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A Knowledge-Based Search Engine Powered by Wikipedia

A Knowledge-Based Search Engine Powered by Wikipedia. David Milne, Ian H. Witten, David M. Nichols (CIKM 2007). Introduction. Whenever we seek out new knowledge — how can one describe the unknown? What knowledge seekers need is a bridge between what they know and what they wish to know.

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A Knowledge-Based Search Engine Powered by Wikipedia

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  1. A Knowledge-Based Search EnginePowered by Wikipedia David Milne, Ian H. Witten, David M. Nichols (CIKM 2007)

  2. Introduction • Whenever we seek out new knowledge — how can one describe the unknown? • What knowledge seekers need is a bridge between what they know and what they wish to know. • Knowledge seekers can benefit from a thesaurus that covers the terminology of both documents and potential queries, and describes relations that bridge between them.

  3. KORU

  4. ，

  5. Creating A Relevant Knowledge Base • To work well, Koru relies on a large and comprehensive thesaurus. • Our own technique automatically extracts thesauri from a huge manually defined information structure. • From Wikipedia, we derive a thesaurus that is specific to each particular document collection.

  6. Creating A Relevant Knowledge Base • The basic idea is to use Wikipedia’s articles as building blocks for the thesaurus. • Each article describes a single concept. • Concepts are often referred to by multiple terms and Wikipedia handles these using “redirects”.

  7. Measuring semantic relatedness • Semantic relatedness concerns the strength of the relations between concepts. • The measure that we use quantifies the strength of the relation between two Wikipedia articles by weighting and comparing the links found within them.

  8. Measuring semantic relatedness • Links are weighted by their probability of occurrence. • Links are less significant for judging the similarity between articles if many other articles also link to the same target. • We simply sum the weights of the links that are common to both articles.

  9. Disambiguating unrestricted text • To identify the concepts relevant to a particular document collection, we work through each document in turn, identifying the significant terms and matching them to individual Wikipedia articles. • Disambiguate each term using the context surrounding it.

  10. Identifying relations between concepts • Wikipedia contains many more links than the redirects we use to identify synonymy. • We gather all relations from article and category links, but weight them so that only the strongest are emphasized.

  11. Weighing topics, occurrences and relations • Every occurrence of every topic is weighted within the thesaurus. • 1. tf-idf：A significant topic for a document should occur many times. • 2. average semantic relatedness measure：A significant topic should relate strongly to other topics in the document.

  12. KORU in action

  13. KORU in action

  14. KORU in action

  15. Evaluation • 2種版本：Topic browsing：有使用thesaurusKeyword Searching：沒有使用thesaurus • 12名參加者、10個task

  16. Evaluation

  17. Evaluation

  18. Evaluation

  19. Evaluation

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