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000801056 24500 $$aOntology-based data access leveraging subjective reports$$h[electronic resource] /$$cGerardo I. Simari ... [et al.].
000801056 260__ $$aCham :$$bSpringer,$$c2017.
000801056 300__ $$a1 online resource.
000801056 336__ $$atext$$btxt$$2rdacontent
000801056 337__ $$acomputer$$2rdamedia
000801056 338__ $$aonline resource$$2rdacarrier
000801056 347__ $$atext file$$bPDF$$2rda
000801056 4901_ $$aSpringerBriefs in computer science
000801056 504__ $$aIncludes bibliographical references.
000801056 5050_ $$aAcknowledgments; Contents; 1 Ontology-Based Data Access with Datalog+/-; 1.1 Description Logics; 1.2 Ontology-Based Data Access (OBDA); 1.3 The Datalog+/- Family of Ontology Languages; 1.3.1 Preliminary Concepts and Notations; 1.3.2 Syntax and Semantics of Datalog+/-; 1.3.2.1 Conjunctive Query Answering; 1.3.2.2 The TGD Chase; 1.3.2.3 Computational Complexity; 1.3.3 Datalog+/- Fragments: Towards Decidability and Tractability; References; 2 Models for Representing User Preferences; 2.1 Basic Definitions and Notation; 2.2 Preferences à la Databases; 2.2.1 Relational Databases
000801056 5058_ $$a2.2.2 Ontology Languages2.2.3 Models for Uncertain Preferences; 2.3 Preferences à la Philosophy and Related Disciplines; 2.4 Final Notes; References; 3 Subjective Data: Model and Query Answering; 3.1 A Logic-Based Data Model; 3.1.1 Trust Measures over Reports; 3.1.2 Relevance of Reports; 3.2 Query Answering Based on Subjective Reports; 3.2.1 A Basic Approach; 3.2.2 Leveraging Trust and Relevance to a Greater Extent; 3.3 Towards More General Reports; References; 4 Related Research Lines; 4.1 Assessing the Impact of Reviews; 4.2 Fraud and Spam Detection; 4.3 Review Summarization
000801056 5058_ $$a4.4 Fact Finding and Inconsistency Detection4.5 Improving Recommender Systems Leveraging Review Text; References
000801056 506__ $$aAccess limited to authorized users.
000801056 520__ $$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.
000801056 650_0 $$aOntologies (Information retrieval)
000801056 650_0 $$aData mining.
000801056 7001_ $$aSimari, Gerardo I.
000801056 77608 $$iPrint version:$$z3319652281$$z9783319652283$$w(OCoLC)994852470
000801056 830_0 $$aSpringerBriefs in computer science.
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