Semantic Model Alignment for Business Process Integration

Business process models describe an enterprise’s way of conducting business and in this form the basis for shaping the organization and engineering the appropriate supporting or even enabling IT. Thereby, a major task in working with models is their analysis and comparison for the purpose of aligning them. As models can differ semantically not only concerning the modeling languages used, but even more so in the way in which the natural language for labeling the model elements has been applied, the correct identification of the intended meaning of a legacy model is a non-trivial task that thus far has only been solved by humans. In particular at the time of reorganizations, the set-up of B2B-collaborations or mergers and acquisitions the semantic analysis of models of different origin that need to be consolidated is a manual effort that is not only tedious and error-prone but also time consuming and costly and often even repetitive. For facilitating automation of this task by means of IT, in this thesis the new method of Semantic Model Alignment is presented. Its application enables to extract and formalize the semantics of models for relating them based on the modeling language used and determining similarities based on the natural language used in model element labels. The resulting alignment supports model-based semantic business process integration. The research conducted is based on a design-science oriented approach and the method developed has been created together with all its enabling artifacts. These results have been published as the research progressed and are presented here in this thesis based on a selection of peer reviewed publications comprehensively describing the various aspects

Ontologies in the Time of Linked Data

This paper discusses some of the methodological issues one encounters when creating and using ontologies in the rapidly expanding Linked Open Data (LOD) landscape. Over the years the notion of applied ontologies has transitioned from that of a logically formalized knowledge system with varying degrees of inferencing power to that of a lightweight knowledge representation tool. This shift is reflected in the current lexicon where different actors in the LOD community use the term ontology interchangeably with more generic terms like vocabulary or even namespace or data schema. Applied ontologies have been a key area of research in the context of Semantic Web initiative since the late 1990s. The Semantic Web has recently found a new stream of development in the Linked Data initiative, which is considered its natural evolution (Allemang and Hendler, 2011). While a good deal of literature has been devoted to investigating ontology engineering for the Semantic Web, not enough attention has yet been paid to understanding the nature and role that ontologies play in the linked data context, especially from the lens of knowledge organization research. Based on our ongoing work creating Linked Open Data applications and services for digital resources in the domain of the performing arts, we describe methodological steps and lessons learned in line with the spirit of the linked data initiative, where an agile and pragmatic approach to development is combined with the practice of learning from one another

Conceptual Modeling of Collaborative Intelligent Manufacturing For Customized Products: An Ontological Approach

Mass customization is implemented to provide outstanding service to customers with diverse tastes and preferences. However, mass customization has limitations in the traditional value chain and production paradigm. Taking advantage of advanced information technology, such as manufacturing grid and virtual enterprise, to facilitate mass customization and improve the customer perceived valued of mass customization raises a challenge issue. To achieve an ideal mass customization, the customer\u27s needs should be identified and met by the collaborative manufacturing from several manufacturers. Comprehensive conceptual models corresponding to the collaborative manufacturing for customized products are essential to understand how the collaborative process can apply in customized production, and facilitate early detection and correction of system development errors. In this paper, an ontology is described via a customized bicycle buying scenario to describe how to use an ontology for collaborative manufacturing. This ontological approach provides understanding of the domain, which can be used as a unifying framework to represent the selected phenomena for conceptual model

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

Knowledge Organization Systems (KOS) in the Semantic Web: A Multi-Dimensional Review

Since the Simple Knowledge Organization System (SKOS) specification and its SKOS eXtension for Labels (SKOS-XL) became formal W3C recommendations in 2009 a significant number of conventional knowledge organization systems (KOS) (including thesauri, classification schemes, name authorities, and lists of codes and terms, produced before the arrival of the ontology-wave) have made their journeys to join the Semantic Web mainstream. This paper uses "LOD KOS" as an umbrella term to refer to all of the value vocabularies and lightweight ontologies within the Semantic Web framework. The paper provides an overview of what the LOD KOS movement has brought to various communities and users. These are not limited to the colonies of the value vocabulary constructors and providers, nor the catalogers and indexers who have a long history of applying the vocabularies to their products. The LOD dataset producers and LOD service providers, the information architects and interface designers, and researchers in sciences and humanities, are also direct beneficiaries of LOD KOS. The paper examines a set of the collected cases (experimental or in real applications) and aims to find the usages of LOD KOS in order to share the practices and ideas among communities and users. Through the viewpoints of a number of different user groups, the functions of LOD KOS are examined from multiple dimensions. This paper focuses on the LOD dataset producers, vocabulary producers, and researchers (as end-users of KOS).Comment: 31 pages, 12 figures, accepted paper in International Journal on Digital Librarie

Mapping subjectivity: performing people-centered vocabulary alignment

This paper describes a mapping of linked data vocabularies in the area of person-related information. Aligning vocabulary terms may help curb the problem of property proliferation that occurs in linked data environments. It also facilitates the process of choosing semantics for vocabulary extensions and integration in the context of linked data applications. Although a work in progress, this investigation would provide support for semantic integration and for knowledge sharing and reuse in the area of personal information representation. It also offers an opportunity to reflect on a new generation of knowledge organization systems such as linked data vocabularies that have started to populate the web and are converging with new representation models and discovery tools in libraries and other cultural heritage institutions

Accomplishments and challenges of protein ontology

Recent progress in proteomics, computational biology, and ontology development has presented an opportunity to investigate protein data sources from unique perspective that is, examining protein data sources through structure and hierarchy of Protein Ontology (PO). Various data mining algorithms and mathematical models provide methods for analysing protein data sources; however, there are two issues that need to be addressed: (1) the need for standards for defining protein data description and exchange and (2) eliminating errors which arise with the data integration methodologies for complex queries. Protein Ontology is designed to meet these needs by providing a structured protein data specification for Protein Data Representation. Protein Ontology is standard for representing protein data in a way that helps in defining data integration and data mining models for Protein Structure and Function. We report here our development of PO; a semantic heterogeneity framework based on relationships between PO concepts; and analysis of resultant PO Data of Human Proteins. We also talk in this paper briefly about our ongoing work of designing a trustworthy framework around PO