Improving relational search performance using relational symmetries and predictors

Relational similarity can be defined as the similarity between two semantic relations R and R' that exist respectively in two word pairs (A, B) and (C, D). Relational search, a novel search paradigm that is based on the relational similarity between word pairs, attempts to find a word D for the slot? in the query {(A,B), (C,?)} such that the relational similarity between the two word pairs (A, B) and (C, D) is a maximum. However, one problem frequently encountered by a Web-based relational search engine is that the inherent noise in Web text leads to incorrect measurement of relational similarity. To overcome this problem, we propose a method for verifying a relational search result that exploits the symmetric properties in proportional analogies. To verify a candidate result D for a query {(A, B), (C,?)}, we replace the original question mark by D to create a new query {(A,B),(?,D)} and verify that we can retrieve C as a candidate for the new query. The score of C in the new query can be seen as a complementary score of D because it reflects the reliability of D in the original query. Moreover, transformations of words in proportional analogies lead to relational symmetries that can be utilized to accurately measure the relational similarity between two semantic relations. For example, if the two word pairs (A, B) and (C, D) show a high degree of relational similarity then the two word pairs (B, A) and (D, C) also have a high degree of relational similarity. We apply this idea in relational search by using symmetric queries such as {(B, A), (D,?)} to create six queries for verifying a candidate answer D to improve the reliability of the verification process. Our experimental results on the Scholastic Aptitude Test (SAT) analogy benchmark show that the proposed method improves the accuracy of a relational search engine by a wide margin.