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In the previous article, we discussed Reasoning Web 2014. In this issue, we describe the tutorial papers prepared for the 11th Reasoning Web Summer School (RW 2015), held in Berlin, Germany, from July 31 to August 4, 2015.
The annual summer school of the Reasoning Web series was initiated in 2005 by the European Network of Excellence REWERSE, and since 2005 the School has become a major educational event in the field of reasoning techniques on the Web, with many young and experienced researchers participating.
The 2015 edition of the School was hosted by the Institute of Computer Science at the Free University of Berlin, Germany, and the Department of Computer Engineering at the University of Huddersfield, UK. As before, this year’s summer school was held in conjunction with the 9th International Conference on Web Reasoning and Rule Systems (RR 2015), but this year’s summer school was held in conjunction with the 9th International Web Rule Symposium (RuleML, Four lectures were joint with the RuleML pro-gramme) and the 25th International Conference on Automated Deduction.
In 2015, the theme of the conference was “Web Logic Rules.”
Recently, the research areas of Semantic Web and Linked Data have attracted much attention in academia and industry. since its inception in 2001, the Semantic Web has enriched the existing Web with metadata and processing methods to provide intelligent functions such as context awareness and decision support to Web-based systems The goal has been to provide such intelligent functions as context-awareness and decision support to Web-based systems. Over the years, the vision of the Semantic Web has fueled the efforts of many communities that have invested significant resources in developing vocabularies and ontologies for semantically annotating resources. In addition to ontologies, rules have long been a central part of the Semantic Web framework and one of the fundamental representational tools with logic as the unifying foundation; Linked Data aims to make RDF data available on the Web and interconnected with other data, thereby value by making RDF data available on the Web and interconnecting it with other data. Many of the advanced features required in Semantic Web and Linked Data application scenarios require reasoning. Therefore, a perspective centered on inference techniques that complements other research efforts in this area is desirable. This summer school provided insights on the Semantic Web, Linked Data, ontologies, rules, and logic from this perspective.
Details are given below.
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.
These are the lecture notes of a tutorial on higher-order modal logics held at the 11th Reasoning Web Summer School. After defining the syntax and (possible worlds) semantics of some higher- order modal logics, we show that they can be embedded into classi- cal higher-order logic by systematically lifting the types of propositions, making them depend on a new atomic type for possible worlds. This app- roach allows several well-established automated and interactive reasoning tools for classical higher-order logic to be applied also to modal higher- order logic problems. Moreover, also meta reasoning about the embedded modal logics becomes possible. Finally, we illustrate how our approach can be useful for reasoning with web logics and expressive ontologies, and we also sketch a possible solution for handling inconsistent data.
A fast growing torrent of data is being created by compa- nies, social networks, mobile phones, smart homes, public transport vehi- cles, healthcare devices, and other modern infrastructures. Being able to unlock the potential hidden in this torrent of data would open unprece- dented 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 for- mats 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 pat- terns 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 standard- ization 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 tech- nological stack and to the more recent Linked Open Data (LOD) initia- tive, 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 presenta- tion 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 Legal- RuleML 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 devis- ing 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. Rulelog is expressively powerful, computationally affordable, and has capable efficient implementations. A large subset of Rulelog is in draft as an industry standard1 to be submitted to RuleML2 and W3C3 as a dialect of Rule Interchange Format (RIF)
This tutorial, which is a continuation of the tutorial “Dat- alog and Its Extensions for Semantic Web Databases” presented in the Reasoning Web 2012 Summer School, discusses recent advances in the Datalog± family of languages for knowledge representation and reason- ing. 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 onto- logical reasoning and, in some relevant cases, also tractability (signi- fied by the symbol “−”). In this tutorial, we first introduce the main Datalog± languages that are based on the well-known notion of guard- edness. 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 algorith- mic 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 devel- oped in the field of logic programming and nonmonotonic reasoning. After more than twenty years from the introduction of ASP, the theoret- ical properties of the language are well understood and the solving tech- nology 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 con- texts. 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.
The TPTP World is a well established infrastructure that supports research, development, and deployment of Automated Theorem Proving (ATP) systems for classical logics. The TPTP World includes the TPTP problem library, the TSTP solution library, standards for writing ATP problems and reporting ATP solutions, tools and services for processing ATP problems and solutions, and it supports the CADE ATP System Competition (CASC). The TPTP World infrastructure has been deployed in a range of applications, in both academia and industry.
This tutorial introduces advanced problem solving techniques addressing the growing range of applications of Answer Set Programming (ASP; [1]) in prac- tice [2]; its particular focus lies on recent techniques needed for embedding ASP in complex software environments.
The tutorial starts with an introduction to the essential formal concepts of ASP [3], needed for understanding its semantics and solving technology. In fact, ASP solving rests on two major components: A grounder turning specifi- cations in ASP’s modeling language into propositional logic programs [4] and a solver computing a requested number of answer sets of the program [5]. We illustrate ASP’s grounding techniques and describe the major algorithms used in the ASP grounder gringo 4. This is accompanied with an introduction to the new ASP language standard [6]. The remainder of the tutorial is dedicated to using ASP in conjunction with Python for modeling complex reasoning sce- narios. This involves an introduction to the API of clingo 4, an ASP system extending clasp and gringo with control capacities expressible in Python (and Lua). See [7] for details. We illustrate this by developing a sample board game [8] and sketch more sophisticated usages in robotics [9] and preference handling [10].
In the next article, we will discuss Reasoning Web 2016.
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