Abstract
We propose a novel knowledge-based technique for inter-document similarity, called Context Semantic Analysis (CSA). Several specialized approaches built on top of specific knowledge base (e.g. Wikipedia) exist in literature but CSA differs from them because it is designed to be portable to any RDF knowledge base. Our technique relies on a generic RDF knowledge base (e.g. DBpedia and Wikidata) to extract from it a vector able to represent the context of a document. We show how such a Semantic Context Vector can be effectively exploited to compute inter-document similarity. Experimental results show that our general technique outperforms baselines built on top of traditional methods, and achieves a performance similar to the ones of specialized methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
We abbreviate URI namespaces with common prefixes, see http://prefix.cc for details.
- 3.
- 4.
When an entity is an instance of more than one class we use the class with the minor number of instances because it better characterizes an entity; however if we filter the knowledge bases by excluding classes defined in external sources such as YAGO, GroNames, etc. only 6.4 % of entities in Dbpedia and 2.22 % in Wikidata are instances of more than one class.
- 5.
- 6.
Implemented as in [15] (only removing the stopwords).
- 7.
If not explicitly stated all the difference in performance are statistically significant at \(p{\text {-}}value < 0.05\) using Fisher’s Z-value transformation.
- 8.
The sets of starting entities are obtained by using NER APIs.
- 9.
With td-idf as weighting function.
- 10.
Reuters collection is available at http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.
- 11.
We executed this experiment in a Ubuntu machine with 16 cores (Intel Xeon E312xx) and 98 Gb of RAM.
- 12.
References
Anyanwu, K., Maduko, A., Sheth, A.: SemRank: ranking complex relationship search results on the semantic web. In Proceedings of the 14th International Conference on World Wide Web, pp. 117–127. ACM (2005)
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007). doi:10.1007/978-3-540-76298-0_52
Bär, D., Zesch, T., Gurevych, I.: A reflective view on text similarity. In: RANLP, pp. 515–520 (2011)
Beneventano, D., Bergamaschi, S., Sorrentino, S., Vincini, M., Benedetti, F.: Semantic annotation of the cerealab database by the agrovoc linked dataset. Ecol. Inform. 26, 119–126 (2015)
Bizer, C., Heath, T., Berners-Lee, T.: Linked data-the story so far. In: Sheth, A.P. (ed.) Semantic Services, Interoperability, Web Applications: Emerging Concepts, pp. 205–227. IGI Global, Hershey (2009)
Bos, L., Donnelly, K.: SNOMED-CT: the advanced terminology and coding system for eHealth. Stud. Health Technol. Inform. 121, 279–290 (2006)
Caracciolo, C., Stellato, A., Morshed, A., Johannsen, G., Rajbhandari, S., Jaques, Y., Keizer, J.: The AGROVOC linked dataset. Semant. Web 4(3), 341–348 (2013)
Cyganiak, R., Wood, D., Lanthaler, M.: RDF 1.1 concepts, abstract syntax. W3C Recomm. 25, 1–8 (2014)
Dumais, S.T.: Latent semantic analysis. Annu. Rev. Inf. Sci. Technol. 38(1), 188–230 (2004)
Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. IJCAI 7, 1606–1611 (2007)
Gomaa, W.H., Fahmy, A.A.: A survey of text similarity approaches. Int. J. Comput. Appl. 68(13), 13–18 (2013)
Hassan, S., Mihalcea, R.: Semantic relatedness using salient semantic analysis. In: AAAI (2011)
Haveliwala, T.H.: Topic-sensitive pagerank. In: Proceedings of the 11th International Conference on World Wide Web, pp. 517–526. ACM (2002)
Lawrence, I., Lin, K.: A concordance correlation coefficient to evaluate reproducibility. Biometrics 45, 255–268 (1989)
Lee, M., Pincombe, B., Welsh, M.: An empirical evaluation of models of text document similarity. In: Cognitive Science (2005)
Manning, C.D., Raghavan, P., Schütze, H., et al.: Introduction to Information Retrieval, vol. 1. Cambridge University Press, Cambridge (2008)
Mendes, P., Jakob, M., García-Silva, A., Bizer, C.: DBpedia spotlight shedding light on the web of documents. In: I-Semantics (2011)
Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticae Investigationes 30(1), 3–26 (2007)
Nakov, P., Popova, A., Mateev, P.: Weight functions impact on LSA performance. In: EuroConference RANLP, pp. 187–193 (2001)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web (1999)
Schuhmacher, M., Ponzetto, S.P.: Knowledge-based graph document modeling. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pp. 543–552. ACM (2014)
Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697–706. ACM (2007)
Turney, P.D., Pantel, P., et al.: From frequency to meaning: vector space models of semantics. J. Artif. Intell. Res. 37(1), 141–188 (2010)
Van de Cruys, T.: Two multivariate generalizations of pointwise mutual information. In Proceedings of the Workshop on Distributional Semantics and Compositionality, pp. 16–20. Association for Computational Linguistics (2011)
Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)
Xing, W., Ghorbani, A.: Weighted pagerank algorithm. In: Second Annual Conference on Communication Networks and Services Research, 2004. Proceedings, pp. 305–314. IEEE (2004)
Yeh, E., Ramage, D., Manning, C.D., Agirre, E., Soroa, A.: WikiWalk: random walks on wikipedia for semantic relatedness. In Proceedings of the 2009 Workshop on Graph-Based Methods for Natural Language Processing, pp. 41–49. Association for Computational Linguistics (2009)
Zhao, Y., Karypis, G.: Evaluation of hierarchical clustering algorithms for document datasets. In Proceedings of the Eleventh International Conference on Information and Knowledge Management, pp. 515–524. ACM (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Benedetti, F., Beneventano, D., Bergamaschi, S. (2016). Context Semantic Analysis: A Knowledge-Based Technique for Computing Inter-document Similarity. In: Amsaleg, L., Houle, M., Schubert, E. (eds) Similarity Search and Applications. SISAP 2016. Lecture Notes in Computer Science(), vol 9939. Springer, Cham. https://doi.org/10.1007/978-3-319-46759-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-46759-7_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46758-0
Online ISBN: 978-3-319-46759-7
eBook Packages: Computer ScienceComputer Science (R0)