出版社：The Japanese Society for Artificial Intelligence
摘要：Latent relational search is a new search paradigm based on the proportional analogy between two entity pairs. A latent relational search engine is expected to return the entity ``Paris'' as an answer to the question mark (?) in the query {(Japan, Tokyo), (France, ?)} because the relation between Japan and Tokyo is highly similar to that between France and Paris. We propose a method for extracting entity pairs from a text corpus to build an index for a high speed latent relational search engine. By representing the relation between two entities in an entity pair using lexical patterns, the proposed latent relational search engine can precisely measure the relational similarity between two entity pairs and can therefore accurately rank the result list. We evaluate the system using a Web corpus and compare the performance with an existing relational search engine. The results show that the proposed method achieves high precision and MRR while requiring small query processing time. In particular, the proposed method achieves an MRR of 0.963 and it retrieves correct answer in the Top 1 result for 95% of queries.
