Using ART2 Neural Network and Bayesian Network for Automating the Ontology Constructing Process

Hourali, Maryam; Montazer, Gholam Ali

doi:10.1016/j.proeng.2012.01.594

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…The variety of the types of ANNs employed is quite large. It includes adaptive resonance theory (ART) networks [20] and associative memories [21], as well as multi-layer perceptron (MLP) [22] and the modern deep convolutional neural network (CNN) [16,23], deep belief networks [9], long-short term memory (LSTM) and bididectional long-short term memory (BiLSTM) [24], and gated recurrent units (GRU) networks [25,26]. The dependency of ontologies on texts has led to the use of networks designed for learning texts and natural language representations.…”

Section: Related Workmentioning

confidence: 99%

Text-to-Ontology Mapping via Natural Language Processing with Application to Search for Relevant Ontologies in Catalysis

et al. 2023

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The paper presents a machine-learning based approach to text-to-ontology mapping. We explore a possibility of matching texts to the relevant ontologies using a combination of artificial neural networks and classifiers. Ontologies are formal specifications of the shared conceptualizations of application domains. While describing the same domain, different ontologies might be created by different domain experts. To enhance the reasoning and data handling of concepts in scientific papers, finding the best fitting ontology regarding description of the concepts contained in a text corpus. The approach presented in this work attempts to solve this by selection of a representative text paragraph from a set of scientific papers, which are used as data set. Then, using a pre-trained and fine-tuned Transformer, the paragraph is embedded into a vector space. Finally, the embedded vector becomes classified with respect to its relevance regarding a selected target ontology. To construct representative embeddings, we experiment with different training pipelines for natural language processing models. Those embeddings in turn are later used in the task of matching text to ontology. Finally, the result is assessed by compressing and visualizing the latent space and exploring the mappings between text fragments from a database and the set of chosen ontologies. To confirm the differences in behavior of the proposed ontology mapper models, we test five statistical hypotheses about their relative performance on ontology classification. To categorize the output from the Transformer, different classifiers are considered. These classifiers are, in detail, the Support Vector Machine (SVM), k-Nearest Neighbor, Gaussian Process, Random Forest, and Multilayer Perceptron. Application of these classifiers in a domain of scientific texts concerning catalysis research and respective ontologies, the suitability of the classifiers is evaluated, where the best result was achieved by the SVM classifier.

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Section: Related Workmentioning

confidence: 99%

Text-to-Ontology Mapping via Natural Language Processing with Application to Search for Relevant Ontologies in Catalysis

et al. 2023

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“…In the machine learning approach, adaptive resonance theory 2 (ART 2) neural network with TF-IDF (Chen et al , 2008; Hourali and Ali, 2012), Markov logic networks (Suresu and Elamparithi, 2016), naïve Bayes classifier (Mousavi and Faili, 2017) and extended association rule (Zhou et al , 2016) methods are used to identify relevant concepts from the text. The machine learning approach warrants the requirement of a large training dataset.…”

Section: Literature Survey On Related Workmentioning

confidence: 99%

Method for automatic key concepts extraction

Sudarsan

Meenachi

Venkatesh

et al. 2018

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Purpose Ontology of a domain mainly consists of a set of concepts and their semantic relations. It is typically constructed and maintained by using ontology editors with substantial human intervention. It is desirable to perform the task automatically, which has led to the development of ontology learning techniques. One of the main challenges of ontology learning from the text is to identify key concepts from the documents. A wide range of techniques for key concept extraction have been proposed but are having the limitations of low accuracy, poor performance, not so flexible and applicability to a specific domain. The propose of this study is to explore a new method to extract key concepts and to apply them to literature in the nuclear domain. Design/methodology/approach In this article, a novel method for key concept extraction is proposed and applied to the documents from the nuclear domain. A hybrid approach was used, which includes a combination of domain, syntactic name entity knowledge and statistical based methods. The performance of the developed method has been evaluated from the data obtained using two out of three voting logic from three domain experts by using 120 documents retrieved from SCOPUS database. Findings The work reported pertains to extracting concepts from the set of selected documents and aids the search for documents relating to given concepts. The results of a case study indicated that the method developed has demonstrated better metrics than Text2Onto and CFinder. The method described has the capability of extracting valid key concepts from a set of candidates with long phrases. Research limitations/implications The present study is restricted to literature coming out in the English language and applied to the documents from nuclear domain. It has the potential to extend to other domains also. Practical implications The work carried out in the current study has the potential of leading to updating International Nuclear Information System thesaurus for ontology in the nuclear domain. This can lead to efficient search methods. Originality/value This work is the first attempt to automatically extract key concepts from the nuclear documents. The proposed approach will address and fix the most of the problems that are existed in the current methods and thereby increase the performance.

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“…The engine uses the patterns proposed by Hearst (Hourali and Montazer, 2012) The part presented in BOLD is detected as a relationship and the noun phrases (NP) as its related concepts or instances. Apart from the aforementioned patterns, there are some more patterns which are added to the engine.…”

Section: Automatic Extraction Of Potential Terms From the Competency mentioning

confidence: 99%

An ontology development approach using concept maps driven by automatic term extraction

Iqbal

Murad

Sharef

2017

IJICT

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A number of ontology engineering methodologies have been proposed to date. Distinct methodologies rely on different techniques and activities for developing ontologies. The concept mapping technique has been recently used for developing ontologies related to different domains. However, the existing approaches using concept mapping do not make use of any automatic term extraction process to ease the overall ontology development process and their evaluation procedures are not robust to specifically meet the need for ontology designs emerging from concept maps. This paper proposes a new approach which uses the concept mapping technique coupled with a term extraction engine. The engine automatically extracts the candidate terms from the competency questions which reduces ontology development time and efforts. Furthermore, considering the importance of evaluation, a robust evaluation procedure is proposed which validates the ontologies at both the formal level and the graphical level.

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Using ART2 Neural Network and Bayesian Network for Automating the Ontology Constructing Process

Cited by 8 publications

References 8 publications

Text-to-Ontology Mapping via Natural Language Processing with Application to Search for Relevant Ontologies in Catalysis

Text-to-Ontology Mapping via Natural Language Processing with Application to Search for Relevant Ontologies in Catalysis

Method for automatic key concepts extraction

An ontology development approach using concept maps driven by automatic term extraction

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