THE METHOD OF AUTOMATED BUILDING OF DOMAIN ONTOLOGY

This article is devoted to the tasks of automating the construction of domain ontologies. In the beginning, the limitations and problems of constructing the ontology of the domain using the well-known methods are discussed. Next, a model of the domain ontology is proposed, which provides the ability to automatically build the ontological hierarchy, including the automatic synthesis of generalized concepts. Then, the article discusses the method of building an ontology based on the proposed model using machine learning, and discusses its capabilities and limitations

Dynamic ontology for service robots

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyAutomatic ontology creation, aiming to develop ontology without or with minimal human intervention, is needed for robots that work in dynamic environments. This is particularly required for service (or domestic) robots that work in unstructured and dynamic domestic environments, as robots and their human users share the same space. Most current works adopt learning to build the ontology in terms of defining concepts and relations of concepts, from various data and information resources. Given the partial or incomplete information often observed by robots in domestic environments, identifying useful data and information and extracting concepts and relations is challenging. In addition, more types of relations which do not appear in current approaches for service robots such as “HasA” and “MadeOf”, as well as semantic knowledge, are needed for domestic robots to cope with uncertainties during human–robot interaction. This research has developed a framework, called Data-Information Retrieval based Automated Ontology Framework (DIRAOF), that is able to identify the useful data and information, to define concepts according to the data and information collected, to define the “is-a” relation, “HasA” relation and “MadeOf” relation, which are not seen in other works, to evaluate the concepts and relations. The framework is also able to develop semantic knowledge in terms of location and time for robots, and a recency and frequency based algorithm that uses the semantic knowledge to locate objects in domestic environments. Experimental results show that the robots are able to create ontology components with correctness of 86.5% from 200 random object names and to associate semantic knowledge of physical objects by presenting tracking instances. The DIRAOF framework is able to build up an ontology for domestic robots without human intervention

Extracción de instancias de una clase desde textos en lenguaje natural independientes del dominio de aplicación

Las ontologías en computación se incluyen en el mundo de la inteligencia artificial y constituyen representaciones formales de un área de conocimiento o dominio. Las ontologías permiten modelar el conocimiento mediante una estructura de conceptos relacionados, lo cual proporciona un vocabulario común y que es de vital importancia para compartir información. La ingeniería ontológica es la disciplina que se encarga del estudio y construcción de herramientas para agilizar el proceso de creación de ontologías desde el lenguaje natural y tiene tres etapas cruciales: aprendizaje de ontologías (Ontology Learning), población de ontologías (Ontology Population) y enriquecimiento de ontologías (Ontology Enrichment). La literatura especializada muestra gran interés por las tres etapas y, para desarrollarlas, utiliza distintos métodos como estadística, extracción de información, procesamiento de lenguaje natural, aprendizaje de máquina (Machine Learning) y combinaciones entre ellos. Sin embargo, algunos problemas subsisten, tales como la dependencia del dominio de aplicación, la carencia de métodos completamente automáticos y la carencia de identificación de instancias de atributos. En consecuencia, el problema que se aborda en esta Tesis Doctoral es la extracción automática de instancias desde el lenguaje natural, sin importar el dominio de aplicación, con el fin de contribuir con el proceso de población de ontologías. En esta Tesis Doctoral se propone un método computacional que utiliza técnicas de extracción de información y procesamiento de lenguaje natural para extraer instancias de una clase y generar como resultado un archivo con una ontología completa en formato OWL, utilizando la herramienta GATE (General Architecture for Text Engineering). Los resultados son prometedores, pues se logra crear ontologías desde cero automáticamente, sin importar el dominio de aplicación y con buenos niveles de precision, recall y F-measure.Abstract: Ontologies in computation belong to artificial intelligence. Ontologies are formal representations of a knowledge area or domain. Ontologies can be used for modeling knowledge by using a structure of related concepts. Such structure provides a common vocabulary and it is crucial for sharing information. Ontological engineering is a discipline for studying and constructing tools for improving the process of ontology creation from natural language. Such a process has three crucial stages: ontology learning, ontology population, and ontology enrichment. The state of the art shows great concern with the three stages, which are developed by using methods like statistics, information extraction, natural language processing, machine learning, and combinations of them. However, some problems still remain—e.g., dependence on the application domain, lack of automation, and lack of attribute instance identification. Consequently, in this Ph.D. Thesis we address the problem of automated extraction of instances from natural language—regardless of the application domain—in order to contribute to the process of ontology population. In this Ph.D. Thesis we propose a computational method by using information extraction and natural language processing technologies in order to extract instances of a class and to generate as an output a file with a complete ontology in OWL format. We use the GATE (General Architecture for Text Engineering) tool for implementing the method. The results are promising, since we automatically create domain-independent ontologies from scratch. Also, our method exhibits satisfactory levels of precision, recall and F-measureDoctorad