Semi-automatic terminology ontology learning based on topic modeling

Rani, Monika; Dhar, Amit Kumar; Vyas, O. P.

doi:10.1016/j.engappai.2017.05.006

Cited by 67 publications

(27 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Rani. (2017) [19] proposed two topic modeling algorithms are explored for learning topic ontology. The objective is to determine the statistical relationship between document and terms to build a topic ontology and ontology graph with minimum human intervention.…”

Section: Related Workmentioning

confidence: 99%

An Ontology Based Framework for Automatic Web Resources Identification

Mahmoud¹,

Mohamed²

2020

IJIES

View full text Add to dashboard Cite

Search engines' capabilities are improved continually for enhancing the retrieved results and satisfying users' needs. Despite the great efforts in the information retrieval field, the user spends long time in changing the search keywords; though the retrieved results may be out of user's expectation. This may be due to the huge number of web resources, the lack of search keywords for specific domains, and the gap between the user and author in specific domain. This paper proposes a model for improving the search engines capabilities through adding level of awareness to the web resources. The model targets the human view about web resources in addition to author perception. This is through automatically extraction of the user's perceptions and mapping them to the resources' based on semantic aspects. The experimental studies realized 36% precision improvement compared to other search engines, and user satisfaction was up to 92%.

show abstract

Section: Related Workmentioning

confidence: 99%

An Ontology Based Framework for Automatic Web Resources Identification

Mahmoud¹,

Mohamed²

2020

IJIES

View full text Add to dashboard Cite

show abstract

“…Another paradigm is based on machine learning and statistical methods which use the statistics of the underlying corpora, such as k-nearest neighbors approach (Maedche et al 2003), association rules (Maedche & Staab 2000), bottom-up hierarchical clustering techniques (Zavitsanos et al 2007), supervised classification (Spiliopoulos et al 2010) and formal concept analysis . There are also some approaches that use topic models (Schaal et al 2005, Lin et al 2012, Rani et al 2017 but they focus on concept names that are words, rather than phrases as in our approach.…”

Section: Extending Ontologies From Unstructured Textmentioning

confidence: 99%

A Method for Extending Ontologies with Application to the Materials Science Domain

Armiento

Lambrix

2019

Data Science Journal

View full text Add to dashboard Cite

In the materials science domain the data-driven science paradigm has become the focus since the beginning of the 2000s. A large number of research groups and communities are building and developing data-driven workflows. However, much of the data and knowledge is stored in different heterogeneous data sources maintained by different groups. This leads to a reduced availability of the data and poor interoperability between systems in this domain. Ontologybased techniques are an important way to reduce these problems and a number of efforts have started. In this paper we investigate efforts in the materials science, and in particular in the nanotechnology domain, and show how such ontologies developed by domain experts, can be improved. We use a phrase-based topic model approach and formal topical concept analysis on unstructured text in this domain to suggest additional concepts and axioms for the ontology that should be validated by a domain expert. We describe the techniques and show the usefulness of the approach through an experiment where we extend two nanotechnology ontologies using approximately 600 titles and abstracts.

show abstract

“…The main difference from our proposal is the fact that it does not use ontologies already defined, and therefore, it needs a later formalization. Rani et al [Rani et al 2017] proposed the use of a text corpus of various topics to form an ontology using machine learning techniques. Two topic modeling algorithms were applied, namely LSI (Latent Semantic Indexing) & SVD (Singular Value Decomposition) and Mr.LDA (MapReduce Latent Dirichlet Allocation) for learning topic ontology.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Ontology Development from the Extraction of Noun Phrases

Oliveira¹,

Moreira²,

Filho³

2019

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2019)

View full text Add to dashboard Cite

Existem vários métodos para construir uma ontologia. Entre o métodos automáticos, uma abordagem é a extração de termos de documentos de domínio e sua extração subsequente. Nesse caso, a primeira etapa do processo é a extração de sintagmas nominais que são possíveis candidatos a serem componentes da terminologia da área de interesse. Este artigo descreve uma ferramenta automática para o idioma português do Brasil que extrai sintagmas nominais que podem ser adotadas como termos para um determinado domínio. Além disso, o sistema associa os termos extraídos a uma ontologia de nível superior, o que resulta em uma ontologia inicial que pode ser refinada posteriormente. Para acoplar à ontologia, foi utilizado um termo âncora, sendo demonstrado estatisticamente que o uso do termo âncora leva a uma melhoria no desempenho do sistema. A ferramenta descrita neste artigo foi usada para selecionar termos a serem usados em uma ontologia para o domínio do setor eleétrico. Além disso, foi avaliada a precisão na criação da ontologia. A técnica conseguiu gerar a hierarquia correta para 70% dos termos

show abstract

Semi-automatic terminology ontology learning based on topic modeling

Cited by 67 publications

References 59 publications

An Ontology Based Framework for Automatic Web Resources Identification

An Ontology Based Framework for Automatic Web Resources Identification

A Method for Extending Ontologies with Application to the Materials Science Domain

Evaluating Ontology Development from the Extraction of Noun Phrases

Contact Info

Product

Resources

About