An ontology-based framework for automatic topic detection in multilingual environments

Gutiérrez-Batista, Karel; Campaña, Jesús R.; Miranda, María Amparo Vila; Martín-Bautista, María J.

doi:10.1002/int.21986

Cited by 19 publications

(11 citation statements)

References 38 publications

(56 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A comparison regarding main approaches [4,13,28,37,45,46,51] with respect to our proposed criteria is shown in Table 1 and illustrates that all existing studies use a semiautomatic approach to learn domain-specific ontologies and use several criteria to evaluate ontologies. Also, the inconsistency regarding entries is still an open challenge.…”

Section: Discussionmentioning

confidence: 99%

“…In the classification phase, we also note that [13,46] and Posch [45] use a certain document representation method which does not consider semantic relations among different words. Gutiérrez-Batista et al [28], Asfari et al [4], and Zavitsanos et al [51] use the probabilistic document representation to recognize terms of documents and to use topic probability distribution for representations. However, none of them uses the probabilistic method with complex terms conducting to generate semantic inconsistency.…”

Section: Discussionmentioning

confidence: 99%

“…There have been many studies [4,13,28,34] that used the learning data provided by LDA for the construction of ontologies in different fields. That is to say, from the result generated by LDA which consists of topics and their associated vocabularies, these existing studies build ontologies (bottom-up principle).…”

Section: Probabilistic Topic Modelsmentioning

confidence: 99%

“…They extracted the noun phrases and verbs that designate the input data for LDA. It is pointed out that there are also various other areas that show the application of LDA for ontology construction such as neurology [52], ecology [28], and telecommunications [27].…”

Section: Probabilistic Topic Modelsmentioning

confidence: 99%

“…It consists of composing a synonym list containing the NPs that are written in different ways in order use one common NP. To do so, we rely here on the NPs list reviewed by experts and consider it "correctly generated" with no [4] Semi-automatic Criteria-based evaluation Domain-specific Unstructured data Simple term Probabilistic [45] Semi-automatic Criteria-based evaluation Domain-specific Unstructured data Simple term Basic [46] Semi-automatic Criteria-based evaluation Domain-specific Unstructured data Simple term Basic [37] Semi-automatic Criteria-based evaluation Domain-specific Unstructured data Complex term Basic [28] Semi-automatic Criteria-based evaluation Domain-specific Unstructured data Simple term Probabilistic Proposed approach Semi-automatic ALL Generic Unstructured Data Complex term Probabilistic SN Computer Science errors. Thus, we highlight that one of the characteristics of our vocabulary is that each generated term has only one meaning which is represented by a single term.…”

Section: Preprocessing Modulementioning

confidence: 99%

See 4 more Smart Citations

Probabilistic Topic Models for Enriching Ontology from Texts

2020

View full text Add to dashboard Cite

The ontology enrichment process is text-based and the application domain in hand is circumscribed to the content of the related texts. However, the main challenge in ontology enrichment is its learning, since there is still a lack of relevant approach able to achieve automatic enrichment from a textual corpus or dataset of various topics. In this paper, we describe a new approach for automatic learning of terminological ontologies from textual corpus based on probabilistic models. In our approach, two topic modeling algorithms are explored, namely LDA and pLSA for learning topic ontology. The objective is to capture semantic relationships between word-topic and topic-document in terms of probability distributions to build a topic ontology and ontology graph with minimum human intervention. Experimental analysis on building a topic ontology and retrieving corresponding topic ontology for a user query demonstrates the effectiveness of the proposed approach.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Probabilistic Topic Modelsmentioning

confidence: 99%

Section: Probabilistic Topic Modelsmentioning

confidence: 99%

Section: Preprocessing Modulementioning

confidence: 99%

See 3 more Smart Citations

Probabilistic Topic Models for Enriching Ontology from Texts

2020

View full text Add to dashboard Cite

show abstract

A top‐down enriching approach for ontology learning from text

Tissaoui

Sassi

Chbeir

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

Summary To allow better communications between computers and people, ontologies have been adopted in several application domains (web, medicine, industry, etc.). Ontology building exhibits a structural and logical complexity. To the end of making high quality domain ontologies, effective and usable methodologies are needed to facilitate their building process. In this article, we propose to extend the classical methods of ontology construction to design semantically richer ontologies. The objective of this article is to study the relevance of the latent Dirichlet allocation model that generates probabilistic topic models for each enrichment proposal by adopting a domain independent core ontology model. The fitted model can be used to estimate the similarity between documents as well as between a set of specified words/terms using an additional layer of latent variables which are referred to as topics. Experiments were conducted to measure the quality of our proposal against other solutions. Obtained results discussed here are satisfactory.

show abstract

Semantic data mining in the information age: A systematic review

Chanmee

Kesorn

2021

Int J Intell Syst

View full text Add to dashboard Cite

Data mining is the discovery of meaningful information or unrevealed patterns in data. Traditional data‐mining approaches, using statistical calculations, machine learning, artificial intelligence, and database technology, cannot interpret data on a conceptual or semantic level and fail to reveal the meanings within the data. This results in a user not being analyzed and determines its signification and implications. Several semantic data‐mining approaches have been proposed in the past decade that overcome these limitations by using a domain ontology as background knowledge to enable and enhance data‐mining performance. The main contributions of this literature survey include organizing the surveyed articles in a new way that provides ease of understanding for interested researchers, and the provision of a critical analysis and summary of the surveyed articles, identifying the contribution of these papers to the field, and the limitations of the analysis methods and approaches discussed in this corpus, with the intention of informing researchers in this growing field in their innovative approaches to new research. Finally, we identify the future trends and challenges in this study track that will be of concern to future researchers, such as dynamic knowledge‐based methods or big‐data tool collaboration. This survey article provides a comprehensive overview of the literature on domain ontologies as used in the various semantic data‐mining tasks, such as preprocessing, modeling, and postprocessing. We investigated the role of semantic data mining in the field of data science and the processes and methods of applying semantic data mining to a data resource description framework.

show abstract

An ontology-based framework for automatic topic detection in multilingual environments

Cited by 19 publications

References 38 publications

Probabilistic Topic Models for Enriching Ontology from Texts

Probabilistic Topic Models for Enriching Ontology from Texts

A top‐down enriching approach for ontology learning from text

Semantic data mining in the information age: A systematic review

Contact Info

Product

Resources

About