KEYSTONE – Semantic Keyword-Based Search on Structures Data Sources


  • Project identification: Keystone – Semantic Keyword-Based Search on Structures Data Sources                            [ IC COST Action IC1302 ]
  • Coordinator: Francesco Guerra (Università degli Studi di Modena e Reggio Emilia, Italy)
  • Responsible at CLUNL: Raquel Amaro (Lexicoloy, Lexicography and Terminology group)
  • Duration: Sep. 2015 – Dec. 2017
  • Funding entity: European Science Foundation
  • Keywords: Keyword-based Search on Structured Data Sources: Metadata Extraction and Indexing; Semantic Database Management and Summarisation; Analysis of Keyword Queries.
  • Website:


“As more and more structured data becomes available on the Web, and Web users shift towards a more general non-technical population, keyword searching is becoming a valuable alternative to traditional structured queries, mainly due to its simplicity and the basic knowledge required from users. Nevertheless, existing approaches suffer from a number of limitations when applied to multi-source scenarios requiring some form of query planning, and with frequent updates precluding any effective implementation of data indexes. Typical scenarios include open data, big data and virtual data integration systems. 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 proposes to join synergies from 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.”

(Retrieved from the website COST Action.)

Participating Entities

Full information at:


Cadegnani, Sara et al. (2017). Exploiting Linguistic Analysis on URLs for Recommending Web Pages: a comparative study. In: N. Nguyen, R. Kowalczyk, A. Pinto and J. Cardoso, eds., Transactions on Computational Collective Intelligence XXVI. Vol. 10190 [n.d.]: Springer, pp.26-45. e-ISBN 978-3-319-59268-8; ISBN 978-3-319-59267-1.
DOI 10.1007/978-3-319-59268-8_2