Foundations for the Web of Information and Services

Interoperability is a qualitative property of computing infrastructures that denotes the ability of sending and receiving systems to exchange and properly interpret information objects across system boundaries. Since this property is not given by default, the interoperability problem and the representation of semantics have been an active research topic for approximately four decades. Early database models such as the Relational Model used schemas to express semantics and implicitly aimed at achieving interoperability by providing programming independence of data storage and access. Thereafter the Entity Relationship Model was introduced providing the basic building blocks of modeling real-world semantics. With the advent of distributed and object-oriented databases, interoperability became an obvious need and an explicit research topic. After a number of intermediate steps such as hypertext and (multimedia) document models, the notions of semantics and interoperability became what they have been over the last ten years in the context of the World Wide Web. With this article we contribute a retrospective on semantics and interoperability research as applied in major areas of computer science. It gives domain experts and newcomers an overview of existing interoperability techniques and points out future research directions.

Research on Semantic Web has a long tradition in the Institute AIFB, and has largely influenced many research projects in the broader field of Applied Informatics. This article reports on a selection of research activities that illustrate the fruitful exchange of Semantic Web and Applied Informatics, covering the topics of Logic and Complexity Management, Efficient Algorithms, Organic Computing, and Business Process Management. This article does not have the character of a research paper in the classical sense. As a contribution to this Festschrift, it comprises a very special and personal selection of topics and research activities and should be considered as a message of greetings in honor of our colleague: Rudi Studer.

Processing of semantics by, e.g., analyzing ontologies, with the concomitant effort in building ontologies, will always have to compete with continuous advances in algorithms and encodings for graph theory, speech recognition and synthesis. Ultimately, the technique will prevail in a given situation that is superior in effectiveness (meeting a needed functionality not easily achievable by other means), and/or efficiency (providing the functionality with a minimum of resources). Coming from a database technology background, this paper gives a few examples of recent projects where the efficiency of database and web technologies was combined with the effectiveness of techniques around ontologies. The paper goes on to argue that by extending the concept of database views to semantics one obtains the means for systematically dealing with pragmatics.

The extensive work on Knowledge Engineering in the 1990s has resulted in a systematic analysis of task-types, and the corresponding problem solving methods that can be deployed for different types of tasks. That analysis was the basis for a sound and widely accepted methodology for building knowledge-based systems, and has made it possible to build libraries of reusable models, methods and code.

In this paper, we make a first attempt at a similar analysis for Semantic Web applications. We will show that it is possible to identify a relatively small number of task-types, and that, somewhat surprisingly, a large set of Semantic Web applications can be described in this typology. Secondly, we show that it is possible to decompose these task-types into a small number of primitive (“atomic”) inference steps. We give semi-formal definitions for both the task-types and the primitive inference steps that we identify. We substantiate our claim that our task-types are sufficient to cover the vast majority of Semantic Web applications by showing that all entries of the Semantic Web Challenges of the last 3 years can be classified in these task-types.

Prof. Rudi Studer has been technical director of a number of significant EU collaborative projects researching the application of semantic technology to Knowledge Management. In this chapter, drawing largely on work done in these projects, we provide an overview of the knowledge management problems and opportunities faced by large organisations; and indeed also shared by some smaller organisations. We show how semantic technologies can make a significant contribution. We look at the key application areas: searching and browsing for information; sharing knowledge; supporting processes, in particular informal processes; and extracting knowledge from unstructured information. In each application area we describe some solutions, either currently available or being researched. We do this to provide examples of what is possible rather than to provide a comprehensive list. The use of ontologies as a form of knowledge representation underlies everything we talk about in the chapter. Ontologies offer expressive power; they provide flexibility, with the ability to evolve dynamically unlike typical database schemata; and they make machine reasoning possible.

The Web Ontology Language (OWL) has been developed and standardised by the World Wide Web Consortium (W3C). It is one of the key technologies underpinning the Semantic Web, but its success has now spread far beyond the Web: it has become the ontology language of choice for a wide range of application domains. One of the key benefits flowing from OWL standardisation has been the development of a huge range of tools and infrastructure that can be used to support the development and deployment of OWL ontologies. These tools are now being used in large scale and commercial ontology development, and are widely recognised as being not simply useful, but essential for the development of the high quality ontologies needed in realistic applications.

While the amount of structured data published on the Web keeps growing (fostered in particular by the Linked Open Data initiative), the Web still comprises of mainly unstructured—in particular textual—content and is therefore a Web for human consumption. Thus, an important question is which techniques are most suitable to enable people to effectively access the large body of unstructured information available on the Web, whether it is semantic or not. While the hope is that semantic technologies can be combined with standard Information Retrieval approaches to enable more accurate retrieval, some researchers have argued against this view. They claim that only data-driven or inductive approaches are applicable to tasks requiring the organization of unstructured (mainly textual) data for retrieval purposes. We argue that the dichotomy between data-driven/inductive and semantic approaches is indeed a false one. We further argue that bottom-up or inductive approaches can be successfully combined with top-down or semantic approaches and illustrate this for a number of tasks such as Ontology Learning, Information Retrieval, Information Extraction and Text Mining.

Web mining is the application of data mining techniques to the Web. In the past eight years, we have been following this line of research within two growing subareas of the Web: the Semantic Web and the Social Web. In this paper, we recall our key observations, and discuss the next upcoming trend—the application of data mining to the Ubiquitous Web.

Despite the enormous amounts of information the Web has made accessible, we still lack the means to interconnect and link this information in a meaningful way in order to lift it from the level of information to the level of knowledge. Additionally, new sources of information about the physical world become available through the emerging sensor technologies. This information needs to be integrated with the existing information on the Web and in information systems which require (light-weight) semantics as a core building block. In this position paper we discuss the potential of a global knowledge space, and which research and technologies are required to enable our vision of networked knowledge.

