Search Engine-Based Web Information Extraction

In this chapter we discuss approaches to find, extract, and structure information from natural language texts on the Web. Such structured information can be expressed and shared using the standard Semantic Web languages and hence be machine interpreted. In this chapter we focus on two tasks in Web information extraction. The first part focuses on mining facts from the Web, while in the second part, we present an approach to collect community-based meta-data. A search engine is used to retrieve potentially relevant texts. From these texts, instances and relations are extracted. The proposed approaches are illustrated using various case-studies, showing that we can reliably extract information from the Web using simple techniques.

The Social Semantic Desktop

This chapter introduces the general vision of the Social Semantic Desktop (SSD) and details it in the context of the NEPOMUK project. It outlines the typical SSD requirements and functionalities that were identified from real world scenarios. In addition, it provides the design of the standard SSD architecture together with the ontology pyramid developed to support it. Finally, the chapter gives an overview of some of the technical challenges that arise from the actual development process of the SSD.

Association Analytics for Network Connectivity in a Bibliographic and Expertise Dataset

This chapter highlights the benefits of semantics for analysis of the collaboration network in a bibliography dataset. Metadata of publications was used for extracting keywords and terms, which can be the starting point towards building a taxonomy of topics. The aggregated effect of the topics over all publications of an author can be used to determine his/her areas of expertise. We also highlight the value of using a taxonomy of topics in searching experts on a given topic.

Semantic Processing of Monitoring Data in Industrial Applications

This chapter discusses the potential of semantically processing monitoring data in industrial applications such as condition-based maintenance and monitoring of complex systems and infrastructure networks. It points out the particular requirements involved and gives a comprehensive and structured overview of current approaches and engineering solutions in these fields. As a case study for engineering industrial end-to-end solutions, it presents the design and prototype implementation of a decision support system in the railway domain.

Benchmarking in the Semantic Web

The Semantic Web technology needs to be thoroughly evaluated for providing objective results and obtaining massive improvement in its quality; thus, the transfer of this technology from research to industry will speed up. This chapter presents software benchmarking, a process that aims to improve the Semantic Web technology and to find the best practices. The chapter also describes a specific software benchmarking methodology and shows how this methodology has been used to benchmark the interoperability of ontology development tools, employing RDF(S) as the interchange language.

RapidOWL

In this chapter we give a brief overview on the recently emerging concepts of Social Software and Web 2.0. Both concepts stress the adaptive, agile methodological character of communication and collaboration. In order to lift the adaptive collaboration and communication patterns of Social Software and the Web 2.0 towards a truly semantic collaboration, we outline an adaptive knowledge engineering methodology–RapidOWL. It is inspired by adaptive software development methodologies from software engineering and emphasises support for small end-user contributions to knowledge bases.

