Search (4 results, page 1 of 1)

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Abstract

Thesauri are used to provide controlled vocabularies for resource classification. Their use can greatly assist document discovery because thesauri man date a consistent shared terminology for describing documents. A particular thesauras classifies documents according to an information community's needs. As a result, there are many different thesaural schemas. This has led to a proliferation of schema-specific thesaural systems. In our research, we exploit schematic regularities to design a generic thesaural ontology and specfiy it as a markup language. The language provides a common representational framework in which to encode the idiosyncrasies of specific thesauri. This approach has several advantages: it offers consistent syntax and semantics in which to express thesauri; it allows general purpose thesaural applications to leverage many thesauri; and it supports a single thesaural user interface by which information communities can consistently organise, score and retrieve electronic documents.

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Abstract

To understand information accessibility issues, research has examined human and technical factors by taking a socio-technical view. While this view provides a profound understanding of how people seek, use, and access information, it often overlooks the larger structure of the information landscapes that shape people's information access. However, theorizing the information landscape of a local community at the community level is challenging because of the diverse contexts and users. One way to minimize the complexity is to focus on the materiality of information. By highlighting the material aspects of information, it becomes possible to understand the community-level structure of local information. This paper develops a theory of local information landscapes (LIL theory) to conceptualize the material structure of local information. LIL theory adapts a concept of the virtual as an ontological view of the local information that is embedded in technical infrastructures, spaces, and people. By complementing existing theories, this paper provides a new perspective on how information deserts manifest as a material pre-condition of information inequality. Based on these theoretical models, a research agenda is presented for future studies of local communities.

Type

a

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Abstract

Exploratory browsing is potentially an important technique for retrieving text information from large knowledge bases. However, the task of information seeking is complex and it is easy to get lost in a composite network of nodes representing concepts. In this paper, we show how an exploring capability can be supported by ontology-based knowledge. The disadvantages mentioned above are avoided by providing facilities for guiding users' exploratory tasks particularly if they are not experts in the domain. We offer a way for information seekers or explorers to pursue information objects relevant to their tasks and apply task action steps to achieve their goals and intentions. Three types of ontology-based models were proposed that structure and organise information to support information access. The retrieved information is designed for navigating users' information seeking, but not restricting users' options. In addition, we discuss the maintenance of users' task actions to support information re-use purposes

Type

a

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Abstract

In this document we describe the final release of the toolset for entity and semantic associations, integrating two versions (language dependent and language independent) of Unsupervised Document Similarity implemented by MU (using gensim tool) and Citation Indexing, Resolution and Matching (UJF/CMD). We give a brief description of tools, the rationale behind decisions made, and provide elementary evaluation. Tools are integrated in the main project result, EuDML website, and they deliver the needed functionality for exploratory searching and browsing the collected documents. EuDML users and content providers thus benefit from millions of algorithmically generated similarity and citation links, developed using state of the art machine learning and matching methods.