TY - GEN
T1 - Exploiting symmetry in relational similarity for ranking relational search results
AU - Goto, Tomokazu
AU - Duc, Nguyen Tuan
AU - Bollegala, Danushka
AU - Ishizuka, Mitsuru
PY - 2010
Y1 - 2010
N2 - Relational search is a novel paradigm of search which focuses on the similarity between semantic relations. Given three words (A, B, C) as the query, a relational search engine retrieves a ranked list of words , where a word is assigned a high rank if the relation between A and B is highly similar to that between C and D. However, if C and D has numerous co-occurrences, then D is retrieved by existing relational search engines irrespective of the relation between A and B. To overcome this problem, we exploit the symmetry in relational similarity to rank the result set . To evaluate the proposed ranking method, we use a benchmark dataset of Scholastic Aptitude Test (SAT) word analogy questions. Our experiments show that the proposed ranking method improves the accuracy in answering SAT word analogy questions, thereby demonstrating its usefulness in practical applications.
AB - Relational search is a novel paradigm of search which focuses on the similarity between semantic relations. Given three words (A, B, C) as the query, a relational search engine retrieves a ranked list of words , where a word is assigned a high rank if the relation between A and B is highly similar to that between C and D. However, if C and D has numerous co-occurrences, then D is retrieved by existing relational search engines irrespective of the relation between A and B. To overcome this problem, we exploit the symmetry in relational similarity to rank the result set . To evaluate the proposed ranking method, we use a benchmark dataset of Scholastic Aptitude Test (SAT) word analogy questions. Our experiments show that the proposed ranking method improves the accuracy in answering SAT word analogy questions, thereby demonstrating its usefulness in practical applications.
UR - http://www.scopus.com/inward/record.url?scp=78049272336&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78049272336&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15246-7_55
DO - 10.1007/978-3-642-15246-7_55
M3 - Conference contribution
AN - SCOPUS:78049272336
SN - 3642152457
SN - 9783642152450
VL - 6230 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 595
EP - 600
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 11th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2010
Y2 - 30 August 2010 through 2 September 2010
ER -