A Web-Based Repository Service for Vocabularies and Alignments in the Cultural Heritage Domain

Meij, Leander van der; Isaac, Antoine; Zinn, Claus

doi:10.1007/978-3-642-13486-9_27

Cited by 11 publications

(7 citation statements)

References 12 publications

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“…The shift from printed books to digital tools for the management and use of controlled vocabularies already lead in the 1990s to a considerable body of research regarding automated and semi-automated methods for achieving interoperability between vocabularies (Doerr, 2001;Tudhope et al, 2011;van der Meij et al, 2010;van Erp et al, 2011). Isaac et al (2008) identified four general approaches towards vocabulary reconciliation or alignment: 1) lexical alignment techniques, 2) structural alignment, 3) extensional alignment, and 4) alignment using background knowledge.…”

Section: Linked Data and The Potential Of Entity Extraction For The Dmentioning

confidence: 99%

Exploring entity recognition and disambiguation for cultural heritage collections

Hooland¹,

Wilde²,

Verborgh³

et al. 2013

Digital Scholarship in the Humanities

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Unstructured metadata fields such as 'description' offer tremendous value for users to understand cultural heritage objects. However, this type of narrative information is of little direct use within a machine-readable context due to its unstructured nature. This paper explores the possibilities and limitations of Named-Entity Recognition (NER) and Term Extraction (TE) to mine such unstructured metadata for meaningful concepts. These concepts can be used to leverage otherwise limited searching and browsing operations, but they can also play an important role to foster Digital Humanities research. In order to catalyze experimentation with NER and TE, the paper proposes an evaluation of the performance of three third-party entity extraction services through a comprehensive case study, based on the descriptive fields of the Smithsonian Cooper-Hewitt National Design Museum in New York. In order to cover both NER and TE, we first offer a quantitative analysis of named-entities retrieved by the services in terms of precision and recall compared to a manually annotated gold-standard corpus, then complement this approach with a more qualitative assessment of relevant terms extracted. Based on the outcomes of this double analysis, the conclusions present the added value of entity extraction services, but also indicate the dangers of uncritically using NER and/or TE, and by extension Linked Data principles, within the Digital Humanities. All metadata and tools used within the paper are freely available, making it possible for researchers and practitioners to repeat the methodology. By doing so, the paper offers a significant contribution towards understanding the value of entity recognition and disambiguation for the Digital Humanities.

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Section: Linked Data and The Potential Of Entity Extraction For The Dmentioning

confidence: 99%

Exploring entity recognition and disambiguation for cultural heritage collections

Hooland¹,

Wilde²,

Verborgh³

et al. 2013

Digital Scholarship in the Humanities

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“…For sharing and evaluating alignments, FOAM was relying on the Alignment API. Recently, [19] has taken inspiration from the Alignment API in order to share alignments between terminologies.…”

Section: Related Systemsmentioning

confidence: 99%

The Alignment API 4.0

Jérôme¹,

Euzenat²,

Scharffe³

et al. 2011

Semantic Web

143

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Alignments represent correspondences between entities of two ontologies. They are produced from the ontologies by ontology matchers. In order for matchers to exchange alignments and for applications to manipulate matchers and alignments, a minimal agreement is necessary. The Alignment API provides abstractions for the notions of network of ontologies, alignments and correspondences as well as building blocks for manipulating them, such as matchers, evaluators, renderers and parsers. We recall the building blocks of this API and present here the version 4 of the Alignment API through some of its new features: ontology proxys, the expressive alignment language EDOAL and evaluation primitives.

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“…There are some methodological guidelines proposed for ontology matching [15], but those guidelines do not treat the ontology mapping process as a collaborative undertaking and only focuses on the creation and management of mappings; the guidelines do not take into account activities such as feasibility studies when the need for creating an ontology mapping arises. As for the state of the art, throughout sections 1 to 3 we have stated that most methods either do not take into account collaboration or focus only on a few of the activities (e.g., discovery and sharing [11]). Collaboration has been studied in, for instance, in [16] the focus was in circulating the correspondences between communities.…”

Section: Related Workmentioning

confidence: 99%

“…Another work done to find alignment and mappings between vocabularies cultural heritage collections through a web based repository service [11]. While designing the repository service, it was taking into consideration the need for vocabulary experts in the mapping activities.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Towards an Adaptive Tool and Method for Collaborative Ontology Mapping

Shosha

Debruyne

O’Sullivan

2015

On the Move to Meaningful Internet Systems: OTM 2015 Workshops

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Abstract. Linked Data makes available a vast amount of data on the Semantic Web for agents, both human and software, to consume. Linked Data datasets are made available with different ontologies, even when their domains overlap. The interoperability problem that rises when one needs to consume and combine two or more of such datasets to develop a Linked Data application or mashup is still an important challenge. Ontology-matching techniques help overcome this problem. The process, however, often relies on knowledge engineers to carry out the tasks as they have expertise in ontologies and semantic technologies. It is reasonable to assume that knowledge engineers should require help from the domain experts, end users, etc. to contribute in the validation of the results and help distilling ontology mappings from these correspondences. However, the current design for the ontology-mapping tools does not take into consideration the different types of users expected to be involved in the creation of Linked Data applications or mashups. In this paper, we identify the different users and their roles in the mapping involved in the context of developing Linked Data mashups and propose a collaborative mapping method in which we prescribe where collaboration between the different stakeholders could, and should, take place. In addition, we propose a tool architecture based on bringing together an adaptive interface, mapping services, workflow services and agreement services that will ease the collaboration between the different stakeholders. This output will be used in an ongoing study to constructing a collaborative mapping platform.

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A Web-Based Repository Service for Vocabularies and Alignments in the Cultural Heritage Domain

Cited by 11 publications

References 12 publications

Exploring entity recognition and disambiguation for cultural heritage collections

Exploring entity recognition and disambiguation for cultural heritage collections

The Alignment API 4.0

Towards an Adaptive Tool and Method for Collaborative Ontology Mapping

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