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

Perspectives on reusing codified project knowledge: a structured literature review

Project documentation represents a valuable source of knowledge in project-based organizations. The practical reality is, however, that the knowledge codified in project documents is hardly re-used in future projects. A central problem in this context is the extensive amount of usually textual material. As a consequence, computer-assisted processes are indispensable in order to analytically manage the constantly growing and evolving databases of available project documents. The goal of this study is to summarize the current research focusing on the computer-assisted reuse of textually codified project knowledge and to define the corresponding state-of-the-art in this this specific field of information systems research. As a result of a literature review, this study structures the body of research contributions and outlines what kinds of computer-assisted techniques are incorporated, what practical application areas these solutions address, and in what business domains they are applied. In particular, this should point out research opportunities and thereby make a contribution to the further development of knowledge management in project environments

Perspectives on reusing codified project knowledge: a structured literature review

Project documentation represents a valuable source of knowledge in project-based organizations. The practical reality is, however, that the knowledge codified in project documents is hardly re-used in future projects. A central problem in this context is the extensive amount of usually textual material. As a consequence, computer-assisted processes are indispensable in order to analytically manage the constantly growing and evolving databases of available project documents. The goal of this study is to summarize the current research focusing on the computer-assisted reuse of textually codified project knowledge and to define the corresponding state-of-the-art in this this specific field of information systems research. As a result of a literature review, this study structures the body of research contributions and outlines what kinds of computer-assisted techniques are incorporated, what practical application areas these solutions address, and in what business domains they are applied. In particular, this should point out research opportunities and thereby make a contribution to the further development of knowledge management in project environments

What to Do With All These Project Documentations? – Research Issues in Reusing Codified Project Knowledge

Project-based organizations invest a lot of time and effort into the extensive documentation of their projects. These project documents usually contain innovative knowledge and represent a significant source of information for the continual development of a learning organization. However, this codified project knowledge often remains untapped afterwards. A central problem in this context is the sheer information overload due to the often very large documentation stocks in project-based organizations. Against this background, this paper poses the following question: what can be done with the extensive project documentation after it has been created? To answer this question, two methodological approaches are combined. First, a literature review summarizes the current status quo of research in this special area. Then, expert interviews with IT project managers provide a deeper understanding of common practical problems. The combination of respective findings makes it possible to uncover research gaps and subsequently to define future needs for research. In sum, this paper formulates six research issues, which represent a starting point on the path to more comprehensive solutions for practically coping with large stocks of codified project knowledge

Developing a Computational Ontology from Mixed-Methods Research: A Workflow and Its Challenges

In this paper, we will discuss some of the challenges faced when developing a data ontology from mixed methods research. Beyond the Multiplex is a three year project that seeks to understand how to enable a wider range of audiences to participate in a more diverse film culture. A key part of the project is exploring regional patterns of film audience experience. In technical terms, this involves inductively generating an ontological data model to formally describe film audiences by drawing on primary mixed methods research. Ultimately, we are seeking to develop a relational understanding of film audiences which will culminate in a searchable triplestore database. This paper will cover the challenges of developing the data ontology. For example, how we maintained coherence across qualitative and quantitative datasets whilst incorporating taxonomies for controlling factors such as socio-cultural indicators, film type, and venue/platform. Overall, our data ontology draws on: a socio-cultural index of audience engagement with film; 200 semi-structured interviews, including 30 elite interviews with film industry and policy professionals; a longitudinal survey of 2000 respondents across three sample points; 16 focus groups using film elicitation to understand how audiences interpret specialised film and experience stories; and a discourse analysis of industry and policy documents. In combination, our mixed-methods approach generates various data types. Drawing on thematic qualitative analysis alongside cluster and latent class analyses, we are integrating our findings by drawing on literature at the intersection of audience studies and theories of cultural consumption. In this paper, we explain how social theory and data analysis combined have enabled us to iteratively generate a taxonomy. We then explain how that taxonomy has become the basis of both a triplestore database and allows an analysis of film audience engagement

Developing a Computational Ontology from Mixed-Methods Research: A Workflow and Its Challenges

In this paper, we will discuss some of the challenges faced when developing a data ontology from mixed methods research. Beyond the Multiplex is a three year project that seeks to understand how to enable a wider range of audiences to participate in a more diverse film culture. A key part of the project is exploring regional patterns of film audience experience. In technical terms, this involves inductively generating an ontological data model to formally describe film audiences by drawing on primary mixed methods research. Ultimately, we are seeking to develop a relational understanding of film audiences which will culminate in a searchable triplestore database. This paper will cover the challenges of developing the data ontology. For example, how we maintained coherence across qualitative and quantitative datasets whilst incorporating taxonomies for controlling factors such as socio-cultural indicators, film type, and venue/platform. Overall, our data ontology draws on: a socio-cultural index of audience engagement with film; 200 semi-structured interviews, including 30 elite interviews with film industry and policy professionals; a longitudinal survey of 2000 respondents across three sample points; 16 focus groups using film elicitation to understand how audiences interpret specialised film and experience stories; and a discourse analysis of industry and policy documents. In combination, our mixed-methods approach generates various data types. Drawing on thematic qualitative analysis alongside cluster and latent class analyses, we are integrating our findings by drawing on literature at the intersection of audience studies and theories of cultural consumption. In this paper, we explain how social theory and data analysis combined have enabled us to iteratively generate a taxonomy. We then explain how that taxonomy has become the basis of both a triplestore database and allows an analysis of film audience engagement

Semantic enrichment for enhancing LAM data and supporting digital humanities. Review article

With the rapid development of the digital humanities (DH) field, demands for historical and cultural heritage data have generated deep interest in the data provided by libraries, archives, and museums (LAMs). In order to enhance LAM data’s quality and discoverability while enabling a self-sustaining ecosystem, “semantic enrichment” becomes a strategy increasingly used by LAMs during recent years. This article introduces a number of semantic enrichment methods and efforts that can be applied to LAM data at various levels, aiming to support deeper and wider exploration and use of LAM data in DH research. The real cases, research projects, experiments, and pilot studies shared in this article demonstrate endless potential for LAM data, whether they are structured, semi-structured, or unstructured, regardless of what types of original artifacts carry the data. Following their roadmaps would encourage more effective initiatives and strengthen this effort to maximize LAM data’s discoverability, use- and reuse-ability, and their value in the mainstream of DH and Semantic Web