Abstract

Comprehension is an essential part of software maintenance. Only software that is well understood can evolve in a controlled manner. In this paper, we present a formal process model to support the comprehension of software systems by using Ontology and Description Logic. This formal representation supports the use of reasoning services across different knowledge resources and therefore, enables us to provide users with guidance during the comprehension process that is context sensitive to their particular comprehension task.

Keywords: Software maintenance, program comprehension, process modeling, ontological reasoning

Abstract

Traceability links provide support for software engineers in understanding the relations and dependencies among software artifacts created during the software development process. In this research, we focus on re-establishing traceability links between existing source code and documentation to support reverse engineering. We present a novel approach that addresses this issue by creating formal ontological representations for both the documentation and source code artifacts.

Abstract

A challenge of existing program comprehension approaches is to provide consistent and flexible representations for software systems. Maintainers have to match their mental models with the different representations these tools provide. In this paper, we present a novel approach that addresses this issue by providing a consistent ontological representation for both source code and documentation. The ontological representation unifies information from various sources, and therefore reduces the maintainers’ comprehension efforts. In addition, representing software artifacts in a formal ontology enables maintainers to formulate hypotheses about various properties of software systems. These hypotheses can be validated through an iterative exploration of information derived by our ontology inference engine. The implementation of our approach is presented in detail, and a case study is provided to demonstrate the applicability of our approach during the architectural evolution of a website content management system.

Keywords: Program Comprehension, Software Evolution, Ontology, Automated Reasoning

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.

Tutorial Description

Do you have a lack of information? Or do you rather feel overwhelmed by the sheer amount of (online) available content, like emails, news, web pages, and electronic documents? The rather young field of Text Mining developed from the observation that most knowledge today - more than 80% of the data stored in databases - is hidden within documents written in natural languages and thus cannot be automatically processed by traditional information systems.

Text Mining, "also known as intelligent text analysis, text data mining or knowledge-discovery in text (KDT), refers generally to the process of extracting interesting and non-trivial information and knowledge from unstructured text." Text Mining is a highly interdisciplinary field, drawing on foundations and technologies from fields like computational linguistics, database systems, and artificial intelligence, but applying these in new and often unconventional ways.

(This webpage is about a technical report on Text Mining, written in German. Try Google Translate for an English version.)

Interner Bericht 2006-5, Fakultät für Informatik, Universität Karlsruhe (TH), Germany

Herausgegeben von René Witte und Jutta Mülle

ISSN 1432-7864

200 Seiten, 75 Abbildungen

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.

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

Abstract

The biomedical literature is growing at an ever-increasing rate, which pronounces the need to support scientists with advanced, automated means of accessing knowledge. We investigate a novel approach employing description logics (DL)-based queries made to formal ontologies that have been created using the results of text mining full-text research papers. In this paradigm, an OWL-DL ontology becomes populated with instances detected through natural language processing (NLP). The generated ontology can be queried by biologists using DL reasoners or integrated into bioinformatics workflows for further automated analyses. We demonstrate the feasibility of this approach with a system targeting the protein mutation literature.

Keywords: text mining; semantic web; ontological NLP; protein mutations; automated reasoning in bioinformatics; querying OWL-DL ontologies; description logics.

Abstract

Software maintainers routinely have to deal with a multitude of artifacts, like source code or documents. These artifacts often end up disconnected from each other, due to their different representations and levels of abstractions. One of the main challenges in software maintenance therefore is to recover and maintain the semantic connections among these artifacts. In this research, we present a novel approach that addresses this traceability issue by creating formal ontological representations for both software documentation and source code artifacts. The resulting representations are then aligned to establish traceability links at semantic level. Ontological queries and reasoning can be applied on these representations to infer and establish additional traceability links to support specific maintenance tasks.

Categories and Subject Descriptors: D2.7 [Distribution, Maintenance, and Enhancement]: Documentation, Restructuring, reverse engineering
General Terms: Software, Documentation, Management
Keywords: Ontologies, Traceability, Software Maintenance