Reasoning Web. Web Logic Rules

The aim of this talk is to present a detailed, self-contained and comprehensive account of the state of the art in representing and reasoning with structured fuzzy knowledge. Fuzzy knowledge comes into play whenever one has to deal with concepts for which membership is a matter of degree (e.g., the degree of illness is a function of, among others, the body temperature). Specifically, we address the case of the fuzzy variants of conceptual languages of the OWL 2 family.

A fast growing torrent of data is being created by companies, social networks, mobile phones, smart homes, public transport vehicles, healthcare devices, and other modern infrastructures. Being able to unlock the potential hidden in this torrent of data would open unprecedented opportunities to improve our daily lives that were not possible before. Advances in the Internet of Things (IoT), Semantic Web and Linked Data research and standardization have already established formats and technologies for representing, sharing and re-using (dynamic) knowledge on the Web. However, transforming data into actionable knowledge requires to cater for (i) automatic mechanisms to discover and integrate heterogeneous data streams on the fly and extract patterns for applications to use, (ii) concepts and algorithms for context and quality-aware integration of semantic data streams, and (iii) the ability to synthesize domain-driven commonsense knowledge (and answers derived from it) with expressive inference that can capture decision analytics in a scalable way. In the first part of this lecture we will characterize the main approaches to stream processing for the Web of Data, showing how data quality and context can guide semantic integration. In the second part of this lecture we will focus on rule-based Web Stream Reasoning and illustrate how scalability and uncertainty issues can be addressed in a rule-based approach. We will discuss new challenges and opportunities in Web Stream Reasoning, briefly considering economical and societal impact in real application scenarios in a smart city context, and we will conclude by providing a brief overview of ongoing research and standardization activities in this area.

The World Wide Web is moving from a Web of hyper-linked documents to a Web of linked data. Thanks to the Semantic Web technological stack and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets connected with each other to form the so called LOD cloud. As of today, we have tons of RDF data available in the Web of Data, but only a few applications really exploit their potential power. The availability of such data is for sure an opportunity to feed personalized information access tools such as recommender systems. We present an overview on recommender systems and we sketch how to use Linked Open Data to build a new generation of semantics-aware recommendation engines.

Object-relational combinations are reviewed with a focus on the integrated Positional-Slotted, Object-Applicative (PSOA) RuleML. PSOA RuleML permits a predicate application (atom) to be without or with an Object IDentifier (OID) – typed by the predicate as its class – and, orthogonally, the predicate’s arguments to be positional, slotted, or combined. This enables six uses of atoms, which are systematically developed employing examples in presentation syntaxes derived from RuleML/POSL and RIF-BLD, and visualized in Scratch Grailog. These atoms, asserted as facts, are retrieved by object-relational look-in queries. On top of such facts, PSOA rules and their inferential querying are explored, e.g. permitting F-logic-like frames derived from relational joins. A use case of bidirectional SQL-PSOA-SPARQL transformation (schema/ontology mapping) is shown. Objectification and the presentation plus (XML-)serialization syntaxes of PSOA RuleML are described. The first-order model-theoretic semantics is formalized, blending (OID-over-)slot distribution, as in RIF, with integrated psoa terms, as in RuleML. The PSOATransRun implementation is surveyed, translating PSOA RuleML to TPTP (PSOA2TPTP) or Prolog (PSOA2Prolog).

This tutorial presents the principles of the OASIS LegalRuleML applied to the legal domain and discusses why, how, and when LegalRuleML is well-suited for modelling norms. To provide a framework of reference, we present a comprehensive list of requirements for devising rule interchange languages that capture the peculiarities of legal rule modelling in support of legal reasoning. The tutorial comprises syntactic, semantic, and pragmatic foundations, a LegalRuleML primer, as well as use case examples from the legal domain.

In this tutorial, we provide a comprehensive and up-to-date introduction to the fundamental concepts and recent progress in the area of Rulelog, a leading approach to semantic rules knowledge representation and reasoning.

This tutorial, which is a continuation of the tutorial “Datalog and Its Extensions for Semantic Web Databases” presented in the Reasoning Web 2012 Summer School, discusses recent advances in the Datalog\(^\pm \) family of languages for knowledge representation and reasoning. These languages extend plain Datalog with key modeling features such as existential quantification (signified by the “+” symbol), and at the same time apply syntactic restrictions to achieve decidability of ontological reasoning and, in some relevant cases, also tractability (signified by the symbol “\(-\)”). In this tutorial, we first introduce the main Datalog\(^\pm \) languages that are based on the well-known notion of guardedness. Then, we discuss how these languages can be extended with important features such as disjunction and default negation.

Recent years have seen an increasing interest in ontology-mediated query answering, in which the semantic knowledge provided by an ontology is exploited when querying data. Adding an ontology has several advantages (e.g. simplifying query formulation, integrating data from different sources, providing more complete answers to queries), but it also makes the query answering task more difficult. In this chapter, we give a brief introduction to ontology-mediated query answering using description logic (DL) ontologies. Our focus will be on DLs for which query answering scales polynomially in the size of the data, as these are best suited for applications requiring large amounts of data. We will describe the challenges that arise when evaluating different natural types of queries in the presence of such ontologies, and we will present algorithmic solutions based upon two key concepts, namely, query rewriting and saturation. We conclude the chapter with an overview of recent results and active areas of ongoing research.

Answer Set Programming (ASP) is a powerful rule-based language for knowledge representation and reasoning that has been developed in the field of logic programming and nonmonotonic reasoning. After more than twenty years from the introduction of ASP, the theoretical properties of the language are well understood and the solving technology has become mature for practical applications. In this paper, we first present the basics of the ASP language, and we then concentrate on its usage for knowledge representation and reasoning in real-world contexts. In particular, we report on the development of some industry-level applications with the ASP system DLV, and we illustrate two advanced development tools for ASP, namely ASPIDE and JDLV, which speed-up and simplify the implementation of applications.