Machine Learning Technology Artificial Intelligence Technology Natural Language Processing Technology Semantic Web Technology Ontology Technology Reasoning Technology Knowledge Information Technology Collecting AI Conference Papers Digital Transformation Technology
The Semantic Web is one of the major current endeavours of applied computer science. The Semantic Web aims at enriching the existing Web with meta-data and processing methods so as to provide Web-based systems with advanced (so- called intelligent) capabilities, in particular with context-awareness and decision support.
The advanced capabilities required in most Semantic Web application scenarios primarily call for reasoning. Reasoning capabilities are offered by Semantic Web languages that are currently being developed. Most of these languages, however, are developed mainly from functionality-centred perspectives (e.g., ontology reasoning or access validation) or application-centred perspectives (e.g., Web service retrieval and composition). A perspective centred on the reasoning techniques complementing the above-mentioned activities appears desirable for Semantic Web systems and applications. The Summer School is devoted to this perspective. The “Reasoning Web” series of annual Summer Schools was started in 2005 on behalf of the work package “Education and Training (ET)” of the Network of Excellence REWERSE.
The previous article focused on several important application areas where Semantic Web technologies have proven to be particularly effective or promising in solving problems. This time, the focus was on the use of semantic technologies to enhance data access on the Web. To this end, the course introduced various techniques and formalisms that bridge semantic-based and data-intensive systems.
The school introduced Semantic Web foundations with a strong perspective on data management as well as applications of scalable semantic-based techniques for data querying. Topics of the lectures where design and analysis of reasoning procedures for Description Logics; Answer Set Programming basics, its modelling methodology and its principal extensions tailored for Semantic Web applications; languages for constraining and querying XML data; RDF databases theory and efficient and scalable support for RDF/OWL data stor- age, loading, inferencing and querying; tractable Description Logics and their use for Ontology-Based Data Access; the Social Semantic Desktop, which de- fines a user’s personal information environment as a source and end-point of the Semantic Web.
Description Logics (DLs) are a well-investigated family of logic-based knowledge representation formalisms, which can be used to represent the conceptual knowledge of an application domain in a structured and formally well-understood way. They are employed in various application domains, such as natural language processing, configuration, and databases, but their most notable success so far is the adoption of the DL-based language OWL as standard ontology language for the semantic web.
This article concentrates on the problem of designing reasoning procedures for DLs. After a short introduction and a brief overview of the research in this area of the last 20 years, it will on the one hand present approaches for reasoning in expressive DLs, which are the foundation for reasoning in the Web ontology language OWL DL. On the other hand, it will consider tractable reasoning in the more light-weight DL EL, which is employed in bio-medical ontologies, and which is the foundation for the OWL 2 profile OWL 2 EL.
Answer Set Programming (ASP) is a declarative problem solving paradigm, rooted in Logic Programming and Nonmonotonic Reasoning, which has been gaining increasing attention during the last years. This article is a gentle introduction to the subject; it starts with motivation and follows the historical development of the challenge of defining a semantics for logic programs with negation. It looks into positive programs over stratified programs to arbitrary pro- grams, and then proceeds to extensions with two kinds of negation (named weak and strong negation), and disjunction in rule heads. The second part then con- siders the ASP paradigm itself, and describes the basic idea. It shows some programming techniques and briefly overviews Answer Set solvers. The third part is devoted to ASP in the context of the Semantic Web, presenting some formalisms and mentioning some applications in this area. The article concludes with issues of current and future ASP research.
XML is the underlying representation formalism of much web-data. Thus to reason about web-data essentially boils down to reasoning about data in XML format. In this course the students learn about the main languages for querying XML data: XPath and XQuery. The course contains both theoretical work and practical examples.
The goal of this paper is to give an overview of the basics of the theory of RDF databases. We provide a formal definition of RDF that includes the features that distinguish this model from other graph data models. We then move into the fundamental issue of querying RDF data. We start by considering the RDF query language SPARQL, which is a W3C Recommendation since January 2008. We provide an algebraic syntax and a compositional semantics for this language, study the complexity of the evaluation problem for different fragments of SPARQL, and consider the problem of optimizing the evaluation of SPARQL queries, showing that a natural fragment of this language has some good properties in this respect. We furthermore study the expressive power of SPARQL, by comparing it with some well-known query languages such as relational algebra. We conclude by considering the issue of querying RDF data in the presence of RDFS vocabulary. In particular, we present a recently proposed extension of SPARQL with navigational capabilities.
