Longtao Huang
ABSTRACT
An entity on the web can be referred by numerous morphs that are always ambiguous, implicit and informal, which makes it challenging to accurately identify all the morphs corresponding to a specific entity. In this paper, we introduce a novel method based on knowledge graph, which takes advantage of both knowledge reasoning and statistic learning. First, we present a model to build a knowledge graph for the given entity. The knowledge graph integrates the fragmented knowledge on how humans create morphs. Then, the candidate morphs are generated based on the rules summarized from the knowledge graph. At last, we use a classification method to filter the useless candidates and identify the target morphs. The experiments conducted on real world dataset demonstrate efficiency of our proposed method in terms of precision and recall.
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Index Terms
- KIEM: A Knowledge Graph based Method to Identify Entity Morphs
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