Combining WordNet and ConceptNet for automatic query expansion: a learning approach

Authors:
Ming-Hung Hsu;Ming-Feng Tsai;Hsin-Hsi Chen
Affiliations:
Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan;Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan;Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
Venue:
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Year:
2008

Citing 13
Cited 6

On the use of spreading activation methods in automatic information

SIGIR '88 Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using lexical-semantic relations

SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using local and global document analysis

SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
An information-theoretic approach to automatic query expansion

ACM Transactions on Information Systems (TOIS)
Predicting query performance

SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Query Expansion by Mining User Logs

IEEE Transactions on Knowledge and Data Engineering
An effective approach to document retrieval via utilizing WordNet and recognizing phrases

Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
ConceptNet — A Practical Commonsense Reasoning Tool-Kit

BT Technology Journal
Word sense disambiguation in queries

Proceedings of the 14th ACM international conference on Information and knowledge management
Mining dependency relations for query expansion in passage retrieval

SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
What makes a query difficult?

SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Information retrieval with commonsense knowledge

SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion with conceptnet and wordnet: an intrinsic comparison

AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology

A method to predict social annotations

Proceedings of the 17th ACM conference on Information and knowledge management
Constructing concept relation network and its application to personalized web search

Proceedings of the 14th International Conference on Extending Database Technology
Tapping into knowledge base for concept feedback: leveraging conceptnet to improve search results for difficult queries

Proceedings of the fifth ACM international conference on Web search and data mining
Genetically optimizing query expansion for retrieving activities from the web

Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
Efficient semantic network construction with application to PubMed search

Knowledge-Based Systems
Diversified query expansion using conceptnet

Proceedings of the 22nd ACM international conference on Conference on information & knowledge management

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.