Towards scott domains-based topological ontology models

Fernández‐Breis, Jesualdo Tomás; Castellanos–Nieves, Dagoberto; Valencia‐García, Rafael; Vivancos-Vicente, Pedro José; Béjar, Rodrigo Martínez; Heras-González, Manuel De las

doi:10.1145/505168.505181

Cited by 3 publications

(2 citation statements)

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“…In the framework of the algebraic approach to work with ontologies, theory of formal concept analy sis is usually applied, which makes it possible to enrich the ontology with new concepts [2]. The Scott domain theory was used for enriching ontologies with topolog ical relations [3].…”

Section: Introductionmentioning

confidence: 99%

Ontology population as algebraic information system processing based on multi-agent natural language text analysis algorithms

Garanina

Sidorova

2015

Program Comput Soft

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The paper presents an approach to ontology population as operations with Scott information sys tem. The deducibility relation in the ontology population information system corresponds to rules of input data processing and ontology population. To implement an ontology population process, we suggest a multi agent approach based on natural language semantic analysis. In the proposed multi agent model, agents of the following two types interact: information agents corresponding to meaningful units of the information being retrieved and rule agents implementing population rules of the given ontology based on the semanticsyntactic model of the language.

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Section: Introductionmentioning

confidence: 99%

Ontology population as algebraic information system processing based on multi-agent natural language text analysis algorithms

Garanina

Sidorova

2015

Program Comput Soft

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“…Due to the nature of an ontology, there is not a unique (valid) manner for defining ontologies [8]. Moreover, several definitions have historically been given to the term ontology, although an ontology is commonly considered to be an enumeration of the relevant concepts in an application area, as well as a definition of classes of concepts and relationships among these classes [4]. A series of functions to capture knowledge has been implemented to represent the knowledge acquired through ontologies.…”

Section: Introductionmentioning

confidence: 99%

An Approach for Ontology Building from Text Supported by NLP Techniques

Valencia‐García

Castellanos–Nieves

Vicente

et al. 2004

Current Topics in Artificial Intelligence

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Abstract. In this work, we present an approach to simplify Knowledge Acquisition Processes (KAPs) by means of extracting knowledge directly from natural language texts. The ultimate goal is to acquire knowledge straight from experts' language. This approach uses a morphologic analyzer to improve the setting-ina-context between knowledge elements (e.g., concepts and attributes). Another objective is achieving language independency. Here, the knowledge acquired from texts is represented by means of ontologies.

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From Big Data to Knowledge: An Ontological Approach to Big Data Analytics

Kuiler¹

2014

Review of Policy Research

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The introduction of Big Data sets in the healthcare domain has presented opportunities to engage in analytics of very large sets containing both structured and unstructured data. With advances in information technology (IT), these data sets have become available from diverse sources at greatly increased rates. The availability of Big Data sets has introduced complexities that we must address, not only in terms of semantics and analytics but also in terms of data management, storage, and distribution. Currently, the capabilities to ingest, analyze, and manage multipetabyte data sets have underscored the limitations of our analytics capabilities supported by relational database management systems. This essay argues that an ontology‐based approach to data analytics provides a practical framework to address the semantic challenges presented by Big Data sets. No ontological framework can address the operational and management requirements introduced by the availability of Big Data sets, however. There are also a number of IT architectural factors that must be considered in implementing such a framework.

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Towards scott domains-based topological ontology models

Cited by 3 publications

References 6 publications

Ontology population as algebraic information system processing based on multi-agent natural language text analysis algorithms

Ontology population as algebraic information system processing based on multi-agent natural language text analysis algorithms

An Approach for Ontology Building from Text Supported by NLP Techniques

From Big Data to Knowledge: An Ontological Approach to Big Data Analytics

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