BioOntoVerb: A top level ontology based framework to populate biomedical ontologies from texts

Ruiz-Martínez, Juana María; Valencia‐García, Rafael; Martı́nez-Béjar, Rodrigo; Hoffmann, Achim

doi:10.1016/j.knosys.2012.06.002

Cited by 39 publications

(8 citation statements)

References 41 publications

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“…En los estudios de Petasis [37], Ruíz-Martínez [40] y Lubani [30] encontramos revisiones de los métodos más utilizados para afrontar el poblado de ontologías.…”

Section: Ontologías Minería De Textos Y Razonamiento Basado En Casosunclassified

SIAMES: Social Impact Advisor and Measurement System

Marin

García

2021

IEEE Technol. Soc. Mag.

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“…En los estudios de Petasis [37], Ruíz-Martínez [40] y Lubani [30] encontramos revisiones de los métodos más utilizados para afrontar el poblado de ontologías.…”

Section: Ontologías Minería De Textos Y Razonamiento Basado En Casosunclassified

SIAMES: Social Impact Advisor and Measurement System

Marin

García

2021

IEEE Technol. Soc. Mag.

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“…OL&P researchers have used natural language processing, machine learning, logic-based, and various other techniques to achieve their respective goals [18] [20] [21] [22]. Some of the OL&P systems are [23], [24], [25], [26].…”

Section: Ontology Learning From Textmentioning

confidence: 99%

Extracting Conceptual Knowledge from Natural Language Text Using Maximum Likelihood Principle

Sharma¹,

Sodhi²

2019

Preprint

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Domain-specific knowledge graphs constructed from natural language text are ubiquitous in today's world. In many such scenarios the base text, from which the knowledge graph is constructed, concerns itself with practical, on-hand, actual or ground-reality information about the domain. Product documentation in software engineering domain are one example of such base texts. Other examples include blogs and texts related to digital artifacts, reports on emerging markets and business models, patient medical records, etc. Though the above sources contain a wealth of knowledge about their respective domains, the conceptual knowledge on which they are based is often missing or unclear. Access to this conceptual knowledge can enormously increase the utility of available data and assist in several tasks such as knowledge graph completion, grounding, querying, etc. Additionally, this conceptual knowledge can also be used to find common semantics across different knowledge graphs. Our contributions in this paper are twofold. First, we propose a novel Markovian stochastic model for document generation from conceptual knowledge. This model is based on the assumption that a writer generates successive sentences of a text only based on her current context as well as the domain-specific conceptual knowledge in her mind. The uniqueness of our approach lies in the fact that the conceptual knowledge in the writer's mind forms a component of the parameter set λ of our stochastic model. Secondly, we solve the inverse problem of learning the best conceptual knowledge from a given document O, by finding model parameters λ * which maximize the likelihood of generating document O over all possible parameter values. This likelihood maximization is done using an application of Baum-Welch algorithm, which is a known special case of Expectation-Maximization (EM) algorithm. We run our conceptualization algorithm on several well-known natural language sources and obtain very encouraging results. The results of our extensive experiments concur with the hypothesis that the information contained in these sources has a well-defined and rigorous underlying conceptual structure, which can be discovered using our method.

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“…A method for ontologies population using Google snippets is proposed in [27], the research is based on manual patterns for class assignment and relations, these patterns are consulted at Google where they are analyzed for obtaining new instances. Another method for automated population is proposed in [28], the methodology combines traditional linguistic analysis and technologies for the extraction of textual knowledge; it is based on contextual distance and knowledge gained with semantic roles.…”

Section: Populationmentioning

confidence: 99%

A Proposal for Domain Ontological Learning

Alemán¹,

Somodevilla²,

Vilariño³

2017

RCS

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In this paper, the population ontology problem is addressed and a semiautomatic methodology for ontology learning is proposed. In this work a study of advances in this research area is presented, which is divided according with the aim of the ontology and the class extraction, creation, or population. Based on this study, an initial proposal for ontology semiautomatic population is introduced. This initial proposal consists of four general steps: class extraction, creation, population and evaluation, where the population step is the main objective of this research. As future work, this methodology is intended to be applied to pedagogic domain, specially to classroom learning tools.

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BioOntoVerb: A top level ontology based framework to populate biomedical ontologies from texts

Cited by 39 publications

References 41 publications

SIAMES: Social Impact Advisor and Measurement System

SIAMES: Social Impact Advisor and Measurement System

Extracting Conceptual Knowledge from Natural Language Text Using Maximum Likelihood Principle

A Proposal for Domain Ontological Learning

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