Ontology population with deep learning-based NLP: a case study on the Biomolecular Network Ontology

Ayadi, A.; Samet, Ahmed; Beuvron, François de Bertrand de; Zanni-Merk, Cécilia

doi:10.1016/j.procs.2019.09.212

Cited by 36 publications

(11 citation statements)

References 18 publications

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“…In recent years, we can observe the increasing tendency of using deep learning methods for extraction of knowledge-rich contexts (Petrucci et al, 2018;Ayadi et al, 2019; Navarro-Almanza, 2020). Usually, the extraction of knowledge-rich contexts using deep neural networks is a two-step procedure.…”

Section: Bilingual Term Extraction (Bite) and Alignment Methodologymentioning

confidence: 99%

“…The state-of-the-art methodology used for interlinking modern termbases is based on Linguistic Linked Open Data (LLOD) technologies which make them interoperable and connected to the Semantic Web. Tim Berners-Lee, the father of Linked Open Data, formulated four conditions for data to be linked data: (1) referred entities should be designated by using URIs (Uniform Resource Identifiers), (2) these URIs should be resolvable over HTTP, (3)…”

Section: Bilingual Term Extraction (Bite) and Alignment Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Methodological Framework for the Development of an English-Lithuanian Cybersecurity Termbase

Rackevičienė

Mockienė

Utka

et al. 2021

StALan

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The aim of the paper is to present a methodological framework for the development of an English-Lithuanian bilingual termbase in the cybersecurity domain, which can be applied as a model for other language pairs and other specialised domains. It is argued that the presented methodological approach can ensure creation of high-quality bilingual termbases even with limited available resources. The paper touches upon the methods and problems of dataset (corpora) compilation, terminology annotation, automatic bilingual term extraction (BiTE) and alignment, knowledge-rich context extraction, and linguistic linked open data (LLOD) technologies. The paper presents theoretical considerations as well as the arguments on the effectiveness of the described methods. The theoretical analysis and a pilot study allow arguing that: 1) a combination of parallel and comparable corpora enable to considerably expand the amount and variety of data sources that can be used for terminology extraction; this methodology is especially important for less-resourced languages which often lack parallel data; 2) deep learning systems trained by using manually annotated data (gold standard corpora) allow effective automatization of extraction of terminological data and metadata, which enables to regularly update termbases with minimised manual input; 3) LLOD technologies enable to integrate the terminological data into the global linguistic data ecosystem and make it reusable, searchable and discoverable across the Web.

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Section: Bilingual Term Extraction (Bite) and Alignment Methodologymentioning

confidence: 99%

Section: Bilingual Term Extraction (Bite) and Alignment Methodologymentioning

confidence: 99%

Methodological Framework for the Development of an English-Lithuanian Cybersecurity Termbase

Rackevičienė

Mockienė

Utka

et al. 2021

StALan

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“…Many methods for ontology learning from text or automatic ontology-population with NLP techniques have been investigated in the literature. For example, in [57], the author provides a knowledge repository of ontology learning tools; in [58], is presented how to populate an ontology with deep learning-based NLP methods from biological documents, and in [59] NLP techniques for ontology population using a combination of rule-based approaches and machine learning are discussed as well. However, only for specific and well-defined domains a fully automatic ontology construction using textual data is feasible [60].…”

Section: Conceptual Layermentioning

confidence: 99%

Tacit knowledge elicitation process for industry 4.0

Fenoglio

Kazim

Latapie

et al. 2022

Discov Artif Intell

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Manufacturers migrate their processes to Industry 4.0, which includes new technologies for improving productivity and efficiency of operations. One of the issues is capturing, recreating, and documenting the tacit knowledge of the aging workers. However, there are no systematic procedures to incorporate this knowledge into Enterprise Resource Planning systems and maintain a competitive advantage. This paper describes a solution proposal for a tacit knowledge elicitation process for capturing operational best practices of experienced workers in industrial domains based on a mix of algorithmic techniques and a cooperative game. We use domain ontologies for Industry 4.0 and reasoning techniques to discover and integrate new facts from textual sources into an Operational Knowledge Graph. We describe a concepts formation iterative process in a role game played by human and virtual agents through socialization and externalization for knowledge graph refinement. Ethical and societal concerns are discussed as well.

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“…After data acquisition, the next steps of a typical OP system are the knowledge extraction process (i.e., semantic annotation, NLP, and semantic extraction), followed by the population process (i.e., redundancy elimination, consistency checking, and ontology instantiation) and, finally, the storage stage (usually in a TripleStore) [13][14][15]54,55]. Semantic annotation is the process of adding metadata about concepts, entities, and relationships over unstructured documents and data.…”

Section: Ontology Instantiation: Populating Knowledge Graphsmentioning

confidence: 99%

“…In general, three main approaches can be distinguished when dealing with OP for extracting domain-specific terms [55]: (i) rule-based ontology population systems [15,58], (ii) ontology population systems using ML or NLP [54,59], and (iii) ontology population systems that use statistical approaches [57]. In rule-based systems (i), the rule group is designed and developed for the location, classification, and extraction of information in predefined categories such as people, organizations, time expressions, places, etc., known as named entities.…”

Section: Ontology Instantiation: Populating Knowledge Graphsmentioning

confidence: 99%

Integroly: Automatic Knowledge Graph Population from Social Big Data in the Political Marketing Domain

Guedea-Noriega

García‐Sánchez

2022

Applied Sciences

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Social media sites have become platforms for conversation and channels to share experiences and opinions, promoting public discourse. In particular, their use has increased in political topics, such as citizen participation, proselytism, or political discussions. Political marketing involves collecting, monitoring, processing, and analyzing large amounts of voters’ data. However, the extraction, integration, processing, and storage of these torrents of relevant data in the political domain is a very challenging endeavor. In the recent years, the semantic technologies as ontologies and knowledge graphs (KGs) have proven effective in supporting knowledge extraction and management, providing solutions in heterogeneous data sources integration and the complexity of finding meaningful relationships. This work focuses on providing an automated solution for the population of a political marketing-related KG from Spanish texts through Natural Language Processing (NLP) techniques. The aim of the proposed framework is to gather significant data from semi-structured and unstructured digital media sources to feed a KG previously defined sustained by an ontological model in the political marketing domain. Twitter and political news sites were used to test the usefulness of the automatic KG population approach. The resulting KG was evaluated through 18 quality requirements, which ensure the optimal integration of political knowledge.

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Ontology population with deep learning-based NLP: a case study on the Biomolecular Network Ontology

Cited by 36 publications

References 18 publications

Methodological Framework for the Development of an English-Lithuanian Cybersecurity Termbase

Methodological Framework for the Development of an English-Lithuanian Cybersecurity Termbase

Tacit knowledge elicitation process for industry 4.0

Integroly: Automatic Knowledge Graph Population from Social Big Data in the Political Marketing Domain

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