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  1. Exploiting Wikipedia as External Knowledge for Document Clustering Xiaohua Hu1, Xiaodan Zhang1, Caimei Lu1, E.K. Park2, Xiaohua Zhou1 1College of Information Science and Technology, Drexel University, Philadelphia, PA 19104, USA 2School of Computing and Engineering, University of Missouri at Kansas City, Kansas City, MO 64110, USA

  2. Outline • Introduction • Framework • Concept Mapping: Exact and Relatedness Match • Category Mapping • Clustering • Experiments & Results • Conclusions & Future work

  3. Introduction • Problems of BOW-based clustering: • Ignores the relationships among words • If two documents use different collections of core words to represent the same topic, they would be assigned to different clusters. • Solution: enrich document representation with the background knowledge represented by an ontology.

  4. Introduction • Twoissues for enhancing text clustering by leveraging ontology semantics: • An ontology which can cover the topical domain of individual document collections as completely as possible. • A proper matching method which can enrich the document representation by fully leveraging ontology terms and relations without introducing more noise. • This paper aims to address both issues.

  5. Wikipedia as Ontology The free encyclopedia that anyone can edit • Wikipedia is a free, multilingual encyclopedia project supported by the non-profit Wikimedia Foundation. • Wikipedia's articles have been written collaboratively by volunteers around the world. • Almost all of its articles can be edited by anyone who can access the Wikipedia website. ----http://en.wikipedia.org/wiki/Wikipeida

  6. Wikipedia as Ontology • Unlike other standard ontologies, such as WordNet and Mesh, Wikipedia itself is not a structured thesaurus. • However, it is more… • Comprehensive: it contains 12 million articles (2.8 million in the English Wikipedia) • Accurate : A study by Giles (2005) found Wikipedia can compete with Encyclopædia Britannica in accuracy*. • Up to date: Current and emerging concepts are absorbed timely. * Giles, J. 2005. Internet encyclopaedias go head to head. Nature 438: 900–901.

  7. Wikipedia as Ontology • Moreover, Wikipedia has a well-formed structure • Each article only describes a single concept. • The title of the article is a short and well-formed phrase like a term in a traditional thesaurus.

  8. Wikipedia Article that describes the Concept Artificial intelligence

  9. Wikipedia as Ontology • Moreover, Wikipedia has a well-formed structure • Each article only describes a single concept • The title of the article is a short and well-formed phrase like a term in a traditional thesaurus. • Equivalent concepts are grouped together by redirected links.

  10. AI is redirected to its equivalent concept Artificial Intelligence

  11. Wikipedia as Ontology • Moreover, Wikipedia has a well-formed structure • Each article only describes a single concept • The title of the article is a short and well-formed phrase like a term in a traditional thesaurus. • Equivalent concepts are grouped together by redirected links. • It contains a hierarchical categorization system, in which each article belongs to at least one category.

  12. The concept Artificial Intelligence belongs to four categories: Artificial intelligence, Cybernetics, Formal sciences & Technology in society

  13. Wikipedia as Ontology • Moreover, Wikipedia has a well-formed structure • Each article only describes a single concept • The title of the article is a short and well-formed phrase like a term in a traditional thesaurus. • Equivalent concepts are grouped together by redirected links. • It contains a hierarchical categorization system, in which each article belongs to at least one category. • Polysemous concepts are disambiguated by Disambiguation Pages.

  14. The different meanings that Artificial intelligence may refer to are listed in its disambiguation page.

  15. The Framework of leveraging Wikipedia for document clustering • Similarity metric between two documents: • SIM(d1, d2)= Sim(W1, W2)+ α∙Sim(C1, C2) • + β∙Sim(Cat1, Cat2)

  16. Concept Mapping • Match documents to Wikipedia concepts. • Result: A document-concept Matrix • Matching Schemes: • Exact Match • Relatedness Match Wikipedia Concept C1 C2 w11 w12 D1 D2 Documents w21 w22

  17. 1. Concept Mapping ---Exact Match d1 Artificial Intelligence, or AI for short, is a combination of computer science, physiology, and philosophy. … document-concept Vector Exact match Wikipedia concept dictionary artificial Intelligence, AI … computer science … physiology … philosophy …

  18. Concept Mapping Schemes: Exact Match • Each document is scanned to find Wikipedia concepts (article titles). • The searched Wikipedia concepts are used to comprise the concept vector of the corresponding document. • Synonymous phrases to the same concept are grouped together through the redirect links in Wikipedia.

  19. Concept Mapping Schemes: Exact Match • A dictionary is constructed, with each entry corresponding to a topic covered by Wikipedia. • Each entry includes not only the preferred Wikipedia concept which is used as the title of the article, but also all the redirected concepts representing the same topic. • Based on the dictionary, both preferred concepts and redirected concepts are retrieved from documents. • Only preferred concepts are used to build the concept vector for each document. The weight of each preferred concept equals to the frequency of itself plus the frequencies of all the redirected concepts appearing in a document. • The document-concept TFIDF matrix is further calculated based on the document-concept frequency matrix.

  20. Concept Mapping Schemes: Exact Match • High efficiency • Low recall. Only the concepts which explicitly appear in a document are extracted and used to construct the concept vector of the document.

