Précis: From unstructured keywords as queries to structured databases as answers

Simitsis, A; Koutrika, G; Ioannidis, Y

dc.contributor.author	Simitsis, A	en
dc.contributor.author	Koutrika, G	en
dc.contributor.author	Ioannidis, Y	en
dc.date.accessioned	2014-03-01T01:29:03Z
dc.date.available	2014-03-01T01:29:03Z
dc.date.issued	2008	en
dc.identifier.issn	1066-8888	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19091
dc.subject	Free-from queries	en
dc.subject	Keyword search	en
dc.subject	Query processing	en
dc.subject.classification	Computer Science, Hardware & Architecture	en
dc.subject.classification	Computer Science, Information Systems	en
dc.subject.other	INFORMATION	en
dc.subject.other	RETRIEVAL	en
dc.title	Précis: From unstructured keywords as queries to structured databases as answers	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s00778-007-0075-9	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s00778-007-0075-9	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	Précis queries represent a novel way of accessing data, which combines ideas and techniques from the fields of databases and information retrieval. They are free-form, keyword-based, queries on top of relational databases that generate entire multi-relation databases, which are logical subsets of the original ones. A logical subset contains not only items directly related to the given query keywords but also items implicitly related to them in various ways, with the purpose of providing to the user much greater insight into the original data. In this paper, we lay the foundations for the concept of logical database subsets that are generated from précis queries under a generalized perspective that removes several restrictions of previous work. In particular, we extend the semantics of précis queries considering that they may contain multiple terms combined through the AND, OR, and NOT operators. On the basis of these extended semantics, we define the concept of a logical database subset, we identify the one that is most relevant to a given query, and we provide algorithms for its generation. Finally, we present an extensive set of experimental results that demonstrate the efficiency and benefits of our approach.	en
heal.publisher	SPRINGER	en
heal.journalName	VLDB Journal	en
dc.identifier.doi	10.1007/s00778-007-0075-9	en
dc.identifier.isi	ISI:000253400700006	en
dc.identifier.volume	17	en
dc.identifier.issue	1	en
dc.identifier.spage	117	en
dc.identifier.epage	149	en