Fault-Tolerant Emergent Semantics in P2P Networks

To survive in the 21st century, enterprises need to collaborate. Collaboration at the enterprise-level presupposes the interoperability of the underlying information systems. Access to heterogeneous information sources must be provided transparently while maintaining their autonomy. Further, the availability of nearly unlimited information calls for efficient and precise information retrieval, which can be achieved by making the semantics embedded in information sources explicit. Solving the semantic interoperability problem becomes imperative to the success of information search and retrieval applications and enterprises that rely on them. Inspired by self-organizing systems found in biology, physics, and computing, the approach of emergent semantics has been proposed as a solution to the semantic interoperability problem. Emergent semantics refers to the bottom-up construction of interoperable systems, in which semantically related peers are discovered and linked together during the normal operation of the system. Individual information source providers will provide mappings (so-called semantic bridges) between their own local and semantically related foreign information sources. Emergent Semantics in a peer-to-peer (P2P) network is the lowest common knowledge, semantically relevant concepts, among all the peers of the network. Local mappings between peers with different knowledge representations, and their correctness, are prerequisites for the creation of emergent semantics. Yet, approaches to emergent semantics often fail to distinguish between permanent and transient mapping faults. This may result in erroneously labeling peers as having incompatible knowledge representations. In turn, this can further prevent such peers from interacting with other semantically related peers . This is because, in emergent semantics, peers use past interactions to determine which peers they will interact with in future collaborations. This chapter will explore the issue of semantic mapping faults. This issue has not received enough attention in the literature. Specifically, it will focus on the effect of non-permanent semantic mapping faults on both inclusiveness of semantic emergence and robustness of applications and systems that use semantic mappings. A fault-tolerant emergent semantics algorithm with the ability to resist transient semantic mapping faults is also provided. The contributions of this chapter are: (a) an analysis of the impact of the semantic mapping faults on the inclusiveness of semantic knowledge sharing in P2P systems, (b) a preliminary solution to the problems created by semantic mapping faults in P2P semantic knowledge sharing systems, and (c) a qualitative analysis of the causal links between fault causes and fault types. The rest of this chapter is organized as follows. Section II provides broad discussion and literature review about semantic interoperability problem among heterogeneous information source. Section III defines what we mean by a semantic mapping fault and the types of faults. Section IV lists sources of semantic mapping faults. Section V classifies temporal semantic mapping faults. Section VI describes the emergent semantics approach. Section VII presents an algorithm to eliminate the harmful effects of transient mapping faults on emergent semantics (fault-tolerant emergent semantics). Section VIII concludes the chapter and Section IX identifies directions for future work.

Semantic Annotation and Ontology Population

This chapter deals with issues related to semantic annotation and ontology population within the framework defined by the Semantic Web (SW). The vision of the Semantic Web, initiated in 1998 by Sir Tim Berners-Lee, aims to structure the information available on the Web. To achieve that goal, the resources, textual or multimedia, must be semantically tagged by metadata so that software agents can utilize them. The idea developed in this chapter is to combine the information extraction (IE) tools with knowledge representation tools from the SW for the achievement of the 2 parallel tasks of semantic annotation and ontology population. The goal is to extract relevant information from the resources based on an ontology, then to populate that ontology with new instances according to the extracted information, and finally to use those instances to semantically annotate the resource. Despite all integration efforts, there is currently a gap between the representation formats of the linguistic tools used to extract information and those of the knowledge representation tools used to model the ontology and store the instances or the semantic annotations. The stake consists in proposing a methodological reflexion on the interoperability of these technologies as well as designing operational solutions for companies and, on a broader scale, for the Web.

Building Semantic Web Portals with a Model-Driven Design Approach

This chapter presents an extension to Web application conceptual models toward Semantic Web. Conceptual models and model-driven methodologies are widely applied to the development of Web applications because of the advantages they grant in terms of productivity and quality of the outcome. Although some of these approaches are meant to address Semantic Web applications too, they do not fully exploit the whole potential deriving from interaction with ontological data sources and from semantic annotations. The authors claim that Semantic Web applications represent an emerging category of software artifacts, with peculiar characteristics and software structures, and hence need some specific methods and primitives for achieving good design results. In particular the contribution presented in this chapter is an extension of the WebML modeling framework that fulfils most of the design requirements emerging in the new area of Semantic Web. The authors generalize the development process to cover Semantic Web needs and devise a set of new primitives for ontology importing and querying. The chapter also presents a comparison of the proposed approach with the most relevant existing proposals and positioned with respect to the background and adopted technologies.

Personalization Based on Semantic Web Technologies

Personalization techniques provide optimized access to content and services, based on the preferences and the characteristics of each individual user. Nowadays many applications, either Web-based or not, call for personalized behavior. Obviously, such behavior leads to an increased demand for knowledge management, since personalization is based on user profiles, user preferences, usage policies, and other knowledge components. The main topic of this chapter is the investigation of how well Semantic Web technologies apply to personalized applications. Semantic Web is a relatively new platform for developing (distributed) knowledge-based applications that has gained great popularity in previous years. Hence, this chapter surveys the most prominent techniques for personalization in the context of the Semantic Web. It discusses and compares different approaches to architectural and engineering techniques and other issues relevant to this hot topic. The chapter provides foundational knowledge on this topic, as well as discussion on some key implementation issues.