Entity Summarization Based on Entity Grouping in Multilingual Projected Entity Space

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Entity descriptions have been exponentially growing in community-generated knowledge databases, such as DBpedia. However, many of those descriptions are not useful for identifying the underlying characteristics of their corresponding entities because semantically redundant facts or triples are included in the descriptions that represent the connections between entities without any semantic properties. Entity summarization is applied to filter out such non-informative triples and meaning-redundant triples and rank the remaining informative facts within the size of the triples for summarization. This study proposes an entity summarization approach based on pre-grouping the entities that share a set of attributes that can be used to characterize the entities we want to summarize. Entities are first grouped according to projected multilingual categories that provide the multi-angled semantics of each entity into a single entity space. Key facts about the entity are then determined through in-group-based rankings. As a result, our proposed approach produced summary information of significantly better quality (p-value = 1.52 x 10(-3) and 2.01 x 10(-3) for the top-10 and -5 summaries, respectively) than the state-of-the-art method that requires additional external resources.
Publisher
IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
Issue Date
2017-09
Language
English
Article Type
Article
Citation

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, v.E100D, no.9, pp.2138 - 2146

ISSN
1745-1361
DOI
10.1587/transinf.2016EDP7235
URI
http://hdl.handle.net/10203/226401
Appears in Collection
CS-Journal Papers(저널논문)
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