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Techn. Universiẗat, Diss., 2005--Berlin.

The success of semantic systems has been proven over the last years. Nowadays, Linked Data is the driver for the rapid development of ever new intelligent systems. Especially in enterprise environments semantic systems successfully support more and more business processes. This is especially true for after sales service in the mechanical engineering domain. Here, service technicians need effective access to relevant technical documentation in order to diagnose and solve problems and defects. Therefore, the usage of semantic information retrieval systems has become the new system metaphor. Unlike classical retrieval software Linked Enterprise Data graphs are exploited to grant targeted and problem-oriented access to relevant documents. However, huge parts of legacy technical documents have not yet been integrated into Linked Enterprise Data graphs. Additionally, a plethora of information models for the semantic representation of technical information exists. The semantic maturity of these information models can hardly be measured. This thesis motivates that there is an inherent need for a self-contained semantification approach for technical documents. This work introduces a maturity model that allows to quickly assess existing documentation. Additionally, the approach comprises an abstracting semantic representation for technical documents that is aligned to all major standard information models. The semantic representation combines structural and rhetorical aspects to provide access to so called Core Documentation Entities. A novel and holistic semantification process describes how technical documents in different legacy formats can be transformed to a semantic and linked representation. The practical significance of the semantification approach depends on tools supporting its application. This work presents an accompanying tool chain of semantification applications, especially the semantification framework CAPLAN that is a highly integrated development and runtime environment for semantification processes. The complete semantification approach is evaluated in four real-life projects: in a spare part augmentation project, semantification projects for earth moving technology and harvesting technology, as well as an ontology population project for special purpose vehicles. Three additional case studies underline the broad applicability of the presented ideas. / Semantische Systeme haben in den letzten Jahren ihren Erfolg bewiesen. Linked Data ist heute der Treiber für die rasante Entwicklung immer neuer intelligenter Systeme. Insbesondere in Unternehmensumgebungen unterstützen semantische Systeme erfolgreich immer mehr Geschäftsprozesse. Dies gilt insbesondere für den After-Sales-Service im Maschinenbau. Hier benötigen Servicetechniker einen effektiven Zugang zu relevanter technischer Dokumentation, um Probleme und Defekte effizient zu diagnostizieren und zu lösen. Daher ist die Verwendung semantischer Information Retrieval Systeme zur neuen Systemmetapher geworden. Im Gegensatz zur klassischen Retrieval-Software werden Linked Enterprise Data Graphen genutzt, um einen gezielten und problemorientierten Zugriff auf relevante Dokumente zu ermöglichen. Große Teile der alten technischen Dokumente wurden jedoch noch nicht mit Enterprise Linked Data Graphen verknüpft. Darüber hinaus gibt es eine Vielzahl von unterschiedlichen Informationsmodellen zur semantischen Repräsentation technischer Informationen. Der semantische Reifegrad dieser Informationsmodelle war bisher kaum messbar. Diese Arbeit zeigt, dass es einen inhärenten Bedarf an einem in sich geschlossenen Semantifizierungs-Ansatz für technische Dokumente gibt. Diese Arbeit stellt ein semantisches Reifegradmodell vor, das es ermöglicht, bestehende Dokumentationen schnell zu bewerten. Darüber hinaus umfasst der Ansatz eine abstrahierende semantische Darstellung für technische Dokumente, die auf alle wichtigen Standardinformationsmodelle abgestimmt ist. Die semantische Repräsentation kombiniert strukturelle und rhetorische Aspekte, um Zugang zu so genannten Core Documentation Entities zu ermöglichen. Ein neuartiger und ganzheitlicher Semantifizierungs-Prozess beschreibt, wie technische Dokumente in verschiedenen Legacy-Formaten in eine semantische und vernetzte Repräsentation transformiert werden können. Die praktische Bedeutung des Semantifizierungsansatzes hängt von Werkzeugen ab, die seine Anwendung unterstützen. Diese Arbeit stellt eine Software-Werkzeugbox von Semantifizierungs-Anwendungen vor, insbesondere das Semantifizierungs-Framework CAPLAN, eine hochintegrierte Entwicklungs- und Laufzeitumgebung für Semantifizierungs-Prozesse. Der vollständige Semantifizierungs-Aansatz wurde in vier realen Projekten evaluiert: einem Projekt zur semantischen Anreicherung von Ersatzteilinformationen, Semantifizierungs-Projekten für Erdbewegungstechnik und Erntetechnik sowie einem Ontologie-Populationsprojekt für Spezialfahrzeuge. Drei weitere Fallstudien unterstreichen die breite Anwendbarkeit der vorgestellten Ideen.

Revised version of the author's dissertation (Tübingen, 1995). / Includes bibliographical references (p. [251]-256) and index.

The Web provides a platform for people to share their data, leading to an abundance of accessible information. In recent years, significant research effort has been directed especially at tables on the Web, which form a rich resource for factual and relational data. Applications such as fact search and knowledge base construction benefit from this data, as it is often less ambiguous than unstructured text. However, many traditional information extraction and retrieval techniques are not well suited for Web tables, as they generally do not consider the role of the table structure in reflecting the semantics of the content. Tables provide a compact representation of similarly structured data. Yet, on the Web, tables are very heterogeneous, often with ambiguous semantics and inconsistencies in the quality of the data. Consequently, recognizing the structure and inferring the semantics of these tables is a challenging task that requires a designated table recovery and understanding process. In the literature, many important contributions have been made to implement such a table understanding process that specifically targets Web tables, addressing tasks such as table detection or header recovery. However, the precision and coverage of the data extracted from Web tables is often still quite limited. Due to the complexity of Web table understanding, many techniques developed so far make simplifying assumptions about the table layout or content to limit the amount of contributing factors that must be considered. Thanks to these assumptions, many sub-tasks become manageable. However, the resulting algorithms and techniques often have a limited scope, leading to imprecise or inaccurate results when applied to tables that do not conform to these assumptions. In this thesis, our objective is to extend the Web table understanding process with techniques that enable some of these assumptions to be relaxed, thus improving the scope and accuracy. We have conducted a comprehensive analysis of tables available on the Web to examine the characteristic features of these tables, but also identify unique challenges that arise from these characteristics in the table understanding process. To extend the scope of the table understanding process, we introduce extensions to the sub-tasks of table classification and conceptualization. First, we review various table layouts and evaluate alternative approaches to incorporate layout classification into the process. Instead of assuming a single, uniform layout across all tables, recognizing different table layouts enables a wide range of tables to be analyzed in a more accurate and systematic fashion. In addition to the layout, we also consider the conceptual level. To relax the single concept assumption, which expects all attributes in a table to describe the same semantic concept, we propose a semantic normalization approach. By decomposing multi-concept tables into several single-concept tables, we further extend the range of Web tables that can be processed correctly, enabling existing techniques to be applied without significant changes. Furthermore, we address the quality of data extracted from Web tables, by studying the role of context information. Supplementary information from the context is often required to correctly understand the table content, however, the verbosity of the surrounding text can also mislead any table relevance decisions. We first propose a selection algorithm to evaluate the relevance of context information with respect to the table content in order to reduce the noise. Then, we introduce a set of extraction techniques to recover attribute-specific information from the relevant context in order to provide a richer description of the table content. With the extensions proposed in this thesis, we increase the scope and accuracy of Web table understanding, leading to a better utilization of the information contained in tables on the Web.