KEYSTONE - semantic KEYword-based Search on sTructured data sOurcEs

Description

As the demographic of Web users has shifted from tech-savvy early adopters towards the general population, keyword search has become the de-facto mechanism used to find information – as opposed to structured queries used in traditional information systems – even while more and more structured data has become available on the Web. This is mainly due to the simplicity of keyword search and the basic knowledge level required by users to get to some desired information. However, existing approaches suffer from a number of limitations when applied to multi-source scenarios requiring some form of query planning to access all of these sources, and with frequent updates making it difficult to produce an effective implementation of data indexes. Typical scenarios include open data, big data and virtual data integration systems. Therefore, building effective keyword search techniques can have a significant 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 create synergies between several disciplines, such as semantic data management, the Semantic Web, information retrieval, artificial intelligence, machine learning, user interaction, service science, service design, and natural language processing.

Researchers

Funded by

EU COST

Keywords

keyword-based search, structured data sources, deep web

Start Date

2013-10-15

End Date

2016-10-15

Edited Books