The rapid production of digital information makes the task of locating relevant information increasingly difficult. Keyword search alleviates this difficulty by retrieving documents containing keywords of interest. However, keyword search suffers from a number of issues such ambiguity, synonymy, and the inability to handle semantic constraints. Semantic search helps resolve these issues but is limited by the quality of annotations which are likely to be incomplete or imprecise. Hybrid search, a search technique that combines the merits of both keyword and semantic search, appears to be a promising solution.In this work we introduce HyKSS, a hybrid search system driven by extraction ontologies for both annotation creation and query interpretation. HyKSS is not limited to a single domain, but rather allows queries to cross ontological boundaries. We show that our hybrid search system, which uses a query driven dynamic ranking mechanism, outperforms keyword and semantic search in isolation, as well as a number of other non-HyKSS hybrid ranking approaches, over data sets of short topical documents. We also find that there is not a statistically significant difference between using multiple ontologies for query generation and simply selecting and using the best matching ontology.

No abstract

Abstract. The current web is a web of linked pages. Frustrated users search for facts by guessing which keywords or keyword phrases might lead them to pages where they can find facts. Can we make it possible for users to search directly for facts embedded in web pages? Instead of a web of human-readable pages containing machine-inaccessible facts, can the web be a web of machine-accessible facts superimposed over a web of human-readable pages? Ultimately, can the web be a web of knowledge that can provide direct answers to factual questions and support these answers by referencing and highlighting relevant base facts embedded in source pages? Answers to these questions call for distilling knowledge from the web's wealth of heterogeneous digital data into a web of knowledge. But how? Or, even more fundamentally, what, precisely, is this web of knowledge, and what is required to enable it? To answer these questions, we proffer a theoretical foundation for a web of knowledge: We formally define a computational view of knowledge in a way that enables practical construction and use of a web of knowledge.

No abstract

The rapid production of digital information makes the task of locating relevant information increasingly difficult. Keyword search alleviates this difficulty by retrieving documents containing keywords of interest. However, keyword search suffers from a number of issues such ambiguity, synonymy, and the inability to handle semantic constraints. Semantic search helps resolve these issues but is limited by the quality of annotations which are likely to be incomplete or imprecise. Hybrid search, a search technique that combines the merits of both keyword and semantic search, appears to be a promising solution.In this work we introduce HyKSS, a hybrid search system driven by extraction ontologies for both annotation creation and query interpretation. HyKSS is not limited to a single domain, but rather allows queries to cross ontological boundaries. We show that our hybrid search system, which uses a query driven dynamic ranking mechanism, outperforms keyword and semantic search in isolation, as well as a number of other non-HyKSS hybrid ranking approaches, over data sets of short topical documents. We also find that there is not a statistically significant difference between using multiple ontologies for query generation and simply selecting and using the best matching ontology.