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KEYSTONE - Semantic keyword-based search on structured data sources

As more and more data becomes available on the Web, as its complexity increases and as the Web's user base shifts towards a more general non-technical population, keyword searching is becoming a valuable alternative to traditional SQL queries, mainly due to its simplicity and the basic knowledge required from users. Existing approaches suffer from a number of limitations when applied to multi-source scenarios requiring some form of query planning, without direct access to database instances, and with frequent updates precluding any effective implementation of data indexes. Typical scenarios include Deep Web databases, virtual data integration systems and data on the Web. Therefore, building effective keyword searching techniques can have an extensive impact since it allows non-professional users to access large amounts of information stored in structured repositories through simple keyword-based query interfaces. This revolutionises the paradigm of searching for data since users are offered access to structured data in a similar manner to the one they already use for documents. To build a successful, unified and effective solution - due to the multifaceted nature of the problem – the Action "semantic KEYword-based Search on sTructured data sOurcEs" (KEYSTONE) proposes to join synergies from several disciplines, such as semantic data management, the Semantic Web, information retrieval, artificial intelligence, machine learning, user interaction and interface design, and natural language processing.

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