Ontology evaluation using wikipedia categories for browsing

Yu, Jonathan; Thom, James A.; Tam, Audrey

doi:10.1145/1321440.1321474

Cited by 73 publications

(66 citation statements)

References 15 publications

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“…Task-based evaluations (Pratt et al 1999;Chen et al 1999;Hearst 2006b;Yu et al 2007;Wang et al 2014) provide more information on how hierarchies and their navigation structures would perform in practice and overall there is a clear indication that systems that provide hierarchical navigation and query support outperform systems that do not. As these task-based evaluations tend to follow the "simulated work task" approach proposed by Borlund and Ingwersen (1997) their results also provide a strong indication as to how the hierarchies would perform in practice.…”

Section: Evaluating Hierarchiesmentioning

confidence: 99%

Evaluating hierarchical organisation structures for exploring digital libraries

et al. 2014

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Search boxes providing simple keyword-based search are insufficient when users have complex information needs or are unfamiliar with a collection, for example in large digital libraries. Browsing hierarchies can support these richer interactions, but many collections do not have a suitable hierarchy available. In this paper we present a number of approaches for automatically creating hierarchies and mapping items into them, including a novel technique which automatically adapts a Wikipedia-based taxonomy to the target collection. These approaches are applied to a large collection of cultural heritage items which is formed through the aggregation of other collections and for which no unified hierarchy is available. We investigate a number of novel user-evaluated metrics to quantify the hierarchies' quality and performance, showing that the proposed technique is preferred by users. From this we draw a number of conclusions as to what makes a hierarchy useful to the user.

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Section: Evaluating Hierarchiesmentioning

confidence: 99%

Evaluating hierarchical organisation structures for exploring digital libraries

et al. 2014

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“…However, Yu et al [37] clearly demonstrate that thesauri with multiple paths to documents perform better in exploration tasks, as they increase the likelihood of the user finding any one path to the documents they are interested in. Thus work is currently ongoing to extend the core spatialisation algorithm to work with thesauri that are directed acyclical graphs.…”

Section: Pre-processingmentioning

confidence: 99%

Exploring Large Digital Library Collections Using a Map-Based Visualisation

Hall

Clough

2013

Research and Advanced Technology for Digital Libraries

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Abstract. In this paper we describe a novel approach for exploring large document collections using a map-based visualisation. We use hierarchically structured semantic concepts that are attached to the documents to create a visualisation of the semantic space that resembles a Google Map. The approach is novel in that we exploit the hierarchical structure to enable the approach to scale to large document collections and to create a map where the higher levels of spatial abstraction have semantic meaning. An informal evaluation is carried out to gather subjective feedback from users. Overall results are positive with users finding the visualisation enticing and easy to use.

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“…For example, Muchnik et al (Muchnik et al, 2007) studied how to automatically discover hierarchies in Wikipedia, Yu et al (Yu et al, 2007) studied how to evaluate ontology with Wikipedia categories, and Silva et al (Silva et al, 2010) studied how to identify borders of certain knowledge networks.…”

Section: Related Workmentioning

confidence: 99%

WIKIPEDIA AS DOMAIN KNOWLEDGE NETWORKS - Domain Extraction and Statistical Measurement

Zheng

Wang

Liu

et al. 2011

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

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Abstract:This paper investigates knowledge networks of specific domains extracted from Wikipedia and performs statistical measurements to selected domains. In particular, we first present an efficient method to extract a specific domain knowledge network from Wikipedia. We then extract four domain networks on, respectively, mathematics, physics, biology, and chemistry. We compare the mathematics domain network extracted from Wikipedia with MathWorld, the web's most extensive mathematical resource created and maintained by professional mathematicians, and show that they are statistically similar to each other. This indicates that MathWorld and Wikipedia's mathematics domain knowledge share a similar internal structure. Such information may be useful for investigating knowledge networks.

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Ontology evaluation using wikipedia categories for browsing

Cited by 73 publications

References 15 publications

Evaluating hierarchical organisation structures for exploring digital libraries

Evaluating hierarchical organisation structures for exploring digital libraries

Exploring Large Digital Library Collections Using a Map-Based Visualisation

WIKIPEDIA AS DOMAIN KNOWLEDGE NETWORKS - Domain Extraction and Statistical Measurement

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