3,691 research outputs found
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
Preliminary results in tag disambiguation using DBpedia
The availability of tag-based user-generated content for a variety of Web resources (music, photos, videos, text, etc.) has largely increased in the last years. Users can assign tags freely and then use them to share and retrieve information. However, tag-based sharing and retrieval is not optimal due to the fact that tags are plain text labels without an explicit or formal meaning, and hence polysemy and synonymy should be dealt with appropriately. To ameliorate these problems, we propose a context-based tag disambiguation algorithm that selects the meaning of a tag among a set of candidate DBpedia entries, using a common information retrieval similarity measure. The most similar DBpedia en-try is selected as the one representing the meaning of the tag. We describe and analyze some preliminary results, and discuss about current challenges in this area
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