Towards open ontology learning and filtering

Zouaq, Amal; Gašević, Dragan; Hatala, Marek

doi:10.1016/j.is.2011.03.005

Cited by 57 publications

(22 citation statements)

References 34 publications

(54 reference statements)

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“…Moreover, as stated before, the assessment of relevance of retrieved documents strongly relies on subjective judgments of experts or real users involved. We can cite some results found in recent literature that also reinforce these di±-culties: Text2Onto obtained an average Precision/Recall of 31.71%/25.16% for concept identi¯cation in the evaluation of the ontologization task of a corpus from SCORM manuals, performed in [44]. In [45], Text2Onto achieved a Precision/Recall of 6%/35% extracting a concept ontology over a total of 80 web-sites relevant for 10 information goals, while the proposed tool showed a Precision/Recall of 78%/87% (on the same test domain).…”

Section: Assessment and Validationsupporting

confidence: 61%

Assisted Knowledge Base Generation, Management and Competence Retrieval

Bellandi

Bellini

Cappuccio

et al. 2012

Int. J. Soft. Eng. Knowl. Eng.

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Despite the presence of many systems for developing and managing structured taxonomies and/or SKOS models for a given domain for which small documents set are accessible, the production and maintenance of these domain knowledge bases is still a very expensive and time consuming process. This paper proposes a solution for assisting expert users in the development and management of knowledge base, including SKOS and ontologies modeling structures and relationships. The proposed solution accelerates the knowledge production by crawling and exploiting di®erent kinds of sources (in multiple languages and with several inconsistencies among them). The proposed tool supports the experts in de¯ning relationships among the most recurrent concepts, reducing the time to SKOS production and allowing assisted production. The validity of the produced knowledge base has been assessed by using SPARQL query interface and a precision and recall model. The results have demonstrated better performance with respect to the state of the art. The solution has been developed for Open Space Innovative Mind project, with the aim of creating a portal to allow industries at posing semantic queries to discover potential competences in a large institution such as the University of Florence, in which several distinct domains are associated with its own departments.

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Section: Assessment and Validationsupporting

confidence: 61%

Assisted Knowledge Base Generation, Management and Competence Retrieval

Bellandi

Bellini

Cappuccio

et al. 2012

Int. J. Soft. Eng. Knowl. Eng.

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“…In particular, ontology-based approaches have been widely used for context modeling and management [8][9][10][11][12]. A variety of ontologies have been adopted to model knowledge about subject domains, users, resources, and other contextual elements of the user surroundings [8,[29][30][31][32][33][34]. In such setups, reasoning techniques are usually applied on metadata derived from a single ontology.…”

Section: Background and Related Workmentioning

confidence: 99%

Context-aware recommender for mobile learners

Benlamri

Zhang

2014

Hum. Cent. Comput. Inf. Sci.

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As mobile technologies become widespread, new challenges are facing the research community to develop lightweight learning services adapted to the learner's profile, context, and task at hand. This paper attempts to solve some of these challenges by proposing a knowledge-driven recommender for mobile learning on the Semantic Web. The contribution of this work is an approach for context integration and aggregation using an upper ontology space and a unified reasoning mechanism to adapt the learning sequence and the learning content based on the learner's activity, background, used technology, and surrounding environment. Whenever context change occurs, the system identifies the new contextual features and translates them into new adaptation constraints in the operating environment. The proposed system has been implemented and tested on various mobile devices. The experimental results show many learning scenarios to demonstrate the usefulness of the system in practice.

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“…A number of survey papers on ontology learning approaches are available in literature [12], [15], [8], [16], [17], [18]. Open ontology learning and filtering approaches are discussed in [1].An approach for class expression learning for ontology engineering is proposed by [8]. There are only a few papers published on description logic (DL) based technique, one of the paper on learning IS A relationship [4].…”

Section: E Motivation For Ontology Learningmentioning

confidence: 99%

Heavy weight ontology learning using text documents

Kumar

Chaudhary

2014

2014 International Conference on Parallel, Distributed and Grid Computing

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Ontology plays an important role not only for data processing in knowledge based systems but also, provide interoperability in heterogeneous environment and is a cornerstone of semantic web technology. The required technology is used for knowledge representation in OWL/RDF format and facilitate faster access of concepts in domain of interest. Development of ontology is a tedious job and requires a lot of man power in terms of experts' time and knowledge. Although there are various tools and techniques for light weight ontology learning; yet full automation of heavy weight ontology learning from text documents is a distant dream. In this paper we have proposed a framework for learning heavy weight ontology, using text documents written in English language. Initial experimental results are shown for demonstration of our on going research.

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Towards open ontology learning and filtering

Cited by 57 publications

References 34 publications

Assisted Knowledge Base Generation, Management and Competence Retrieval

Assisted Knowledge Base Generation, Management and Competence Retrieval

Context-aware recommender for mobile learners

Heavy weight ontology learning using text documents

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