Efficient and scalable support for RDF/OWL data storage, loading, inferencing and querying, in conjunction with already available support for enterprise level data and operations reliability requirements, can make databases suitable to act as enterprise-level RDF/OWL repository and hence become a viable platform for building semantic applications for the enterprise environments.
This tutorial outlines the requirements for supporting semantic technologies in databases including bulk load and data manipulation operations, inference based on RDFS, OWL and user-defined rules, and support for SPARQL queries. It also discusses the design choices for handling issues that arise in implementing support for storage and operations on large scale RDF/OWL data, and in general, touches upon the practical aspects related to RDF/OWL support that become important in enterprise environments. Semantic technologies support in Oracle Database is used as a case study to illustrate with concrete examples the key requirements and design issues.
The vision of the Social Semantic Desktop defines a user’s personal information environment as a source and end-point of the Se- mantic Web: Knowledge workers comprehensively express their information and data with respect to their own conceptualizations. Semantic Web languages and protocols are used to formalize these conceptualizations and for coordinating local and global information access.
A core challenge is to integrate existing legacy Desktop data into the Social Semantic Desktop. Semantic lifting is the process of capturing the semantics of various types of (semi-)structured data and/or non-semantic metadata and translating such data into Semantic Web conceptualizations.
From the way the vision of the Social Semantic Desktop is being pursued in the NEPOMUK project, we identified several requirements and research questions with respect to knowledge representation. In addition to the general question of the expressivity needed in such a scenario, two main challenges come into focus: i) How can we cope with the heterogeneity of knowledge models and ontologies, esp. multiple knowledge mod- ules with potentially different interpretations? ii) How can we support the tailoring of ontologies towards different needs in various exploiting applications?
In this paper, we present semantic lifting as a means to create semantic metadata and the Nepomuk Representation Language (NRL) as a means to represent these metadata. NRL is an approach to these two aforementioned questions that is based on named graphs for the modularization aspect and a view concept for the tailoring of ontologies. This view con- cept turned out to be of additional value, as it also provides a mechanism to impose different semantics on the same syntactical structure.
We furthermore present some of the ontologies that have been de- veloped with the help of NRL in the NEPOMUK project to build the semantic foundations for the Social Semantic Desktop.
Ontologies provide a conceptualization of a domain of interest. Nowadays, they are typically represented in terms of Description Logics (DLs), and are seen as the key technology used to describe the semantics of information at various sites. The idea of using ontologies as a conceptual view over data repositories is becoming more and more popular, but for it to become widespread in standard applications, it is fundamental that the conceptual layer through which the underlying data layer is accessed does not introduce a significant overhead in dealing with the data. Based on these observations, in recent years a family of DLs, called DL-Lite, has been proposed, which is specifically tailored to capture basic ontology and conceptual data modeling languages, while keeping low complexity of reasoning and of answering complex queries, in particular when the complexity is measured w.r.t. the size of the data. In this article, we present a de- tailed account of the major results that have been achieved for the DL-Lite family. Specifically, we concentrate on DL-LiteA,id, an expressive member of this family, present algorithms for reasoning and query answering over DL-LiteA,id ontologies, and analyze their computational complexity. Such algorithms exploit the distinguishing feature of the logics in the DL-Lite family, namely that ontology reasoning and answering unions of conjunctive queries is first-order rewritable, i.e., it can be delegated to a relational database management system. We analyze also the effect of extending the logic with typical DL constructs, and show that for most such extensions, the nice computational properties of the DL-Lite family are lost. We address then the problem of accessing relational data sources through an ontology, and present a solution to the notorious impedance mismatch between the abstract objects in the ontology and the values appearing in data sources. The solution exploits suitable mappings that create the objects in the ontology from the appropriate values extracted from the data sources. Finally, we discuss the QUONTO system that implements all the above mentioned solutions and is wrapped by the DIG-QUONTO server, thus providing a standard DL reasoner for DL-LiteA,id with extended functionality to access external data sources.
In the next article, we will discuss Reasoning Web 2010.
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