Steffen Staab

Measuring Similarity between Ontologies

Ontologies now play an important role for many knowledge-intensive applications for which they provide a source of precisely defined terms. However, with their wide-spread usage there come problems concerning their proliferation. Ontology engineers or users frequently have a core ontology that they use, e.g., for browsing or querying data, but they need to extend it with, adapt it to, or compare it with the large set of other ontologies. For the task of detecting and retrieving relevant ontologies, one needs means for measuring the similarity between ontologies. We present a set of ontology similarity measures and a multiple-phase empirical evaluation.

Knowledge processes and ontologies

In this article, we present an approach for ontology-based knowledge management (KM) that includes a tool suite and a methodology for developing ontology-based KM systems. It builds on the distinction between knowledge processes and knowledge metaprocesses, and is illustrated by CHAR (Corporate History AnalyzeR), a KM system for corporate history analysis.

QOM: quick ontology mapping

(Semi-)automatic mapping - also called (semi-) automatic alignment - of ontologies is a core task to achieve interoperability when two agents or services use different ontologies. In the existing literature, the focus has so far been on improving the quality of mapping results. We here consider QOM, Quick Ontology Mapping, as a way to trade off between effectiveness (i.e. quality) and efficiency of the mapping generation algorithms. We show that QOM has lower run-time complexity than existing prominent approaches. Then, we show in experiments that this theoretical investigation translates into practical benefits. While QOM gives up some of the possibilities for producing high-quality results in favor of efficiency, our experiments show that this loss of quality is marginal.

What Is an Ontology

The word “ontology” is used with different senses in different communities. The most radical difference is perhaps between the philosophical sense, which has of course a well-established tradition, and the computational sense, which emerged in the recent years in the knowledge engineering community, starting from an early informal definition of (computational) ontologies as “explicit specifications of conceptualizations”. In this paper we shall revisit the previous attempts to clarify and formalize such original definition, providing a detailed account of the notions of conceptualization and explicit specification, while discussing at the same time the importance of shared explicit specifications.

Towards the self-annotating web

The success of the Semantic Web depends on the availability of ontologies as well as on the proliferation of web pages annotated with metadata conforming to these ontologies. Thus, a crucial question is where to acquire these metadata from. In this paper wepropose PANKOW (Pattern-based Annotation through Knowledge on theWeb), a method which employs an unsupervised, pattern-based approach to categorize instances with regard to an ontology. The approach is evaluated against the manual annotations of two human subjects. The approach is implemented in OntoMat, an annotation tool for the Semantic Web and shows very promising results.

