A Process for Extracting Non-Taxonomic Relationships of Ontologies from Text

Serra, Ivo; Girardi, Rosario

doi:10.4236/iim.2011.34014

Cited by 11 publications

(2 citation statements)

References 8 publications

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“…Serra y Girardi en [6] proponen un proceso semiautomático para extracción de relaciones no taxonómicas de fuentes de texto, haciendo uso de técnicas de procesamiento de lenguaje natural (PLN) para identificar las relaciones no taxonómicas y técnicas de minería de datos para sugerir un nivel alto dentro de la jerarquía de la ontología en inglés. El proceso se divide en tres fases.…”

Section: Trabajos Relacionadosunclassified

Algoritmo basado en reglas de asociación para la extracción de relaciones no taxonómicas en corpus de dominio

Moreno¹,

Vidal²,

Lavalle-Martínez³

et al. 2019

RCS

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Resumen. La identificación de relaciones no taxonómicas es una tarea que se realiza con el aprendizaje y la creación de ontologías. Además, la construcción manual de ontologías para expertos e ingenieros de conocimiento es una tarea costosa y lenta, por lo que es necesario crear algoritmos automáticos y/o semiautomáticos que agilicen el procedimiento. En esta investigación se propone un algoritmo para la extracción de relaciones no taxonómicas en una ontología de Inteligencia Artificial (IA), las cuales son evaluadas a través de una técnica de minería de datos: reglas de asociación, que cuenta con medidas estadísticas que determinan la probabilidad de ocurrencia entre los conceptos y el verbo conector relacionados. Los resultados experimentales indican que el 72 % de las relaciones obtenidas en el algoritmo existen en la ontología de IA. Palabras clave: relaciones no taxonómicas, extracción de información, ontología.

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Section: Trabajos Relacionadosunclassified

Algoritmo basado en reglas de asociación para la extracción de relaciones no taxonómicas en corpus de dominio

Moreno¹,

Vidal²,

Lavalle-Martínez³

et al. 2019

RCS

View full text Add to dashboard Cite

show abstract

“…The definition of an ontology [25] also includes axioms of the knowledge domain. In this way it is possibility to reason over that domain, i.e., to check the consistency of the relations or infer new knowledge.…”

Section: Ontologies -Knowledge Formal Representationmentioning

confidence: 99%

Ontological approach for DSL development

Pereira

Fonseca

Henriques

2016

Computer Languages, Systems & Structures

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a b s t r a c tThis paper presents a project whose main objective is to explore the ontological based development of Domain Specific Languages (DSL), more precisely, of their underlying Grammar. After reviewing the basic concepts characterizing Ontologies and DSLs, we introduce a tool, Onto2Gra, that takes profit of the knowledge described by the ontology and automatically generates a grammar for a DSL that allows to discourse about the domain described by that ontology. This approach represents a rigorous method to create, in a secure and effective way, a grammar for a new specialized language restricted to a concrete domain. The usual process of creating a grammar from the scratch is, as every creative action, difficult, slow and error prone; so this proposal is, from a grammar engineering point of view, of uttermost importance. After the grammar generation phase, the Grammar Engineer can manipulate it to add syntactic sugar to improve the final language quality or even to add specific semantic actions. The Onto2Gra project is composed of three engines. The main one is OWL2DSL, the component that converts an OWL ontology into a complete Attribute Grammar for the construction of an internal representation of all the input data. The two additional modules are Onto2OWL, converts ontologies written in OntoDL into standard OWL, and DDesc2OWL, converts domain instances written in the new DSL into the initial OWL ontology.Published by Elsevier Ltd.

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Exploiting lexical patterns for knowledge graph construction from unstructured text in Spanish

Ríos-Alvarado

Martinez-Rodriguez

Garcia-Perez

et al. 2022

Complex Intell. Syst.

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Knowledge graphs (KGs) are useful data structures for the integration, retrieval, dissemination, and inference of information in various information domains. One of the main challenges in building KGs is the extraction of named entities (nodes) and their relations (edges), particularly when processing unstructured text as it has no semantic descriptions. Generating KGs from texts written in Spanish represents a research challenge as the existing structures, models, and strategies designed for other languages are not compatible in this scenario. This paper proposes a method to design and construct KGs from unstructured text in Spanish. We defined lexical patterns to extract named entities and (non) taxonomic, equivalence, and composition relations. Next, named entities are linked and enriched with DBpedia resources through a strategy based on SPARQL queries. Finally, OWL properties are defined from the predicate relations for creating resource description framework (RDF) triples. We evaluated the performance of the proposed method to determine the degree of elements extracted from the input text and to assess their quality through standard information retrieval measures. The evaluation revealed the feasibility of the proposed method to extract RDF triples from datasets in general and computer science domains. Competitive results were observed by comparing our method regarding an existing approach from the literature.

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A Process for Extracting Non-Taxonomic Relationships of Ontologies from Text

Cited by 11 publications

References 8 publications

Algoritmo basado en reglas de asociación para la extracción de relaciones no taxonómicas en corpus de dominio

Algoritmo basado en reglas de asociación para la extracción de relaciones no taxonómicas en corpus de dominio

Ontological approach for DSL development

Exploiting lexical patterns for knowledge graph construction from unstructured text in Spanish

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