A Two-Phased Ontology Selection Approach for Semantic Web

Hong, Tzung-Pei; Chang, Wen-Chang; Lin, Jiann-Horng

doi:10.1007/11554028_56

Cited by 3 publications

(4 citation statements)

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“…The most common issues addressed by these systems are Ontology Selection -how to identify/select automatically the set of relevant ontologies from a given collection [26,27] -and Ontology Evaluation -how to assess the quality and relevance of an ontology [14,27]. Several studies have contributed to the solution of both the above problems, including approaches to ranking ontologies [18] and to select appropriate ontologies [22,29,31]. These works focus on the mechanisms required to support SWSE systems in automatically identifying ontologies from their collections and presenting them in a ranked list to the users.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, it also requires manual analyses of the content of candidate ontologies to choose the ones that are adequate to their intended use. For these reasons, the automatic Ontology Selection process has been studied in several different contexts in the recent years [4,5,14,18,22,26,27,29,31] with the aim of improving the methods used to collect, assess and rank candidate ontologies. However, the more user-centric Ontology Search process, here defined as the activity of browsing the results from a SWSE to identify the ontologies adequate to the search goal, has not been researched extensively until now.…”

Section: Introductionmentioning

confidence: 99%

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Impact of Using Relationships between Ontologies to Enhance the Ontology Search Results

Allocca

d’Aquin

Motta

2012

Lecture Notes in Computer Science

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Using semantic web search engines, such as Watson, Swoogle or Sindice, to find ontologies is a complex exploratory activity. It generally requires formulating multiple queries, browsing pages of results, and assessing the returned ontologies against each other to obtain a relevant and adequate subset of ontologies for the intended use. Our hypothesis is that at least some of the difficulties related to searching ontologies stem from the lack of structure in the search results, where ontologies that are implicitly related to each other are presented as disconnected and shown on different result pages. In earlier publications we presented a software framework, Kannel, which is able to automatically detect and make explicit relationships between ontologies in large ontology repositories. In this paper, we present a study that compares the use of the Watson ontology search engine with an extension, Watson+Kannel, which provides information regarding the various relationships occurring between the result ontologies. We evaluate Watson+Kannel by demonstrating through various indicators that explicit relationships between ontologies improve users' efficiency in ontology search, thus validating our hypothesis.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of Using Relationships between Ontologies to Enhance the Ontology Search Results

Allocca

d’Aquin

Motta

2012

Lecture Notes in Computer Science

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“…For examples, [30,20,31] are non-automatic method in which users are involved in the recommendation process. In OntoKhoj the user is requested for the disambiguation process.…”

Section: Related Workmentioning

confidence: 99%

“…• Automation : Is the tool or the method fully automatic or does it requires an interaction with the user for whom a recommendation is necessary? For examples, [ 30 , 20 , 31 ] are non-automatic method in which users are involved in the recommendation process. In OntoKhoj the user is requested for the disambiguation process.…”

Section: Related Workmentioning

confidence: 99%

Building a biomedical ontology recommender web service

Jonquet

Musen

Shah

2010

Journal of Biomedical Semantics

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BackgroundResearchers in biomedical informatics use ontologies and terminologies to annotate their data in order to facilitate data integration and translational discoveries. As the use of ontologies for annotation of biomedical datasets has risen, a common challenge is to identify ontologies that are best suited to annotating specific datasets. The number and variety of biomedical ontologies is large, and it is cumbersome for a researcher to figure out which ontology to use.MethodsWe present the Biomedical Ontology Recommender web service. The system uses textual metadata or a set of keywords describing a domain of interest and suggests appropriate ontologies for annotating or representing the data. The service makes a decision based on three criteria. The first one is coverage, or the ontologies that provide most terms covering the input text. The second is connectivity, or the ontologies that are most often mapped to by other ontologies. The final criterion is size, or the number of concepts in the ontologies. The service scores the ontologies as a function of scores of the annotations created using the National Center for Biomedical Ontology (NCBO) Annotator web service. We used all the ontologies from the UMLS Metathesaurus and the NCBO BioPortal.ResultsWe compare and contrast our Recommender by an exhaustive functional comparison to previously published efforts. We evaluate and discuss the results of several recommendation heuristics in the context of three real world use cases. The best recommendations heuristics, rated ‘very relevant’ by expert evaluators, are the ones based on coverage and connectivity criteria. The Recommender service (alpha version) is available to the community and is embedded into BioPortal.

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