Technology of training a neural-network model for ontology population

The paper considers one of the subtasks of ontology learning - the ontology population, which implies the extension of existing ontology by new instances without changing the structure of its classes and relations. A brief overview of existing ontology learning approaches is presented. A highly automated technology for ontology population based on training and application of the neural-network language model to identify and extract potential instances of ontology classes from domain texts is proposed. The main stages of its application, as well as the results of its experimental evaluation and the main directions of its further improvement are considered.

Training set augmentation in training neural- network language model for ontology population

This paper is a continuation of the research focused on solving the problem of ontology population using training on an automatically generated training set and the subsequent use of a neural-network language model for analyzing texts in order to discover new concepts to add to the ontology. The article is devoted to the text data augmentation - increasing the size of the training set by modification of its samples. Along with this, a solution to the problem of clarifying concepts (i.e. adjusting their boundaries in sentences), which were found during the automatic formation of the training set, is considered. A brief overview of existing approaches to text data augmentation, as well as approaches to extracting so-called nested named entities (nested NER), is presented. A procedure is proposed for clarifying the boundaries of the discovered concepts of the training set and its augmentation for subsequent training a neural-network language model in order to identify new concepts of ontology in the domain texts. The results of the experimental evaluation of the trained model and the main directions of further research are considered.

Approach to automatic population of ontologies of scientific subject domain using lexico-syntactic patterns

This paper presents an approach to automatic population of ontologies of a scientific subject domain (SSD) using Lexico-Syntactic Patterns (LSPs) and a corpus of texts related to modeled domain. The main feature of this approach is that such patterns are automatically built based on Ontology Design Patterns of other types provided by the system for the automated development of SSD ontologies using heterogeneous Ontology Design Patterns. The implementation of the ontology population using constructed LSPs is described in detail. The results of the experiments on the SSD ontology population are presented. It is noted that there is a problem in establishing a subject of a relation when extracting facts. To address this problem, the authors are planning to employ the coreference resolution methods.

A generic metamodel for data extraction and generic ontology population

As the next step in the development of intelligent computing systems is the addition of human expertise and knowledge, it is a priority to build strong computable and well-documented knowledge bases. Ontologies partially respond to this challenge by providing formalisms for knowledge representation. However, one major remaining task is the population of these ontologies with concrete application. Based on Model-Driven Engineering principles, a generic metamodel for the extraction of heterogeneous data is presented in this article. The metamodel has been designed with two objectives, namely (1) the need of genericity regarding the source of collected pieces of knowledge and (2) the intent to stick to a structure close to an ontological structure. As well, an example of instantiation of the metamodel for textual data in chemistry domain and an insight of how this metamodel could be integrated in a larger automated domain independent ontology population framework are given.