Information retrieval by constrained spreading activation in semantic networks
Information Processing and Management: an International Journal - Artificial Intelligence and Information Retrieval
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
SALSA: the stochastic approach for link-structure analysis
ACM Transactions on Information Systems (TOIS)
Enhancing retrieval with hyperlinks: a general model based on propositional argumentation systems
Journal of the American Society for Information Science and Technology - Mathematical, logical, and formal methods in information retrieval
PageRank without hyperlinks: structural re-ranking using links induced by language models
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance weighting for query independent evidence
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
ACM SIGIR Forum
Respect my authority!: HITS without hyperlinks, utilizing cluster-based language models
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
A probabilistic relevance propagation model for hypertext retrieval
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Comparing the effectiveness of hits and salsa
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Overview of the INEX 2007 Ad Hoc Track
Focused Access to XML Documents
Is Wikipedia link structure different?
Proceedings of the Second ACM International Conference on Web Search and Data Mining
WikiRelate! computing semantic relatedness using wikipedia
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
What's in a Link? From Document Importance to Topical Relevance
ICTIR '09 Proceedings of the 2nd International Conference on Theory of Information Retrieval: Advances in Information Retrieval Theory
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Examining the "leftness" property of Wikipedia categories
Proceedings of the 20th ACM international conference on Information and knowledge management
Proceedings of the 4th Information Interaction in Context Symposium
Improving contextual advertising by adopting collaborative filtering
ACM Transactions on the Web (TWEB)
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Why do links work? Link-based ranking algorithms are based on the often implicit assumption that linked documents are semantically related to each other, and that link information is therefore useful for retrieval. Although the benefits of link information are well researched, this underlying assumption on why link evidence works remains untested, and the main aim of this paper is to do exactly that. Specifically, we use Wikipedia because it has a dense link structure in combination with a large category structure, which allows for an independent measurement of the semantic relatedness of linked documents. Our main findings are that: 1) global, query-independent link evidence, is not affected by the semantic nature of the links, and 2) for local, query-dependent link evidence, the effectiveness of links increases as their semantic distance decreases. That is, we directly observe that links between semantically related pages are more effective for ad hoc retrieval than links between unrelated ones. These findings confirm and quantify the underlying assumption of existing link-based methods, which sheds further light on our understanding of the nature of link evidence. Such deeper understanding is instrumental for the development of novel link-based methods.