Knowledge Repository of Ontology Learning Tools from Text

Konys, Agnieszka

doi:10.1016/j.procs.2019.09.332

Cited by 37 publications

(14 citation statements)

References 27 publications

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“…However, the typical condition is that a full ontology for a particular industrial domain does not exist or is available only partially. In this case, the domain ontology is initially developed from the CMs [28] with the foundational ontologies used as guidance for domain experts during the collaborative process (Section 4) taking advantage of the many ontology learning methods from texts for extracting ontologies with NLP techniques [42].…”

Section: Concepts Formationmentioning

confidence: 99%

Tacit Knowledge Elicitation Process for Industry 4.0

Fenoglio

Kazim

Latapie

et al. 2021

Preprint

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Manufacturers migrate their processes to Industry 4.0 that 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 for maintaining a competitive advantage. This paper describes a solution proposal for a tacit knowledge elici-tation process for capturing operational best practices of experienced workers in industrial domains for on-site training. We use Concept Maps mining and domain ontologies to discover and integrate information into a Knowledge Graph in a role game played by human and virtual agents. Ethical and societal concerns are discussed as well.

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Section: Concepts Formationmentioning

confidence: 99%

Tacit Knowledge Elicitation Process for Industry 4.0

Fenoglio

Kazim

Latapie

et al. 2021

Preprint

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“…Para seleccionar el índice de términos, este trabajo utilizó la evaluación basada en gold standard por dos razones. Primero, los resultados son reproducibles y comparables al examinar el mismo corpus (Konys, 2019). Segundo, esta validación facilita la adquisición de métricas como precision, recall, and F-measure que caracterizan la funcionalidad de la ontología aprendida a través de los términos que son los bloques de construcción iniciales.…”

Section: Construcción Vocabulariounclassified

Apropiación social del conocimiento investigación, innovación y extensión

Mejía¹,

Suta²,

Arango³

et al. 2021

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“…Knowledge Bases are usually built as relational schema using expert knowledge from existing ontologies [5], or by ontology extraction from semi-structured ( [8,23]) or unstructured text [7,13]. Automatic ontology learning from unstructured text involves extracting concepts, entity linking and ontology population, using methods based on NLP, information retrieval, machine learning and data mining [11,12] or dedicated platforms (OntoLearn [24], Text2Onto [2], OntoText [18]). Some of the most known "industrial" KG as Google Knowledge Graph, YAGO [8] and ConceptNet [22] have mapped billions of entities and relations from web-based ontologies like Wikipedia, WordNet, Wikidata, Freebase or Facebook [20].…”

Section: Related Workmentioning

confidence: 99%

KnowText: Auto-generated Knowledge Graphs for custom domain applications

Božić

Sasikumar

Matthews

2021

The 23rd International Conference on Information Integration and Web Intelligence

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While industrial Knowledge Graphs enable information extraction from massive data volumes creating the backbone of the Semantic Web, the specialised, custom designed knowledge graphs focused on enterprise specific information are an emerging trend. We present "KnowText", an application that performs automatic generation of custom Knowledge Graphs from unstructured text and enables fast information extraction based on graph visualisation and free text query methods designed for non-specialist users. An OWL ontology automatically extracted from text is linked to the knowledge graph and used as a knowledge base. A basic ontological schema is provided including 16 Classes and Data type Properties. The extracted facts and the OWL ontology can be downloaded and further refined. KnowText is designed for applications in business (CRM, HR, banking). Custom KG can serve for locally managing existing data, often stored as "sensitive" information or proprietary accounts, which are not on open web access. KnowText deploys a custom KG from a collection of text documents and enable fast information extraction based on its graph based visualisation and text based query methods.

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Knowledge Repository of Ontology Learning Tools from Text

Cited by 37 publications

References 27 publications

Tacit Knowledge Elicitation Process for Industry 4.0

Tacit Knowledge Elicitation Process for Industry 4.0

Apropiación social del conocimiento investigación, innovación y extensión

KnowText: Auto-generated Knowledge Graphs for custom domain applications

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