Domain Ontology Induction Using Word Embeddings

Gupta, N.K.; Podder, Sanjay; Annervaz, K. M.; Sengupta, Shamik

doi:10.1109/icmla.2016.0027

Cited by 5 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In learning and extending ontologies, ANNs have been used mainly for identifying concepts, attributes, and their relations [9][10][11]. Regarding relations, some ANN-based methods have been developed specifically for subsumption relations needed for taxonomy creation [12][13][14][15].…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Keeping a human in the loop for labeling is not a new idea. Gupta et al [11] use human assistance in naming clusters of concepts. For naming, they check whether some elements of the concept clusters are present in WordNet [31] and propose the name to the user.…”

Section: Bringing the Human In The Loopmentioning

confidence: 99%

“…Finally, the different concepts are arranged into a hierarchical structure. Word embedding is a popular method for discovering the hierarchal structure of concepts [11,15,39].…”

Section: Introductionmentioning

confidence: 99%

Taxonomizer: Interactive Construction of Fully Labeled Hierarchical Groupings from Attributes of Multivariate Data

Mahmood

Mueller

2020

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Figure 1: The housing dataset for Kings County displayed using Taxonomizer. The user interface consists of six coordinated views. (a) The semantic space. (b) The data space. (c) The hierarchy built from combining information spaces (a) and (b). (d) The cophenetic correlation plot which allows users to specify the weight of (a) and (b) to generate (c). (e) The control panel gives the user various options to manipulate the structure of the hierarchy. (f) The word suggestion panel gives suggestions for labeling the nodes of the hierarchy.Abstract-Organizing multivariate data spaces by their dimensions or attributes can be a rather difficult task. Most of the work in this area focuses on the statistical aspects such as correlation clustering, dimension reduction, and the like. These methods typically produce hierarchies in which the leaf nodes are labeled by the attribute names while the inner nodes are often represented by just a statistical measure and criterion, such as a threshold. This makes them difficult to understand for mainstream users. Taxonomies in science, biology, engineering, etc. on the other hand, are easy to comprehend since they provide meaningful labels at the inner nodes as well. Labeling inner nodes of taxonomies automatically requires the identification of hypernyms. Our proposed framework, called Taxonomizer, takes a visual analytics approach to meet this challenge. It appeals to the wisdom of humans to liaise with state of the art data analytics, neural word embeddings, and lexical databases. It consists of a set of visual tools that starts out with an automatically computed hierarchy where the leaf nodes are the original data attributes, and it then allows users to sculpt high-quality taxonomies for any multivariate dataset.

show abstract

“…Recently, word embeddings representations are involved in the task of knowledge hierarchical organization [6], [30], [8]. However, these methods are only considering hypernymhyponym relationship extraction between lexical terms, using word embeddings.…”

Section: Related Workmentioning

confidence: 99%

“…For example the DBpedia Ontology [11], which is a central hub for many applications in the Semantic Web domain [23], has been manually created based on the most commonly used infoboxes within Wikipedia. Many recent studies propose automatic extraction of class hierarchies [8,10,27]. The importance of automatic approaches for the induction class hierarchy becomes more apparent when we deal with large scale automatically acquired knowledge bases such as the WebIsA database (WebIsADb) [24].…”

Section: Introductionmentioning

confidence: 99%

Large-scale taxonomy induction using entity and word embeddings

Ristoski

Faralli

Ponzetto

et al. 2017

Proceedings of the International Conference on Web Intelligence

View full text Add to dashboard Cite

Taxonomies are an important ingredient of knowledge organization, and serve as a backbone for more sophisticated knowledge representations in intelligent systems, such as formal ontologies. However, building taxonomies manually is a costly endeavor, and hence, automatic methods for taxonomy induction are a good alternative to build large-scale taxonomies. In this paper, we propose TIEmb, an approach for automatic unsupervised class subsumption axiom extraction from knowledge bases using entity and text embeddings. We apply the approach on the WebIsA database, a database of subsumption relations extracted from the large portion of the World Wide Web, to extract class hierarchies in the Person and Place domain.

show abstract

Domain Ontology Induction Using Word Embeddings

Cited by 5 publications

References 15 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

Taxonomizer: Interactive Construction of Fully Labeled Hierarchical Groupings from Attributes of Multivariate Data

Large-scale taxonomy induction using entity and word embeddings

Contact Info

Product

Resources

About