Extracting semantic relations from query logs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Ranking very many typed entities on wikipedia
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
On the value of temporal information in information retrieval
ACM SIGIR Forum
Near-Term Prospects for Semantic Technologies
IEEE Intelligent Systems
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
User generated content: how good is it?
Proceedings of the 3rd workshop on Information credibility on the web
Relevance feedback between hypertext and Semantic Web search: Frameworks and evaluation
Web Semantics: Science, Services and Agents on the World Wide Web
Relevance feedback between web search and the semantic web
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Scope of ontological annotation in e-commerce
International Journal of Business Information Systems
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Semantic search seems to be an elusive and fuzzy target to IR, SW and NLP researchers. One reason is that this challenge lies in between all those fields, which implies a broad scope of issues and technologies that must be mastered. In this extended abstract we survey the work of Yahoo! Research at Barcelona to approach this problem. Our research is intended to produce a virtuous feedback circuit by using machine learning for capturing semantics, and, ultimately, for better search.