Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Freebase: a collaboratively created graph database for structuring human knowledge
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Using unified famous objects (UFO) to automate Alzheimer's disease diagnostics
BIBMW '11 Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops
Less is more: selecting sources wisely for integration
Proceedings of the VLDB Endowment
Cascade: crowdsourcing taxonomy creation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Compact explanation of data fusion decisions
Proceedings of the 22nd international conference on World Wide Web
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Relevance of search-results is a key factor for any search engine. In order to return and rank the Web-pages that are most relevant to the query, contemporary search engines use complex ranking functions that depend on hundreds of features. For example, presence or absence of the query keywords on the page, their proximity, frequencies, HTML markup are just a few to name. Additional features might include fonts, tags, hyperlinks, metadata, and parts of the Web-page description. All this information is used by the search-engine to rank HTML Web pages returned to the user, but is unfortunately absent in free text that has no HTML markup, tags, hyperlinks, and any other metadata, except implicit natural language structure. Here we demonstrate one of the first Big text search engines that leverages hidden structure of the natural language sentences in order to process user queries and return more relevant search-results than a standard keyword-search. It provides a structured index extracted from the text using Natural Language Processing (NLP) that can be used to browse and query free text.