LEADER 04394nam 22005775 450 001 9910254851303321 005 20240603095649.0 010 $a3-319-65229-X 024 7 $a10.1007/978-3-319-65229-0 035 $a(CKB)4100000000587303 035 $a(DE-He213)978-3-319-65229-0 035 $a(MiAaPQ)EBC5056813 035 $a(PPN)204534909 035 $a(EXLCZ)994100000000587303 100 $a20170922d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOntology-Based Data Access Leveraging Subjective Reports /$fby Gerardo I. Simari, Cristian Molinaro, Maria Vanina Martinez, Thomas Lukasiewicz, Livia Predoiu 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (VIII, 77 p. 32 illus., 14 illus. in color.) 225 1 $aSpringerBriefs in Computer Science,$x2191-5768 311 $a3-319-65228-1 320 $aIncludes bibliographical references at the end of each chapters. 327 $a1 Ontology-Based Data Access with Datalog+/- -- 2 Models for Representing User Preferences -- 3 Subjective Data: Model and Query Answering -- 4 Related Research Lines. 330 $aThis SpringerBrief  reviews the knowledge engineering problem of engineering objectivity in top-k query answering; essentially, answers must be computed taking into account the user?s preferences and a collection of (subjective) reports provided by other users. Most assume each report can be seen as a set of scores for a list of features, its author?s preferences among the features, as well as other information is discussed in this brief. These pieces of information for every report are then combined, along with the querying user?s preferences and their trust in each report, to rank the query results. Everyday examples of this setup are the online reviews that can be found in sites like Amazon, Trip Advisor, and Yelp, among many others. Throughout this knowledge engineering effort the authors adopt the Datalog+/? family of ontology languages as the underlying knowledge representation and reasoning formalism, and investigate several alternative ways in which rankings can b e derived, along with algorithms for top-k (atomic) query answering under these rankings. This SpringerBrief also investigate assumptions under which our algorithms run in polynomial time in the data complexity. Since this SpringerBrief contains a gentle introduction to the main building blocks (OBDA, Datalog+/-, and reasoning with preferences), it should be of value to students, researchers, and practitioners who are interested in the general problem of incorporating user preferences into related formalisms and tools.  Practitioners also  interested in using Ontology-based Data Access to leverage information contained in reviews of products and services for a better customer experience will be interested in this brief and  researchers working in the areas of Ontological Languages, Semantic Web, Data Provenance, and Reasoning with Preferences. 410 0$aSpringerBriefs in Computer Science,$x2191-5768 606 $aComputers 606 $aElectrical engineering 606 $aInformation Systems and Communication Service$3https://scigraph.springernature.com/ontologies/product-market-codes/I18008 606 $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035 615 0$aComputers. 615 0$aElectrical engineering. 615 14$aInformation Systems and Communication Service. 615 24$aCommunications Engineering, Networks. 676 $a006.332 700 $aSimari$b Gerardo I$4aut$4http://id.loc.gov/vocabulary/relators/aut$01058797 702 $aMolinaro$b Cristian$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aVanina Martinez$b Maria$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aLukasiewicz$b Thomas$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aPredoiu$b Livia$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254851303321 996 $aOntology-Based Data Access Leveraging Subjective Reports$92502520 997 $aUNINA