The Semantic Web - ISWC 2008

Research Track.- Involving Domain Experts in Authoring OWL Ontologies.- Supporting Collaborative Ontology Development in Protege.- Identifying Potentially Important Concepts and Relations in an Ontology.- RoundTrip Ontology Authoring.- nSPARQL: A Navigational Language for RDF.- An Experimental Comparison of RDF Data Management Approaches in a SPARQL Benchmark Scenario.- Anytime Query Answering in RDF through Evolutionary Algorithms.- The Expressive Power of SPARQL.- Integrating Object-Oriented and Ontological Representations: A Case Study in Java and OWL.- Extracting Semantic Constraint from Description Text for Semantic Web Service Discovery.- Enhancing Semantic Web Services with Inheritance.- Using Semantic Distances for Reasoning with Inconsistent Ontologies.- Statistical Learning for Inductive Query Answering on OWL Ontologies.- Optimization and Evaluation of Reasoning in Probabilistic Description Logic: Towards a Systematic Approach.- Modeling Documents by Combining Semantic Concepts with Unsupervised Statistical Learning.- Comparison between Ontology Distances (Preliminary Results).- Folksonomy-Based Collabulary Learning.- Combining a DL Reasoner and a Rule Engine for Improving Entailment-Based OWL Reasoning.- Improving an RCC-Derived Geospatial Approximation by OWL Axioms.- OWL Datatypes: Design and Implementation.- Laconic and Precise Justifications in OWL.- Learning Concept Mappings from Instance Similarity.- Instanced-Based Mapping between Thesauri and Folksonomies.- Collecting Community-Based Mappings in an Ontology Repository.- Algebras of Ontology Alignment Relations.- Scalable Grounded Conjunctive Query Evaluation over Large and Expressive Knowledge Bases.- A Kernel Revision Operator for Terminologies - Algorithms and Evaluation.- Description Logic Reasoning with Decision Diagrams.- RDF123: From Spreadsheets to RDF.- Evaluating Long-Term Use of the Gnowsis Semantic Desktop for PIM.- Bringing the IPTC News Architecture into the Semantic Web.- RDFS Reasoning and Query Answering on Top of DHTs.- An Interface-Based Ontology Modularization Framework for Knowledge Encapsulation.- On the Semantics of Trust and Caching in the Semantic Web.- Semantic Web Service Choreography: Contracting and Enactment.- Formal Model for Semantic-Driven Service Execution.- Efficient Semantic Web Service Discovery in Centralized and P2P Environments.- Exploring Semantic Social Networks Using Virtual Reality.- Semantic Grounding of Tag Relatedness in Social Bookmarking Systems.- Semantic Modelling of User Interests Based on Cross-Folksonomy Analysis.- ELP: Tractable Rules for OWL 2.- Term Dependence on the Semantic Web.- Semantic Relatedness Measure Using Object Properties in an Ontology.- Semantic Web in Use Track.- Thesaurus-Based Search in Large Heterogeneous Collections.- Deploying Semantic Web Technologies for Work Integrated Learning in Industry - A Comparison: SME vs. Large Sized Company.- Creating and Using Organisational Semantic Webs in Large Networked Organisations.- An Architecture for Semantic Navigation and Reasoning with Patient Data - Experiences of the Health-e-Child Project.- Requirements Analysis Tool: A Tool for Automatically Analyzing Software Requirements Documents.- OntoNaviERP: Ontology-Supported Navigation in ERP Software Documentation.- Market Blended Insight: Modeling Propensity to Buy with the Semantic Web.- DogOnt - Ontology Modeling for Intelligent Domotic Environments.- Introducing IYOUIT.- A Semantic Data Grid for Satellite Mission Quality Analysis.- A Process Catalog for Workflow Generation.- Inference Web in Action: Lightweight Use of the Proof Markup Language.- Supporting Ontology-Based Dynamic Property and Classification in WebSphere Metadata Server.- Towards a Multimedia Content Marketplace Implementation Based on Triplespaces.- Doctoral Consortium Track.- Semantic Enrichment of Folksonomy Tagspaces.- Contracting and Copyright Issues for Composite Semantic Services.- Parallel Computation Techniques for Ontology Reasoning.- Towards Semantic Mapping for Casual Web Users.- Interactive Exploration of Heterogeneous Cultural Heritage Collections.- End-User Assisted Ontology Evolution in Uncertain Domains.- Learning Methods in Multi-grained Query Answering.

OntoEdit: Collaborative Ontology Development for the Semantic Web

Ontologies now play an important role for enabling the semantic web. They provide a source of precisely defined terms e.g. for knowledge-intensive applications. The terms are used for concise communication across people and applications. Typically the development of ontologies involves collaborative efforts of multiple persons. OntoEdit is an ontology editor that integrates numerous aspects of ontology engineering. This paper focuses on collaborative development of ontologies with OntoEdit which is guided by a comprehensive methodology.

S-CREAM - Semi-automatic CREAtion of Metadata

Richly interlinked, machine-understandable data constitute the basis for the Semantic Web. We provide a framework, S-CREAM, that allows for creation of metadata and is trainable for a specific domain. Annotating web documents is one of the major techniques for creating metadata on the web. The implementation of S-CREAM, OntoMat-Annotizer supports now the semi-automatic annotation of web pages. This semi-automatic annotation is based on the information extraction component Amilcare. OntoMat-Annotizer extract with the help of Amilcare knowledge structure from web pages through the use of knowledge extraction rules. These rules are the result of a learning-cycle based on already annotated pages.

Social networks applied

Social networks have interesting properties. They influence our lives enormously without us being aware of the implications they raise. The authors investigate the following areas concerning social networks: how to exploit our unprecedented wealth of data and how we can mine social networks for purposes such as marketing campaigns; social networks as a particular form of influence, i.e.., the way that people agree on terminology and this phenomenons implications for the way we build ontologies and the Semantic Web; social networks as something we can discover from data; the use of social network information to offer a wealth of new applications such as better recommendations for restaurants, trustworthy email senders, or (maybe) blind dates; investigation of the richness and difficulty of harvesting FOAF (friend-of-a-friend) information; and by looking at how information processing is bound to social context, the resulting ways that network topologys definition determines its outcomes.

Ontologies improve text document clustering

Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large sets of documents into a small number of meaningful clusters. The bag of words representation used for these clustering methods is often unsatisfactory as it ignores relationships between important terms that do not cooccur literally. In order to deal with the problem, we integrate core ontologies as background knowledge into the process of clustering text documents. Our experimental evaluations compare clustering techniques based on pre-categorizations of texts from Reuters newsfeeds and on a smaller domain of an eLearning course about Java. In the experiments, improvements of results by background knowledge compared to a baseline without background knowledge can be shown in many interesting combinations.