Abstract
Knowledge graphs (KGs) play a crucial role in many modern applications. Many open information extraction approaches propose the extraction of triples from natural language text in order to populate knowledge. Nonetheless, most approaches do not consider forming links between the extracted triples and the KG triples, especially for predicates. Predicate linking is used to identify the predicate in a KG that exactly corresponds to an extracted predicate; this allows the avoidance of the heterogeneous problem. Resolving the heterogeneous problem can increase searchability over KGs. Although there have been a few studies that considered linking predicates, most of them have relied on statistical knowledge patterns, which are not able to generate all possible patterns. In this paper, we introduce distributed representations of elements in triples and leverage them for computing the similarity between predicates in order to find links that would not appear in statistical patterns. In the experiment, the results show that leveraging the distributed representations of triple elements can discover links between identical predicates, which cannot be achieved by the statistical pattern approach. As a result, our approach outperformed the traditional baseline for the predicate linking task.
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This work was partially supported by NEDO (New Energy and Industrial Technology Development Organization).
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Kertkeidkachorn, N., Ichise, R. (2017). Leveraging Distributed Representations of Elements in Triples for Predicate Linking. In: Martínez de Pisón, F., Urraca, R., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2017. Lecture Notes in Computer Science(), vol 10334. Springer, Cham. https://doi.org/10.1007/978-3-319-59650-1_7
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