TRank: Ranking entity types using the web of data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8218 LNCS, Issue PART 1, 2013, Pages 640-656

  Tonon, Alberto ^a   Catasta, Michele ^b   Demartini, Gianluca ^a   Cudré Mauroux, Philippe ^a   Aberer, Karl ^b  

^a UNIVERSITY OF FRIBOURG   (Switzerland)

^b EPFL   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

BACK-GROUND KNOWLEDGE; DOCUMENT COLLECTION; EXPERIMENTAL EVALUATION; FACTUAL INFORMATION; LINKED OPEN DATUM; RELATED ENTITIES; SEARCH ENGINE RESULTS; TYPE HIERARCHIES;

SEARCH ENGINES; SEMANTIC WEB;

WORLD WIDE WEB;

EID: 84891928059     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-41335-3_40     Document Type: Conference Paper

References (27)

1. Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: DBpedia - A crystallization point for the Web of Data. Journal of Web Semantics, 154-165 (2009)
2. Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: SIGMOD, pp. 1247-1250 (2008)
3. Campinas, S., Ceccarelli, D., Perry, T.E., Delbru, R., Balog, K., Tummarello, G.: The Sindice-2011 dataset for entity-oriented search in the web of data. In: 1st International Workshop on Entity-Oriented Search (EOS), pp. 26-32 (2011)
4. Ciaramita, M., Altun, Y.: Broad-coverage sense disambiguation and information extraction with a supersense sequence tagger. In: EMNLP, pp. 594-602 (2006)
5. Cunningham, H., Humphreys, K., Gaizauskas, R., Wilks, Y.: GATE: a general architecture for text engineering. In: ANLC, pp. 29-30 (1997)
6. Demartini, G., Difallah, D.E., Cudré-Mauroux, P.: ZenCrowd: leveraging probabilistic reasoning and crowdsourcing techniques for large-scale entity linking. In: WWW, pp. 469-478 (2012)
7. Fang, Y., Si, L., Yu, Z., et al.: Purdue at TREC 2010 Entity Track: A Probabilistic Framework for Matching Types Between Candidate and Target Entities. In: TREC (2010)
8. Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by gibbs sampling. In: ACL, pp. 363-370 (2005)
9. Finkel, J.R., Manning, C.D.: Joint parsing and named entity recognition. In: NAACL, pp. 326-334 (2009)
10. Gangemi, A., Nuzzolese, A.G., Presutti, V., Draicchio, F., Musetti, A., Ciancarini, P.: Automatic typing of dbpedia entities. In: Cudré-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J., Hauswirth, M., Parreira, J.X., Hendler, J., Schreiber, G., Bernstein, A., Blomqvist, E. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 65-81. Springer, Heidelberg (2012)
11. Holmes, G., Hall, M., Frank, E.: Generating rule sets from model trees. In: Foo, N.Y. (ed.) AI 1999. LNCS, vol. 1747, pp. 1-12. Springer, Heidelberg (1999)
12. Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. In: TOIS, pp. 422-446 (2002)
13. Kalyanpur, A., Murdock, J.W., Fan, J., Welty, C.: Leveraging community-built knowledge for type coercion in question answering. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part II. LNCS, vol. 7032, pp. 144-156. Springer, Heidelberg (2011)
14. Kumar, R., Tomkins, A.: A characterization of online search behavior. IEEE Data Eng. Bull. (2009)
15. Liu, T.-Y.: Learning to rank for information retrieval. In: FTIR, pp. 225-331 (2009)
16. Matuszek, C., Cabral, J., Witbrock, M., DeOliveira, J.: An introduction to the syntax and content of cyc. In: AAAI Spring Symposium (2006)
17. Mühleisen, H., Bizer, C.: Web data commons - extracting structured data from two large web corpora. In: LDOW (2012)
18. Nadeau, D.: Semi-supervised named entity recognition: learning to recognize 100 entity types with little supervision. PhD thesis (2007)
19. Nadeau, D., Turney, P.D., Matwin, S.: Unsupervised named-entity recognition: generating gazetteers and resolving ambiguity. In: Lamontagne, L., Marchand, M. (eds.) Canadian AI 2006. LNCS (LNAI), vol. 4013, pp. 266-277. Springer, Heidelberg (2006)
20. Pound, J., Mika, P., Zaragoza, H.: Ad-hoc object retrieval in the web of data. In: WWW, pp. 771-780 (2010)
21. Quinlan, J.R.: Learning with continuous classes. In: AI, pp. 343-348 (1992)
22. Suchanek, F.M., Abiteboul, S., Senellart, P.: Paris: Probabilistic alignment of relations, instances, and schema. In: PVLDB, pp. 157-168 (2011)
23. Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: WWW, pp. 697-706 (2007)
24. Tonon, A., Demartini, G., Cudré-Mauroux, P.: Combining inverted indices and structured search for ad-hoc object retrieval. In: SIGIR, pp. 125-134 (2012)
25. Tylenda, T., Sozio, M., Weikum, G.: Einstein: physicist or vegetarian? summarizing semantic type graphs for knowledge discovery. In: WWW, pp. 273-276 (2011)
26. Welty, C., Murdock, J.W., Kalyanpur, A., Fan, J.: A comparison of hard filters and soft evidence for answer typing in watson. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012, Part II. LNCS, vol. 7650, pp. 243-256. Springer, Heidelberg (2012)
27. Whang, S.E., Garcia-Molina, H.: Joint Entity Resolution on Multiple Datasets. The VLDB Journal (2013)