Investigating a Method for Automatic Construction and Population of Ontologies for Services: Performances and Limitations

Louge, Thierry; Karray, Mohamed Hedi; Archimède, Bernard

doi:10.1109/aiccsa.2018.8612844

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…They propose to identify the predicate of a given ontology, which links two terms -labelled with the URIs of the concepts they refer to. The recent research work of Louge et al [11] fulfills a similar goal by building a taxonomy from services' short descriptions, but a taxonomy is a lightweight ontology and is not expressive enough.…”

Section: Related Workmentioning

confidence: 99%

NALDO: From natural language definitions to OWL expressions

Emani

Silva

Fiés

et al. 2019

Data & Knowledge Engineering

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Domain ontologies are pivotal for Semantic Web applications. The richness of an ontology goes in hand with its usefulness and efficiency. Unfortunately, manually enriching an ontology is very time-consuming. In this paper, we propose to enrich an ontology automatically by obtaining logical expressions of concepts. We present NALDO, a novel approach that provides an OWL DL (Web Ontology Language Description Logics) expression of a concept from two inputs: (1) the natural language definition of the concept and (2) an ontology describing the domain of this concept. NALDO uses as much as possible entities provided by the domain ontology, however it can suggest, when needed, new entities. The expressiveness of expressions provided by NALDO covers value and cardinality restrictions, subsumption and equivalence. We evaluate our approach against the definitions and the corresponding ontologies of the BEAUFORD benchmark. Our results show that NALDO is able to perform the correct identification of formal entities with an F1-measure up to 0.79.

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Section: Related Workmentioning

confidence: 99%

NALDO: From natural language definitions to OWL expressions

Emani

Silva

Fiés

et al. 2019

Data & Knowledge Engineering

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“…Several approaches for automatic ontology construction have been discussed in the literature. For example, information extraction (IE) methods, natural language processing (NLP), and comparison with knowledge references are used to build and populate ontologies automatically and semi-automatically [16].…”

Section: Introductionmentioning

confidence: 99%

A systematic literature review of automatic ontology construction

Zulkipli

Maskat

Teo

2022

IJEECS

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In recent years, ontologies have received a lot of attention as a knowledge representation technique. Constructing an ontology can be a difficult task for several reasons: it necessitates time-consuming expert work, the classification task is not as simple as it appears, and the incredible speed with which knowledge evolves in the real world forces ontology engineers to constantly update and enrich the generated ontologies with new concepts, terms, and lexicon. However, there are other ways to automate ontology construction, such as semi-automatically, where human intervention is required in one or more ontology design tasks, and fully automatically, where the entire ontology construction is delegated to a software system. In this paper, we will conduct a systematic review of literature that will focus on a comparative analysis of different techniques relating to both semi-automatic and fully automatic ontology construction using various techniques and automated algorithms applied. In these fields, the goal is to identify the domain areas, current trends, data architectures, and ongoing challenges. This paper will review academic documents published in peer-reviewed venues from 2017 to 2021, based on a four-step selection process of identification, screening, eligibility, and inclusion for the selection process. To examine these documents, a systematic review was conducted and six main research questions were answered. The results indicate that automatic ontology construction could give higher complexity, shorter time, and reduce the role of the expert knowledge to evaluate ontology than manual ontology construction. The fields that have been investigated in this survey include online retail, biomedical, public security, information security (IS), Quran, Arabic, Dubai government services, Alzheimer’s disease, agriculture, Chinese tax, job portal, sentiment, and ontology learning. Finally, we summarize the most commonly used methods in automatic ontology construction, which we believe will serve as a foundation for future multidisciplinary research.

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“…If some systems exist for knowledge gathering from unstructured sources of data, most of them remain very domain specific [7,22,23] in order to keep accuracy performances. The few systems that might cover several domains [24,25] are rebuilding their own ontology structure instead of populating an existing predefined structure without altering it. This approach is also limited because all the knowledge structure preset by the experts is not reused.…”

Section: Introductionmentioning

confidence: 99%

A generic metamodel for data extraction and generic ontology population

Chasseray

Barthe‐Delanoë

Negny

et al. 2021

Journal of Information Science

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As the next step in the development of intelligent computing systems is the addition of human expertise and knowledge, it is a priority to build strong computable and well-documented knowledge bases. Ontologies partially respond to this challenge by providing formalisms for knowledge representation. However, one major remaining task is the population of these ontologies with concrete application. Based on Model-Driven Engineering principles, a generic metamodel for the extraction of heterogeneous data is presented in this article. The metamodel has been designed with two objectives, namely (1) the need of genericity regarding the source of collected pieces of knowledge and (2) the intent to stick to a structure close to an ontological structure. As well, an example of instantiation of the metamodel for textual data in chemistry domain and an insight of how this metamodel could be integrated in a larger automated domain independent ontology population framework are given.

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Investigating a Method for Automatic Construction and Population of Ontologies for Services: Performances and Limitations

Cited by 4 publications

References 19 publications

NALDO: From natural language definitions to OWL expressions

NALDO: From natural language definitions to OWL expressions

A systematic literature review of automatic ontology construction

A generic metamodel for data extraction and generic ontology population

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