TY - GEN
T1 - Towards domain-specific semantic relatedness
T2 - 24th International Joint Conference on Artificial Intelligence, IJCAI 2015
AU - Sen, Shilad
AU - Johnson, Isaac
AU - Harper, Rebecca
AU - Mai, Huy
AU - Olsen, Samuel Horlbeck
AU - Mathers, Benjamin
AU - Vonessen, Laura Souza
AU - Wright, Matthew
AU - Hecht, Brent J
PY - 2015
Y1 - 2015
N2 - Semantic relatedness (SR) measures form the algorithmic foundation of intelligent technologies in domains ranging from artificial intelligence to human-computer interaction. Although SR has been researched for decades, this work has focused on developing general SR measures rooted in graph and text mining algorithms that perform reasonably well for many different types of concepts. This paper introduces domain-specific SR, which augments general SR by identifying, capturing, and synthesizing domain-specific relationships between concepts. Using the domain of geography as a case study, we show that domain-specific SR - and even geography-specific signals alone (e.g. distance, containment) without sophisticated graph or text mining algorithms - significantly outperform the SR state-of-the-art for geographic concepts. In addition to substantially improving SR measures for geospatial technologies, an area that is rapidly increasing in importance, this work also unlocks an important new direction for SR research: SR measures that incorporate domain-specific customizations to increase accuracy.
AB - Semantic relatedness (SR) measures form the algorithmic foundation of intelligent technologies in domains ranging from artificial intelligence to human-computer interaction. Although SR has been researched for decades, this work has focused on developing general SR measures rooted in graph and text mining algorithms that perform reasonably well for many different types of concepts. This paper introduces domain-specific SR, which augments general SR by identifying, capturing, and synthesizing domain-specific relationships between concepts. Using the domain of geography as a case study, we show that domain-specific SR - and even geography-specific signals alone (e.g. distance, containment) without sophisticated graph or text mining algorithms - significantly outperform the SR state-of-the-art for geographic concepts. In addition to substantially improving SR measures for geospatial technologies, an area that is rapidly increasing in importance, this work also unlocks an important new direction for SR research: SR measures that incorporate domain-specific customizations to increase accuracy.
UR - http://www.scopus.com/inward/record.url?scp=84949770810&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84949770810&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84949770810
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 2362
EP - 2370
BT - IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence
A2 - Wooldridge, Michael
A2 - Yang, Qiang
PB - International Joint Conferences on Artificial Intelligence
Y2 - 25 July 2015 through 31 July 2015
ER -