Information retrieval by constrained spreading activation in semantic networks
Information Processing and Management: an International Journal - Artificial Intelligence and Information Retrieval
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
A corpus analysis approach for automatic query expansion
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Application of Spreading Activation Techniques in InformationRetrieval
Artificial Intelligence Review
Combining multiple evidence from different types of thesaurus for query expansion
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Improving the effectiveness of information retrieval with local context analysis
ACM Transactions on Information Systems (TOIS)
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Information Retrieval
Inferring query models by computing information flow
Proceedings of the eleventh international conference on Information and knowledge management
Knowledge-Based Approaches to Query Expansion in Information Retrieval
AI '96 Proceedings of the 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Evaluating high accuracy retrieval techniques
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
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
Integrating word relationships into language models
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using term relationships in language models for information retrieval
Proceedings of the 14th ACM international conference on Information and knowledge management
Query expansion using random walk models
Proceedings of the 14th ACM international conference on Information and knowledge management
Improving the estimation of relevance models using large external corpora
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
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Improving weak ad-hoc queries using wikipedia asexternal corpus
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Query Expansion Using External Evidence
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Towards natural question guided search
Proceedings of the 19th international conference on World wide web
Combining WordNet and ConceptNet for automatic query expansion: a learning approach
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
Interactive sense feedback for difficult queries
Proceedings of the 20th ACM international conference on Information and knowledge management
Query expansion with conceptnet and wordnet: an intrinsic comparison
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
KORE: keyphrase overlap relatedness for entity disambiguation
Proceedings of the 21st ACM international conference on Information and knowledge management
Short-text domain specific key terms/phrases extraction using an n-gram model with wikipedia
Proceedings of the 21st ACM international conference on Information and knowledge management
Query expansion using path-constrained random walks
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Predicting the impact of expansion terms using semantic and user interaction features
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Diversified query expansion using conceptnet
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Query expansion is an important and commonly used technique for improving Web search results. Existing methods for query expansion have mostly relied on global or local analysis of document collection, click-through data, or simple ontologies such as WordNet. In this paper, we present the results of a systematic study of the methods leveraging the ConceptNet knowledge base, an emerging new Web resource, for query expansion. Specifically, we focus on the methods leveraging ConceptNet to improve the search results for poorly performing (or difficult) queries. Unlike other lexico-semantic resources, such as WordNet and Wikipedia, which have been extensively studied in the past, ConceptNet features a graph-based representation model of commonsense knowledge, in which the terms are conceptually related through rich relational ontology. Such representation structure enables complex, multi-step inferences between the concepts, which can be applied to query expansion. We first demonstrate through simulation experiments that expanding queries with the related concepts from ConceptNet has great potential for improving the search results for difficult queries. We then propose and study several supervised and unsupervised methods for selecting the concepts from ConceptNet for automatic query expansion. The experimental results on multiple data sets indicate that the proposed methods can effectively leverage ConceptNet to improve the retrieval performance of difficult queries both when used in isolation as well as in combination with pseudo-relevance feedback.