Canonicalization of Open Knowledge Bases with Side Information from the Source Text
Xueling Lin, Lei Chen · 2019
Nowadays Open Information Extraction (Open IE) approaches, which extracttriples from unstructured text, contribute to the construction of large Open Knowledge Bases (Open KBs). However, one crucial problem is that the noun phrases and relation phrases in the extracted triples are not well canonicalized, which leads to a large number of redundant and ambiguous facts. For example, bothandmay be extracted and stored in Open KBs. Recent research proposes to solve this problem by clustering over manually-defined feature spaces based on the similarity of the noun phrases and relation phrases. However, the performance of such techniques is limited, since only the information contained in the triples is utilized to measure their similarity. In this paper, we propose to perform canonicalization over Open IE triples by incorporating the side information from the original data sources, including the candidate entities of the noun phrases detected in the source text, the types of the candidate entities and the domain knowledge of the source text. We model the canonicalization problem of noun phrases and relation phrases jointly based on such side information, and demonstrate the effectiveness of our approach through extensive experiments on two real-world datasets.