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.
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Notes
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We abbreviate URI namespaces with common prefixes, see http://prefix.cc for details.
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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.
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Implemented as in [15] (only removing the stopwords).
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If not explicitly stated all the difference in performance are statistically significant at \(p{\text {-}}value < 0.05\) using Fisher’s Z-value transformation.
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The sets of starting entities are obtained by using NER APIs.
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With td-idf as weighting function.
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Reuters collection is available at http://kdd.ics.uci.edu/databases/reuters21578/reuters21578.
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We executed this experiment in a Ubuntu machine with 16 cores (Intel Xeon E312xx) and 98 Gb of RAM.
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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
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