Separating Wheat from the Chaff – A Relationship Ranking Algorithm
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We address the problem of ranking relationships in an automatically constructed knowledge graph. We propose a probabilistic ranking mechanism that utilizes entity popularity, entity affinity, and support from text corpora for the relationships. Results obtained from preliminary experiments on a standard dataset are encouraging and show that our proposed ranking mechanism can find more informative and useful relationships compared to a frequency based approach.
KeywordsKnowledge Graph Ent Input Target Entity Domain-specific Corpus Large Human Effort
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