Ontology Learning Process Research Articles

RWTH-DBIS at LLMs4OL 2024 Tasks A and B

The increasing capabilities of Large Language Models (LLMs) have opened new opportunities for enhancing Ontology Learning (OL), a process crucial for structuring domain knowledge in a machine-readable format. This paper reports on the participation of the RWTH-DBIS team in the LLMs4OL Challenge at ISWC 2024, addressing two primary tasks: term typing and taxonomy discovery. We used LLaMA-3-8B and GPT-3.5-Turbo models to find the performance gaps between open-source and commercial LLMs. For open-source LLMs, our methods included domain-specific continual training, fine-tuning, and knowledge-enhanced prompt-tuning. These approaches were evaluated on the benchmark datasets from the challenge, i.e., GeoNames, UMLS, Schema.org, and the Gene Ontology (GO), among others. The results indicate that domain-specific continual training followed by task-specific fine-tuning enhances the performance of open-source LLMs in these tasks. However, performance gaps remain when compared to commercial LLMs. Additionally, the developed prompting strategies demonstrate substantial utility. This research highlights the potential of LLMs to automate and improve the OL process, offering insights into effective methodologies for future developments in this field.

Learning Non-Taxonomic Relations of Ontologies

Ontologies, as semantic knowledge representation, have a crucial role in various information systems. The main pitfall of manually building ontologies is effort and time-consuming. Ontology learning is a key solution. Learning Non-Taxonomic Relationships of Ontologies (LNTRO) is the process of automatic/semi-automatic extraction of all possible relationships between concepts in a specific domain, except the hierarchal relations. Most of the research works focused on the extraction of concepts and taxonomic relations in the ontology learning process. This article presents the results of a systematic review of the state-of-the-art approaches for LNTRO. Sixteen approaches have been described and qualitatively analyzed. The solutions they provide are discussed along with their respective positive and negative aspects. The goal is to provide researchers in this area a comprehensive understanding of the drawbacks of the existing work, thereby encouraging further improvement of the research work in this area. Furthermore, this article proposes a set of recommendations for future research.

An Overview of Ontology Learning Process from Arabic Text

Ontology learning (OL) Plays an important role in many fields such as semantic web, Data Integration and Interoperability, Machine Translation, and Information Retrieval. The success of any of these fields depends on the quality of its ontologies. This paper presents a comprehensive survey of research on the Ontology Learning for Arabic texts. Most of the previous works focused on three main issues: extracting the terms, extracting the semantic relations, and building the ontology from the Arabic text. There are more of techniques and methods can be used for this process. In this paper, first we present the Arabic challenges that were reasons for developing few Arabic Ontology Learning systems. Second we make a research comparison based on the techniques used and their results. Third, we pointed limitations and comments of research works on Arabic Ontology Learning. Finally, we concluded the paper and outlined our future research direction in this area.

A Case Study on Sepsis Using PubMed and Deep Learning for Ontology Learning.

We investigate the application of distributional semantics models for facilitating unsupervised extraction of biomedical terms from unannotated corpora. Term extraction is used as the first step of an ontology learning process that aims to (semi-)automatic annotation of biomedical concepts and relations from more than 300K PubMed titles and abstracts. We experimented with both traditional distributional semantics methods such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) as well as the neural language models CBOW and Skip-gram from Deep Learning. The evaluation conducted concentrates on sepsis, a major life-threatening condition, and shows that Deep Learning models outperform LSA and LDA with much higher precision.

Property type distribution in Wordnet, corpora and Wikipedia

The ontology learning from text lacks an initial evaluation of the property types present in the resource used in the learning task. We need a way to explore the distribution of property types before the actual ontology learning process. In this paper we propose three algorithms that help ontologists in the preliminary resource exploration. The algorithms are devised for Wordnet, generic corpora and Wikipedia. Minimal assumptions about the property types that can be extracted are made. The algorithms are tested with concepts belonging to five taxonomies. The distribution of property types for the extracted concepts is computed and reported.