The Web has proved to be an unprecedented success for facilitating the publication, use and exchange of information on a planetary scale, on virtually every topic, and representing an amazing diversity of opinions, viewpoints, mindsets and backgrounds. Its design principles and core technological components have lead to an unprecedented growth and mass collaboration. This trend is also finding increasing adoption in business environments. Nevertheless, the Web is also confronted with fundamental challenges with respect to the purposeful access, processing and management of these sheer amounts of information, whilst remaining true to its principles, and leveraging the diversity inherently unfolding through world wide scale collaboration. In this chapter we will motivate engagement with these challenges and the development of methods, techniques, software and data sets that leverage diversity as a crucial source of innovation and creativity. We consider how to provide enhanced support for feasibly managing data at a very large scale, and design novel algorithms that reflect diversity in the ways information is selected, ranked, aggregated, presented and used. A successful diversity-aware information management solution will scale to very large amounts of data and hundreds of thousands of users, but also to a plurality of points of views and opinions. Research towards this end is carried out on realistic data sources with billions of items, through open source extensions to popular communication and collaboration platforms such as MediaWiki and WordPress.

Ontologies constitute formal models of some aspect of the world that may be used for drawing interesting logical conclusions even for large models. Software models capture relevant characteristics of a software artefact to be developed, yet, most often these software models have no formal semantics or the underlying (often graphical) software language varies between different use cases in a way that makes it hard if not impossible to even fix its semantics. In this contribution, we survey the use of ontology technologies for software models in order to carry advantages over to the software modeling domain. It will demonstrate that ontology-based metamodels constitute a core means for exploiting expressive ontology reasoning in the software modeling domain while remaining flexible enough to accommodate varying needs of software modelers.

Intelligent infrastructures, in particular Semantic Technologies, have accompanied the development to service-centric architectures and computing paradigms since the early days. In this contribution, we assess the current state of these technologies with respect to the intelligent management of services and we describe the main developments that will most likely shape the field in the future. As a general introduction, we structure the landscape of services according to three complementary dimensions: (i) Level of Information and Communication Technology (ICT) Involvement, (ii) Level of Co-Creation, and (iii) Service Role. In the main part, we focus on the following technology areas: semantic description of services incl. the acquisition of service descriptions, discovery and ranking algorithms, service composition, service markets, as well as service monitoring and analytics.

Cloud computing has become a generic umbrella term for the flexible delivery of IT resources—such as storage, computing power, software development platforms, and applications—as services over the Internet. The foremost innovation is that the IT infrastructure no longer lies with the user, breaking up the previously monolithic ownership and administrative control of its assets. The combination of cloud computing and semantic technologies holds great potential. In this chapter, we analyze three ways in which cloud computing and semantic technologies can be combined: (1) building on cloud computing technologies, e.g. from the area of distributed computing, to realize better semantic applications and enable semantic technologies to scale to ever larger data sets, (2) delivering semantic technologies as services in the cloud, and (3) using semantic technologies to improve cloud computing, in particular to further improve automatic data-center management. For each of these dimensions we identify challenges and opportunities, provide a survey, and present a research roadmap.

Complex event processing is about processing huge amounts of information in real time, in a rather complex way. The degree of complexity is determined by the level of the interdependencies between information to be processed. There are several more or less traditional operators for defining these interdependencies, which are supported by existing approaches and the main competition is around the speed (throughput) of processing. However, novel application domains like Future Internet are challenging complex event processing for a more comprehensive approach: from how to create complex event patterns over the heterogeneous event sources (including textual data), to how to efficiently detect them in a distributed setting, including the usage of background knowledge. In this chapter we present an approach for intelligent CEP (iCEP) based on the usage of semantic technologies. It represents an end-to-end solution for iCEP starting from the definition of complex event patterns, through intelligent detection, to advanced 3-D visualization of complex events. At the center of the approach is the semantic model of complex events that alleviates the process of creating and maintaining complex event patterns. The approach utilizes logic-based processing for including domain knowledge in the complex event detection process, leading to complex event reasoning. This approach has been implemented in the web-based framework called iCEP Studio.

This chapter exemplarily illustrates the role that semantic technologies can play in knowledge management. Starting from a conceptual overview of knowledge management, the role of semantic technologies is explored along two dimensions: (1) on the one hand, the degree of formality of explicit knowledge (from tags in folksonomies to F-Logic rules); and (2) on the other hand, the degree of externalization of knowledge (from implicit knowledge in human’s heads to actionable knowledge in expert systems). Several examples from industry and research are used to illustrate operating points along these dimensions.

Semantic MediaWiki (SMW) is an extension of MediaWiki—a widely used wiki-engine that also powers Wikipedia—which makes semantic technologies available to broad user communities by smoothly integrating with the established wiki usage. SMW is used productively on a large number of sites world-wide in application areas ranging from science over knowledge management to leisure activities. Meanwhile, a vibrant ecosystem of third-party extensions has grown around SMW, offering many options for extended features and customizations. Yet, the original vision of establishing “Semantic Wikipedia” has remained important for the development of the SMW project, leading to a strong focus on usability and scalability.

Ontoprise GmbH is a leading provider of industry-proven Semantic Web infrastructure technologies and products supporting dynamic semantic information integration and information management processes at the enterprise level. Ontoprise has developed a comprehensive product suite to support the deployment of semantic technologies in a range of industries. In this article, we present some typical applications where we successfully demonstrated the feasibility, maturity, and power of semantic technologies in real enterprise settings.