Thomas Lukasiewicz – författare

This book contains revised and significantly extended versions of selected papers from three workshops on Uncertainty Reasoning for the Semantic Web (URSW), held at the International Semantic Web Conferences (ISWC) in 2011, 2012, and 2013. The 16 papers presented were carefully reviewed and selected from numerous submissions. The papers included in this volume are organized in topical sections on probabilistic and Dempster-Shafer models, fuzzy and possibilistic models, inductive reasoning and machine learning, and hybrid approaches.

This book contains revised and significantly extended versions of selected papers from three workshops on Uncertainty Reasoning for the Semantic Web (URSW), held at the International Semantic Web Conferences (ISWC) in 2011, 2012, and 2013. The 16 papers presented were carefully reviewed and selected from numerous submissions. The papers included in this volume are organized in topical sections on probabilistic and Dempster-Shafer models, fuzzy and possibilistic models, inductive reasoning and machine learning, and hybrid approaches.

This 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 bederived, 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.

This 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

ederived, 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.

The Semantic Web aims at allowing knowledge to be freely accessed and - changed by software. It is now widely recognized that if the Semantic Web is to contain deep knowledge, the need for new representation and reasoning te- niques is critical. These techniques need to ?nd the right trade-o? between - pressiveness, scalability, and robustness to deal with the inherently incomplete, contradictory, and uncertain nature of knowledge on the Web. The annual International Conference on Web Reasoning and Rule Systems (RR) addresses these needs and has grown into a major international forum for the discussion and dissemination of new results concerning Web Reasoning and Rule Systems. The ?rst three International Conferences on Web Reasoning and Rule Systems (see http://www.rr-conference.org), held in Innsbruck, A- tria (2007), Karlsruhe, Germany (2008), and Chantilly, Virginia, USA (2009), received enthusiastic support from the Web Reasoning community.This volume contains the papers presented at the Fourth International C- ference on Web Reasoning and Rule Systems (RR 2010), which was held in Bressanone/Brixen, Italy, September 22-24, 2010, and which continued the - cellence of the RR series. It contains nine full papers, six short papers, four poster/position papers, one PhD paper, and two system descriptions, which wereselectedoutof31submissionsfollowinga rigorousreviewingprocess,where each submission was reviewed by at least three program committee members. The volumealso containsextended abstractsof the threeinvited talks/tutorials.

This book constitutes the proceedings of the 7th International Symposium on Foundations of Information and Knowledge Systems, FoIKS 2012, held in Kiel, Germany, in March 2012.The 12 regular and 8 short papers, presented together with two invited talks in full paper-length, were carefully reviewed and selected from 53 submissions. The contributions cover foundational aspects of information and knowledge systems. These include the application of ideas, theories or methods from specific disciplines to information and knowledge systems, such as discrete mathematics, logic and algebra, model theory, informaiton theory, complexity theory, algorithmics and computation, statistics, and optimization.

This book constitutes the proceedings of the 7th International Symposium on Foundations of Information and Knowledge Systems, FoIKS 2012, held in Kiel, Germany, in March 2012.The 12 regular and 8 short papers, presented together with two invited talks in full paper-length, were carefully reviewed and selected from 53 submissions. The contributions cover foundational aspects of information and knowledge systems. These include the application of ideas, theories or methods from specific disciplines to information and knowledge systems, such as discrete mathematics, logic and algebra, model theory, informaiton theory, complexity theory, algorithmics and computation, statistics, and optimization.

This book contains revised and significantly extended versions of selected papers from three workshops on Uncertainty Reasoning for the Semantic Web (URSW), held at the International Semantic Web Conferences (ISWC) in 2008, 2009, and 2010 or presented at the first international Workshop on Uncertainty in Description Logics (UniDL), held at the Federated Logic Conference (FLoC) in 2010. The 17 papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on probabilistic and Dempster-Shafer models, fuzzy and possibilistic models, inductive reasoning and machine learning, and hybrid approaches.