Combining WordNet and ConceptNet for Automatic Query Expansion: A Learning Approach

We present a novel approach that transforms the weighting task to a typical coarse-grained classification problem, aiming to assign appropriate weights for candidate expansion terms, which are selected from WordNet and ConceptNet by performing spreading activation. This transformation benefits us to automatically combine various features. The experimental results show that our approach successfully combines WordNet and ConceptNet and improves retrieval performance. We also investigated the relationship between query difficulty and effectiveness of our approach. The results show that query expansion utilizing the two resources obtains the largest improving effect upon queries of “medium” difficulty.

• Type: Conference
• Year: 2008
• Where: AIRS
• Authors: Ming-Hung Hsu, Ming-Feng Tsai, Hsin-Hsi Chen
• Keywords: AIRS 2008, Candidate Expansion Terms, Information Retrieval, Typical Coarse-grained Classification, WordNet