Association Rule Mining Based Extraction of Semantic Relations Using Markov Logic Network

Ontology may be a conceptualization of a website into a human understandable, however machinereadable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the intentional aspects of a site, whereas the denotative part is provided by a mental object that contains assertions about instances of concepts and relations. Semantic relation it might be potential to extract the whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations describe the linguistics relationships among the entities involve that is beneficial for a higher understanding of human language. The relation can be identified from the result of concept hierarchy extraction. The existing ontology learning process only produces the result of concept hierarchy extraction. It does not produce the semantic relation between the concepts. Here, we have to do the process of constructing the predicates and also first order logic formula. Here, also find the inference and learning weights using Markov Logic Network. To improve the relation of every input and also improve the relation between the contents we have to propose the concept of ARSRE. This method can find the frequent items between concepts and converting the extensibility of existing lightweight ontologies to formal one. The experimental results can produce the good extraction of semantic relations compared to state-of-art method.

A New Method for Knowledge and Information Management Domain Ontology Graph Model

A new ontology learning model called domain ontology graph (DOG) is proposed in this paper. There are two key components in the DOG, i.e., the definition of the ontology graph and the ontology learning process. The former defines the ontology and knowledge conceptualization model from the domain-specific text documents; the latter offers the necessary method of semiautomatic domain ontology learning and generates the corresponding ontology graphs. Two kinds of ontological operations are also defined based on the proposed DOG, i.e., document ontology graph generation and ontology-graph-based text classification. The simulation studies focused upon Chinese text data are used to demonstrate the potential effectiveness of our proposed strategy. This is accomplished by generating DOGs to represent the domain knowledge and conducting the text classifications based on the generated ontology graph. The experimental results show that the new method can produce significantly better classification accuracy (e.g., with 92.3% in f-measure) compared with other methods (such as 86.8% in f-measure for the term-frequency-inverse-document-frequency approach). The high performance demonstrates that our presented ontological operations based on the ontology graph knowledge model are effectively developed.

Domain ontology graph model and its application in Chinese text classification

This paper proposes an ontology learning method which is used to generate a graphical ontology structure called ontology graph. The ontology graph defines the ontology and knowledge conceptualization model, and the ontology learning process defines the method of semiautomatic learning and generates ontology graphs from Chinese texts of different domains, the so-called domain ontology graph (DOG). Meanwhile, we also define two other ontological operations—document ontology graph generation and ontology graph-based text classification, which can be carried out with the generated DOG. This research focuses on Chinese text data, and furthermore, we conduct two experiments: the DOG generation and ontology graph-based text classification, with Chinese texts as the experimental data. The first experiment generates ten DOGs as the ontology graph instances to represent ten different domains of knowledge. The generated DOGs are then further used for the second experiment to provide performance evaluation. The ontology graph-based approach is able to achieve high text classification accuracy (with 92.3 % in f-measure) over other text classification approaches (such as 86.8 % in f-measure for tf–idf approach). The better performance in the comparative experiments reveals that the proposed ontology graph knowledge model, the ontology learning and generation process, and the ontological operations are feasible and effective.

A New Approach for Automating the Ontology Learning Process Using

A New Approach for Automating the Ontology Learning Process Using

Learning Semantics from Relational Database Schema and Data

Ontology has been proposed to resolve the issues of semantic heterogeneity in data interoperability and data sharing among heterogeneous systems because it provides an effective approach to represent the knowledge about data of a specific domain and has been applied in many fields, such as Semantic Web, e-commerce and information retrieval, etc. However, building ontology for information source manually is not only hard and error-prone, but also very personal if there is no common guideline. We propose a framework for data interoperability using semantic Web service to resolve the semantic heterogeneity in healthcare environment. We found that learning ontology from existing information resources is a good solution to explicitly express the semantics of information source, but some semantics may be missing during the ontology learning process. Since relational database is widely used for storing source data, in this paper a new approach of learning OWL ontology from a relational database is proposed. In order to acquire the complete conceptual information, a group of learning rules are used to obtain OWL ontology, including classes, properties, property characteristics, cardinalities and instances. The proposed learning rules are being implemented as a prototype. KeywordsOntology; Ontology Learning; OWL; Relational Database; Relational Model