Generating Ontologies Via Language Components and Ontology Reuse

Ding, Yihong; Lonsdale, Deryle; Embley, David W.; Hepp, Martin; Xu, Li

doi:10.1007/978-3-540-73351-5_12

Cited by 15 publications

(15 citation statements)

References 6 publications

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“…For instance, the designers of OntoMapology utilized a number of string techniques like Jaro, Jaro-Winkler, TF-IDF, and Monge Elkan and found that JaroWinkler is having the most effective execution [7], and the engineers of SAMBO, used for biomedical ontologies, discovered that a weighted sum of these string metrics that are n-gram, alter separate, and a mysterious set metric worked better than any of those measurements alone [8]. Likewise, the X-SOM developers noticed that the ideal grouping of similarity metrics does not change in light of the domain of the ontologies rather than taking into account their design characteristics [9].…”

Section: Related Workmentioning

confidence: 99%

Evaluation of String based Ontology Alignment Technique

Sohail¹,

Waqar²,

Khan³

2018

IJCA

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To connect the semantic space between various related ontologies, computer scientists and specialists in the field of Semantic Web have suggested and executed different ontology matching systems. In any case, these strategies have a great space for improvement and continuous research should be done to get better results in terms of precision, recall and F-measure for the purpose of aligning ontologies. This work analysed various string similarity metrics for the task of ontology alignment whose definitions are taken from two libraries that are Alignment API and secondstring library. These metrics are used to align different ontologies provided with Alignment API package and then evaluated the performance of these metrics with regard to precision, recall and F-measure. Further, different ontology alignment systems are studied which used string similarity metrics in combination of structural similarity metrics and linguistic similarity metrics. The role of string similarity metrics is analysed and found that Jaccard in combination with TF/IDF similarity metric works good as well as ngram also works better.

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Section: Related Workmentioning

confidence: 99%

Evaluation of String based Ontology Alignment Technique

Sohail¹,

Waqar²,

Khan³

2018

IJCA

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“…Automating ontology construction [13,16,23], population [8,30], and reuse [7] have long been topics of research. There have been many different applications but none seem to have the overall aim of the creation of a system to quickly understand the scope of a longitudinal study and identify relevant areas of interest to a clinical specialist.…”

Section: Related Researchmentioning

confidence: 99%

Mapping longitudinal studies to risk factors in an ontology for dementia

Roantree

Donoghue

Kelly

et al. 2015

Health Informatics J

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A common activity carried out by healthcare professionals is to test various hypotheses on longitudinal study data in an effort to develop new and more reliable algorithms that might determine the possibility of developing certain illnesses. The In-MINDD project provides input from a number of European dementia experts to identify the most accurate model of inter-related risk factors which can yield a personalised dementia risk quotient and profile. This model is then validated against the large population-based prospective Maastricht Aging Study (MAAS) dataset. As part of this overall goal, the research presented in this paper demonstrates how we can automate the process of mapping modifiable risk factors against large sections of the aging study and thus, use information technology to provide more powerful query interfaces.

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“…A bootstrapping approach to knowledge acquisition in the fields of visual media [29] and multimedia [5] uses existing ontologies for ontology evolution. Another perspective focuses on reusing ontologies and language components for ontology generation [30]. Noy and Klein [1] defined a set of ontology-change operations and their effects on instance data used during the ontology evolution process.…”

Section: Ontology Creation and Evolutionmentioning

confidence: 99%

Bootstrapping Ontologies for Web Services

Segev

Sheng

2012

IEEE Trans. Serv. Comput.

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Abstract-Ontologies have become the de-facto modeling tool of choice, employed in many applications and prominently in the semantic web. Nevertheless, ontology construction remains a daunting task. Ontological bootstrapping, which aims at automatically generating concepts and their relations in a given domain, is a promising technique for ontology construction. Bootstrapping an ontology based on a set of predefined textual sources, such as web services, must address the problem of multiple, largely unrelated concepts. In this paper, we propose an ontology bootstrapping process for web services. We exploit the advantage that web services usually consist of both WSDL and free text descriptors. The WSDL descriptor is evaluated using two methods, namely Term Frequency/Inverse Document Frequency (TF/IDF) and web context generation. Our proposed ontology bootstrapping process integrates the results of both methods and applies a third method to validate the concepts using the service free text descriptor, thereby offering a more accurate definition of ontologies. We extensively validated our bootstrapping method using a large repository of real-world web services and verified the results against existing ontologies. The experimental results indicate high precision. Furthermore, the recall versus precision comparison of the results when each method is separately implemented presents the advantage of our integrated bootstrapping approach.Index Terms-Web services discovery, metadata of services interfaces, service-oriented relationship modeling.

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Generating Ontologies Via Language Components and Ontology Reuse

Cited by 15 publications

References 6 publications

Evaluation of String based Ontology Alignment Technique

Evaluation of String based Ontology Alignment Technique

Mapping longitudinal studies to risk factors in an ontology for dementia

Bootstrapping Ontologies for Web Services

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