  21. 2. Concept Mapping –Relatedness Match d1 Wikipedia collection Artificial Intelligence, or AI for short, is a combination of computer science, physiology, and philosophy. … Wikipedia term concept matrix term vector of document d1 document-concept Vector

  22. Concept Mapping Schemes: Relatedness Match Step 1: A Wikipedia term-concept matrix is constructed from Wikipedia article collection. • The values in the matrix are TFIDF scores, which denote the relatedness of each term to each Wikipedia concept article. • For each word, only top k concepts with highest TFIDF scores are selected. (In this study, k = 5)

  23. Concept Mapping Schemes: Relatedness Match Step 2: The word-concept matrix is used as a bridge to build Document-Wikipedia concept Matrix. The relatedness of a Wikipedia concept to a given document is calculated as: : the TFIDF score of word in document ; representing the importance of to : the TFIDF score of word in concept ; representing the importance of to • For each document, only the top M concepts with highest relatedness score are selected. (In this experiment, M is set to 200)

  24. Concept Mapping Schemes: Relatedness Match • More time consuming • Helpful for identify relevant Wikipedia concepts which are not explicitly present in a document. • Especially useful when Wikipedia concepts have less coverage for a dataset.

  25. WikipediaCategory CAT1 CAT2 C1 C2 Wikipedia Concepts WikipediaCategory CAT1 CAT2 v11 w11 1 0 v12 w12 D1 D2 Documents w21 v21 1 1 w22 v22 C1 C2 D1 D2 Wikipedia Concept Documents Category Mapping

  26. 3. Category Mapping Concept Mapping (Exact Match or Relatedness Match) The hierarchical categorization system of Wikipedia Wiki Concept-Category Matrix document-concept Vector document-category Vector

  27. Category Mapping • Exact match: • A document-category frequency matrix is first derived from the document-concept frequency matrix by replacing each concept with its corresponding categories. • If a category is mapped to a document through more than one concept, the sum of the frequencies of these concepts is the category’s frequency. • Based on the generated document-category frequency matrix, we further derive the document-category TFIDF matrix

  28. Category Mapping • Relatedness Match • The document-category matrix is generated by replacing each concept in the document-concept matrix with its corresponding categories, which share the same normalized relatedness score as the concept. • If a category is mapped to a document through more than one concept, its relatedness score to the document is the sum of the scores of all these concepts.

  29. Document Clustering • Agglomerative Clustering • Partitional Clustering and respectively indicate the importance of concept vector and category vector in measuring the similarity between two documents.

  30. Experiments • Wikipedia data: 911,028 articles and about 29000 categories after pre-processing and filtering. • Clustering dataset: TDT2, LA Times (from TREC), and 20-newsgroups (20NG). • Evaluation Metrics: • Purity • F-score • NMI (normalized mutual information )

  31. Experiments • Clustering Schemes under Comparison

  32. Experiments • Parameter Setting (α and β that indicate the importance of concept vector and category vector in measuring the similarity between two documents.) • For Word_Conceptscheme, β is set to zero and α is set to 0.1, 0.2, ∙∙∙, 1.0 respectively. The average result of the ten runs is used as the final clustering results for Word_Concept scheme. • For Word_Categoryscheme, α is set to zero and β is set to 0.1, 0.2, ∙∙∙, 1.0 respectively. The average result of the ten runs is used as the final clustering results for Word_Category scheme. • For Word_Concept_Category scheme, α is set to the value which produces best results for Word_Concept based clustering, and β is set to the value that generates best results for Word_Category based clustering.

  33. Agglomerative clustering results * indicates the improvement is significant according to the paired-sample T-test at the level of p<0.05.

  34. Agglomerative clustering results * indicates the improvement is significant according to the paired-sample T-test at the level of p<0.05.

  35. Agglomerative clustering results * indicates the improvement is significant according to the paired-sample T-test at the level of p<0.05.

  36. Partitional Clustering Results * indicates the improvement is significant according to the paired-sample T-test at the level of p<0.05.

  37. Partitional Clustering Results * indicates the improvement is significant according to the paired-sample T-test at the level of p<0.05.

  38. Partitional Clustering Results * indicates the improvement is significant according to the paired-sample T-test at the level of p<0.05.

  39. Conclusions • Category information is most useful for improving clustering results. In both agglomerative clustering and partitional clustering, combining category information with document content information generates the best results in most cases. • Clustering based on all three document vectors (word vector, concept vector, category vector) also gets significantly better results than the baseline, but does not outperform clustering based only on word vector and category vector. • Concept information is not as useful as category information for improving clustering performance due to the noisy information it contains and sense ambiguity problem. • The effect of category and concept information on k-means clustering is not as significant as it on agglomerative clustering. But, in most cases, Word_Category based clustering still achieves best performance among all clustering schemes. • The effect of the two mapping schemes depends on the dataset, quality metric and clustering approach.

  40. Future Work • Introduce sense disambiguation functions into the concept mapping process by leveraging Wikipedia disambiguation pages. • Explore how to utilize the link structure among Wikipedia concepts for document clustering.

  41. Q & A