Ontology augmentation

Fernández, Miriam; Zhang, Ziqi; López, Vanesa; Uren, Victoria; Motta, Enrico

doi:10.1145/1999676.1999680

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…guidance) [40]. Also, the use of general thesaurus resources such as WordNet [3] is not suitable for this resources although it is recommended for expanding or specifying information in other ontologies [41]. Second element is the expertise which involves human intervention to make sure the quality of a knowledge based.…”

Section: Development Of Quran Ontologymentioning

confidence: 99%

Extracting Knowledge From English Translated Quran Using NLP Pattern

2015

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Ontology is able to represent knowledge from an abstract view into formal semantics. It is essential for the success of knowledge-based systems because it has been used to share vocabulary, discover new knowledge, flexible access of knowledge and easy integration of knowledge. Currently, Ontology from Quran is not complete and most of the development is done manually. Manual development of ontology is time consuming and labor intensive task. Hence, the automatic or semi-automatic ontology development which is a field of Ontology Learning is needed to efficiently extract knowledge and transform it into Ontology. Current techniques employed in Ontology Learning are based on statistical and Natural Language Processing. This paper provides result from an experiment to extract knowledge using the existing Natural Language Processing (NLP) Pattern based on the Ontology Learning approach. Initial experiment shows that the pattern could be used to extract knowledge in terms of relations that exist in English translated Quran. In addition, NLP could also use to identify new pattern that can be further explored.

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Section: Development Of Quran Ontologymentioning

confidence: 99%

Extracting Knowledge From English Translated Quran Using NLP Pattern

2015

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What Should I Link to? Identifying Relevant Sources and Classes for Data Linking

2012

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Abstract. With more data repositories constantly being published on the Web, choosing appropriate data sources to interlink with newly published datasets becomes a non-trivial problem. It is necessary to choose both the repositories to link to and the relevant subsets of these repositories, which contain potentially matching individuals. In order to do this, detailed information about the content and structure of semantic repositories is often required. However, retrieving and processing such information for a potentially large number of datasets is practically unfeasible. In this paper, we propose an approach which utilises an existing semantic web index in order to identify potentially relevant datasets for interlinking and rank them. Furthermore, we adapt instance-based ontology schema matching to extract relevant subsets of selected data source and, in this way, pre-configure data linking tools.

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Realizing Networks of Proactive Smart Products

d’Aquin

Motta

Nikolov

et al. 2012

Lecture Notes in Computer Science

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Abstract. The sheer complexity and number of functionalities embedded in many everyday devices already exceed the ability of most users to learn how to use them effectively. An approach to tackle this problem is to introduce 'smart' capabilities in technical products, to enable them to proactively assist and co-operate with humans and other products. In this paper we provide an overview of our approach to realizing networks of proactive and co-operating smart products, starting from the requirements imposed by real-world scenarios. In particular, we present an ontology-based approach to modeling proactive problem solving, which builds on and extends earlier work in the knowledge acquisition community on problem solving methods. We then move on to the technical design aspects of our work and illustrate the solutions, to do with semantic data management and co-operative problem solving, which are needed to realize our functional architecture for proactive problem solving in concrete networks of physical and resource-constrained devices. Finally, we evaluate our solution by showing that it satisfies the quality attributes and architectural design patterns, which are desirable in collaborative multi-agents systems.

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Ontology augmentation

Cited by 5 publications

References 16 publications

Extracting Knowledge From English Translated Quran Using NLP Pattern

Extracting Knowledge From English Translated Quran Using NLP Pattern

What Should I Link to? Identifying Relevant Sources and Classes for Data Linking

Realizing Networks of Proactive Smart Products

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