A Confidence–Weighted Metric for Unsupervised Ontology Population from Web Texts

Oliveira, Hilário; Lima, Rinaldo; Gomes, Jorge de Oliveira; Ferreira, Rafael; Costa, Evandro

doi:10.1007/978-3-642-32600-4_14

Cited by 7 publications

(5 citation statements)

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“…Since unsupervised learning is the desired characteristic of OL learning technique therefore we are interested in a such techniques. Different statistical techniques for unsupervised learning are discussed in [2], [7], [13] which gives a proper understanding of the ontology learning using text documents. The approaches discussed are focused on light weight ontology learning from text and thus fair enough for taxonomy induction from text corpora.…”

Section: E Motivation For Ontology Learningmentioning

confidence: 47%

Heavy weight ontology learning using text documents

Kumar

Chaudhary

2014

2014 International Conference on Parallel, Distributed and Grid Computing

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Ontology plays an important role not only for data processing in knowledge based systems but also, provide interoperability in heterogeneous environment and is a cornerstone of semantic web technology. The required technology is used for knowledge representation in OWL/RDF format and facilitate faster access of concepts in domain of interest. Development of ontology is a tedious job and requires a lot of man power in terms of experts' time and knowledge. Although there are various tools and techniques for light weight ontology learning; yet full automation of heavy weight ontology learning from text documents is a distant dream. In this paper we have proposed a framework for learning heavy weight ontology, using text documents written in English language. Initial experimental results are shown for demonstration of our on going research.

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Section: E Motivation For Ontology Learningmentioning

confidence: 47%

Heavy weight ontology learning using text documents

Kumar

Chaudhary

2014

2014 International Conference on Parallel, Distributed and Grid Computing

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“…We adopt the method in Maynard et al (2016) to establish the ontology development into several main tasks: ontology learning, population, and refinement. For these tasks, the most challenging bottleneck is knowledge acquisition from unlimited free text (Abeyruwan et al 2013;Oliveira et al 2012;Snchez 2012). The knowledge can be at different granulality, i.e., class (Tomaz et al 2012), entity (Ciaramita et al 2008), concept (Hoxha et al 2016;Karoui et al 2007), term (Conde et al 2016;Maynard et al 2016), and relation (Punuru and Chen 2012;Sánchez and Moreno 2006).…”

Section: Ontology Developmentmentioning

confidence: 99%

Unsupervised Approaches for Textual Semantic Annotation, A Survey

Liao

Zhao

2019

ACM Comput. Surv.

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Semantic annotation is a crucial part of achieving the vision of the Semantic Web and has long been a research topic among various communities. The most challenging problem in reaching the Semantic Web’s real potential is the gap between a large amount of unlabeled existing/new data and the limited annotation capability available. To resolve this problem, numerous works have been carried out to increase the degree of automation of semantic annotation from manual to semi-automatic to fully automatic. The richness of these works has been well-investigated by numerous surveys focusing on different aspects of the problem. However, a comprehensive survey targeting unsupervised approaches for semantic annotation is still missing and is urgently needed. To better understand the state-of-the-art of semantic annotation in the textual domain adopting unsupervised approaches, this article investigates existing literature and presents a survey to answer three research questions: (1) To what extent can semantic annotation be performed in a fully automatic manner by using an unsupervised way? (2) What kind of unsupervised approaches for semantic annotation already exist in literature? (3) What characteristics and relationships do these approaches have? In contrast to existing surveys, this article helps the reader get an insight into the state-of-art of semantic annotation using unsupervised approaches. While examining the literature, this article also addresses the inconsistency in the terminology used in the literature to describe the various semantic annotation tools’ degree of automation and provides more consistent terminology. Based on this, a uniform summary of the degree of automation of the many semantic annotation tools that were previously investigated can now be presented.

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“…El poblado de ontologías hace referencia al descubrimiento, a partir de los textos, de individuos de clases, relaciones entre los individuos y su representación en el modelo ontológico. Diversos trabajos han sido propuestos en este ámbito [11], [5] y [12] por ello se ha dedicado la Sección 3 de este trabajo a revisar el estado del arte relacionado al poblado de ontologías, particularmente del dominio académico y de perfiles profesionales.…”

Section: Poblado De Ontologías a Partir De Textosunclassified

“…El tipo de enfoque y nivel de análisis de PLN aplicado a los textos de entrada con la finalidad de extraer el conocimiento para el poblado de las ontologías depende del idioma del dominio. En este ámbito, se han identificado trabajos que utilizan un análisis ligero de los textos con un enfoque estadístico como en [4]; enfoques basados en dependencias sintáctica con un análisis superficial han sido propuestos en [5,6,7]; semántico o heurístico basado en reglas [8,9,10,11,20,21,23] y un enfoque lingüístico con un análisis ligero de los textos ha sido presentado en [24].…”

Section: Trabajos Relacionadosunclassified

“…En el contexto del aprendizaje de elementos ontológicos, el poblado de ontologías hace referencia a extraer y representar, de manera automática o semiautomática, los individuos de clase, relaciones entre los individuos y valores de las propiedades. En la literatura, existen enfoques ligeros para el poblado de ontologías basados en modelos estadísticos-sintácticos ( [4,5,6,7]) y enfoques semánticos basados en reglas ( [8,9,10]), estos no satisfacen el poblado ya que desatienden el conocimiento lingüístico profundo de los textos.…”

Section: Introductionunclassified

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Poblado automático de ontologías de perfiles académicos a partir de textos en español

Reyes‐Ortiz¹,

Bravo²,

Herrera³

et al. 2015

RCS

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Resumen. Este artículo propone un enfoque para el poblado automático de ontologías de perfiles académicos a partir de los textos, expedientes curriculares y resúmenes, de publicaciones científicas en español. El enfoque utiliza reglas semánticas y marcadores lingüísticos para extraer los individuos de clase, relaciones y valores de propiedad. Una evaluación ha sido realizada con un conjunto de individuos gold standard, en términos de precisión y exhaustividad para el poblado de elementos ontológicos.Palabras clave: poblado automático de ontologías, reglas semánticas, marcadores lingüísticos, procesamiento de lenguaje natural (PLN).

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A Confidence–Weighted Metric for Unsupervised Ontology Population from Web Texts

Cited by 7 publications

References 10 publications

Heavy weight ontology learning using text documents

Heavy weight ontology learning using text documents

Unsupervised Approaches for Textual Semantic Annotation, A Survey

Poblado automático de ontologías de perfiles académicos a partir de textos en español

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