Learning Well-Founded Ontologies through Word Sense Disambiguation

Leão, Felipe; Revoredo, Kate; Baião, Fernanda

doi:10.1109/bracis.2013.40

Cited by 5 publications

(11 citation statements)

References 17 publications

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“…Looking at the 7 cases in error, we observe that all errors are the result of the frequent disambiguation problem been reported in several studies of semantic analysis and which is currently a challenge for the area [35]. With this result, we believe that the extraction of concepts was satisfactory.…”

Section: Phase 3 -Evaluationsupporting

confidence: 53%

“…However, we conclude that the method needs to be revised to include a new phase, in order to reduce the ambiguities. In this sense, the work of Leão, Revoredo and Baiao [35] could provide a basis to support this new phase.…”

Section: Phase 3 -Evaluationmentioning

confidence: 93%

“…Leão, Revoredo and Baiao [35] intends to obtain a well-founded ontology using natural language processing coupled with the Wordnet elements. In this case we might generate an ontology less ambiguous or subjected to diverse interpretations.…”

Section: Related Work and Next Stepsmentioning

confidence: 99%

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Learning Ontology from Text: A Storytelling Exploratory Case Study

Confort

Revoredo

Baião

et al. 2015

Lecture Notes in Business Information Processing

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Abstract. Business and IT systems are facing increasingly complex environments characterized by collaboration, change and variety of customers, suppliers and products. Appling group storytelling technique can contribute to the organization knowledge management. It brings benefits from capture to disseminating information, through communication and understanding of the concepts. American companies (3M and Apple), Japanese (Sony and Toshiba) and European (ClubMed and Océ) already use this approach in practice. On the other hand, the Ontology Engineering can contribute towards improving the quality of information and offer a solution to address knowledge management systematically. However, the specification and manually made of ontology management can be expensive, tedious, biased and pruned to error. Aiming to contribute with the management and quality of information, we explore the automatic learning of ontologies, which is an approach that extracts ontologies from data, both structured and unstructured (text). This work presents a proposal to extract an ontology from the tacit knowledge of those involved in the field. An exploratory study was able to get an ontology automatically from stories told by a group from a university department.

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Section: Phase 3 -Evaluationsupporting

confidence: 53%

Section: Phase 3 -Evaluationmentioning

confidence: 93%

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Learning Ontology from Text: A Storytelling Exploratory Case Study

Confort

Revoredo

Baião

et al. 2015

Lecture Notes in Business Information Processing

Self Cite

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“…In the literature, most systems/methodologies (Friedman et al, 2004;Leao et al, 2013;Rajni & Taneja, 2013) require a knowledge engineer to translate unstructured text into fully structured form and most systems have been developed using NLP techniques and without the support of controlled natural language (Friedman et al, 2004;Houser, 2004;Jindal & Taneja, 2013). Regarding structured knowledge construction, some studies do not support lexical ambiguity (Rajni & Taneja, 2013;Reuss et al, 2015).…”

Section: Domain Knowledge Construction Literaturementioning

confidence: 99%

“…The research community prefers to use natural language processing (NLP) techniques to construct machine-readable knowledge. In the literature, most systems/methodologies (Friedman, Shagina, Lussier, & Hripcsak, 2004;Leao, Revoredo, & Baiao, 2013;Rajni & Taneja, 2013) require high intervention of a knowledge engineer to translate unstructured text into a structured form and to resolve the construction of unambiguous machine-readable knowledge. For an automated CBL, a structured knowledge construction from textual data is a challenging task (Rusu et al, 2013).…”

mentioning

confidence: 99%

A knowledge construction methodology to automate case‐based learning using clinical documents

et al. 2019

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The case‐based learning (CBL) approach has gained attention in medical education as an alternative to traditional learning methodology. However, current CBL systems do not facilitate and provide computer‐based domain knowledge to medical students for solving real‐world clinical cases during CBL practice. To automate CBL, clinical documents are beneficial for constructing domain knowledge. In the literature, most systems and methodologies require a knowledge engineer to construct machine‐readable knowledge. Keeping in view these facts, we present a knowledge construction methodology (KCM‐CD) to construct domain knowledge ontology (i.e., structured declarative knowledge) from unstructured text in a systematic way using artificial intelligence techniques, with minimum intervention from a knowledge engineer. To utilize the strength of humans and computers, and to realize the KCM‐CD methodology, an interactive case‐based learning system(iCBLS) was developed. Finally, the developed ontological model was evaluated to evaluate the quality of domain knowledge in terms of coherence measure. The results showed that the overall domain model has positive coherence values, indicating that all words in each branch of the domain ontology are correlated with each other and the quality of the developed model is acceptable.

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Research on Security Awareness to Protect Data Through Ontology and Cloud Computing

Kiran

Nalini²

2021

Emerging Research in Computing, Information, Communication and Applications

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Learning Well-Founded Ontologies through Word Sense Disambiguation

Cited by 5 publications

References 17 publications

Learning Ontology from Text: A Storytelling Exploratory Case Study

Learning Ontology from Text: A Storytelling Exploratory Case Study

A knowledge construction methodology to automate case‐based learning using clinical documents

Research on Security Awareness to Protect Data Through Ontology and Cloud Computing

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