base. 675 Sources 24%

  21. Google Base Top-5 Precision-Movies 25%

  22. Trustworthiness of Source Selection Corrupted the results in sample crawl by replacing attribute vales not specified in the queries with random strings (since partial titles are the queries, we corrupted attributes except titles). If the source selection is sensitive to corruption, the ranks should decrease with the corruption levels. Google Base Movies Every relevance measure based on query-similarity are oblivious to the corruption of attributes unspecified in queries.

  23. Trustworthiness- Google Base Books

  24. Collusion—Ablation Study • Two database with the same one million tuples from IMDB are created. • Correlation between the ranking functions reduced increasingly. Observations: At high correlation the adjusted agreement is very low. Adjusted agreement is almost the same as the pure agreement at low correlations. Natural agreement will be preserved while catching near-mirrors.

  25. Computation Time • Random walk is known to be feasible in large scale. • Time to compute the agreements is evaluated against number of sources. • Note that the computation is offline. • Easy to parallelize.

  26. Contributions The search using SourceRank is demonstrated on Friday: 10-15:30 • Agreement based trust assessment for the deep web • Agreement based relevance assessment for the deep web • Collusion detection between the web sources • Evaluations in Google Base sources and online web databases

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