Abstract
The integration of Linked Open Data faces great challenges on the semantic level, despite unified data models. Inappropriate use of ontology concepts, namely predicates, impedes knowledge discovery. Although predicate unification is one of the most crucial steps when building structured knowledge base, little effort has been put forward. In this paper, we propose a supervised approach to detect synonymous predicates. Our detection focuses on feature selection and their effectiveness analysis. We not only leverage different resources such as Wikipedia, Freebase, but also use different word embeddings to represent predicates. The experimental results indicate that wikitext defined by Wikipedia and predicate surface form are most useful features.
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Based on Chinese Wikipedia web pages in August 2014.
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Available at https://dumps.wikimedia.org/zhwiki/.
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The linking property in Freebase rdf dump is Wikipedia.zh-cn_id while the Freebase category predicate is rdf:type.
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The version of Freebase used in the experiment is 2013-06-02 (1.37 billion triples). We collected categories of 337042 entities in Freebase.
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Acknowledgement
This work was supported by National High Technology R&D Program of China (Grant No. 2015AA015403, 2014AA015102), Natural Science Foundation of China (Grant No. 61202233, 61272344, 61370055) and the joint project with IBM Research. Any correspondence please refer to Yansong Feng.
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Han, Z., Feng, Y., Zhao, D. (2016). Detecting Synonymous Predicates from Online Encyclopedia with Rich Features. In: Ma, S., et al. Information Retrieval Technology. AIRS 2016. Lecture Notes in Computer Science(), vol 9994. Springer, Cham. https://doi.org/10.1007/978-3-319-48051-0_9
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DOI: https://doi.org/10.1007/978-3-319-48051-0_9
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