Using Artificial Neural Networks to Determine Ontologies Most Relevant
  to Scientific Texts
This paper provides an insight into the possibility of how to find ontologies
most relevant to scientific texts using artificial neural networks. The basic
idea of the presented approach is to select a representative paragraph from a
source text file, embed it to a vector space by a pre-trained fine-tuned
transformer, and classify the embedded vector according to its relevance to a
target ontology. We have considered different classifiers to categorize the
output from the transformer, in particular random forest, support vector
machine, multilayer perceptron, k-nearest neighbors, and Gaussian process
classifiers. Their suitability has been evaluated in a use case with ontologies
and scientific texts concerning catalysis research. From results we can say the
worst results have random forest. The best results in this task brought support
vector machine classifier.
Lukáš Korel, Alexander S. Behr, Norbert Kockmann, Martin Holeňa
# Using Artificial Neural Networks to Determine Ontologies Most Relevant to Scientific Texts

###### Abstract

This paper provides an insight into the possibility of how to find ontologies most relevant to scientific texts using artificial neural networks. The basic idea of the presented approach is to select a representative paragraph from a source text file, embed it to a vector space by a pre-trained fine-tuned transformer, and classify the embedded vector according to its relevance to a target ontology. We have considered different classifiers to categorize the output from the transformer, in particular random forest, support vector machine, multilayer perceptron, k-nearest neighbors, and Gaussian process classifiers. Their suitability has been evaluated in a use case with ontologies and scientific texts concerning catalysis research. The obtained results confirm support vector machines as a promising classifier, but surprisingly show random forest as unsuitable for this task.

_Keywords:_ ontology; text data; text preprocessing; text representation learning; text classification

## 1 Introduction

A domain ontology defines a set of representational primitives with which to model a domain of knowledge or discourse. The representational primitives are typically classes, attributes, and relationships. The definitions of the representational primitives include information about their meaning and constraints on their logically consistent application. Classes can be defined in two ways: by annotating their definitions, or by connecting classes with each other and with properties. Each domain ontology typically uses domain-specific definitions of terms denoting its primitives.

The FAIR research data management (Findable, Accessable, Interoperable, and Reuseable) needs a consistent data representation in ontologies, particularly for representing the data structure in the specific domain [34]. Since different ontologies are written by different people, they are often incompatible, even within the same domain. As systems that rely on domain ontologies expand, it is often needed to merge domain ontologies by manual tuning. The same is true for enhancing an ontology with information available in domain-related texts. Merging and enhancing ontologies is thus a largely manual process and therefore time-consuming and expensive.

The need to find a suitable ontology for an input text can help in classifying the information presented within the text as well as to connect the input text with data. This would allow for automated selection of ontologies and respective classification of the text. Different text data could thus be compared automatically in an understandable way and connected with corresponding research data. Ontologies represent "a formal specification of a shared conceptualization" [7] and can thus be used to express knowledge and data in a formalized, standardized description language to specify terms and relations between those terms.

Current ontology recommenders, such as the NCBO ontology recommender [8], score annotations based on words similar to preferred and alternate labels of ontology classes and term frequency. In contrast to this, this work aims to use text representation learning in order to not only search for words also contained in ontologies but also to find concepts with similar semantic meaning between text and ontology.

This paper is devoted to a specific problem encountered during enhancing ontologies and sometimes during their merging: to decide which of several available ontologies is most relevant to given domain-related piece of text. Our solution to the problem relies primarily on artificial neural networks (ANNs), in particular on natural language processing (NLP).

The next section surveys the applicability of artificial neural networks to ontologies. Section 3 recalls the employed methods of text preprocessing. There have been used modules for text extractions from PDF files, for transforming extracted files to pure text and for eliminating irrelevant paragraphs. In the section, text representation learning is described as well as the principles of the employed classifiers. In section 4, an application of the proposed methodology to catalysis is described and evaluated.

With regard to sources we have studied described in part 2 of this article, we are not aware that classifiers learned from the results of representational learning have ever been used to determine the most relevant of a given set of ontologies.