Semantic Web

Tutorial: Applications for the Semantic Web

Description

The Semantic Web vision is considered the next generation of the Web that enables sharing data, resources and knowledge between parties that belong to different organizations, different cultures, and/or different communities. Ontologies and rules play the main role in the Semantic Web for publishing community vocabularies and policies, for annotating resources and for turning Web applications into inference-enabled collaboration platforms. After a short introduction into the basic concepts, standards, and tools of the Semantic Web, we present how today's Semantic Web tools, languages, and techniques can be used in various application. We first start from the use of the Semantic Web technologies for providing online educators with feedback about how their students use online courses in learning management systems. Next, we demonstrate the use of the Semantic Web technologies and text mining techniques to improve software development process and software maintenance. Finally, we explain the use of the Semantic Web technologies in multimedia-enhanced applications.

Empowering the Enzyme Biotechnologist with Ontologies

Introduction

The FungalWeb Ontology is a knowledge representation vehicle designed to integrate information relevant to industrial applications of enzymes. The ontology integrates information from established sources and supports complex queries to the instantiated FungalWeb knowledge base. The ontology represents prototype Semantic Web technology customized to the domain of industrial enzymes with a focus on enzyme discovery, commercial enzyme products and vendors, and the industrial applications and benefits of industrial enzymes. Using a series of application scenarios we demonstrate the utility of this 'Semantic Web' infrastructure to the enzyme biotechnologist.

Ontology Design for Biomedical Text Mining

Abstract

Text Mining in biology and biomedicine requires a large amount of domain-specific knowledge. Publicly accessible resources hold much of the information needed, yet their practical integration into natural language processing (NLP) systems is fraught with manifold hurdles, especially the problem of semantic disconnectedness throughout the various resources and components. Ontologies can provide the necessary framework for a consistent semantic integration, while additionally delivering formal reasoning capabilities to NLP.

In this chapter, we address four important aspects relating to the integration of ontology and NLP: (i) An analysis of the different integration alternatives and their respective vantages; (ii) The design requirements for an ontology supporting NLP tasks; (iii) Creation and initialization of an ontology using publicly available tools and databases; and (iv) The connection of common NLP tasks with an ontology, including technical aspects of ontology deployment in a text mining framework. A concrete application example—text mining of enzyme mutations—is provided to motivate and illustrate these points.

Keywords: Text Mining, NLP, Ontology Design, Ontology Population, Ontological NLP

Empowering Software Maintainers with Semantic Web Technologies

Achtung Seilbahn!

Abstract

Software maintainers routinely have to deal with a multitude of artifacts, like source code or documents, which often end up disconnected, due to their different representations and the size and complexity of legacy systems. One of the main challenges in software maintenance is to establish and maintain the semantic connections among all the different artifacts. In this paper, we show how Semantic Web technologies can deliver a unified representation to explore, query and reason about a multitude of software artifacts. A novel feature is the automatic integration of two important types of software maintenance artifacts, source code and documents, by populating their corresponding sub-ontologies through code analysis and text mining. We demonstrate how the resulting "Software Semantic Web" can support typical maintenance tasks through ontology queries and DL reasoning, such as security analysis, architectural evolution, and traceability recovery between code and documents.

Keywords: Software Maintenance, Ontology Population, Text Mining.

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