A survey of ontology learning techniques and applications

Abstract

Ontologies have gained a lot of popularity and recognition in the semantic web because of their extensive use in Internet-based applications. Ontologies are often considered a fine source of semantics and interoperability in all artificially smart systems. Exponential increase in unstructured data on the web has made automated acquisition of ontology from unstructured text a most prominent research area. Several methodologies exploiting numerous techniques of various fields (machine learning, text mining, knowledge representation and reasoning, information retrieval and natural language processing) are being proposed to bring some level of automation in the process of ontology acquisition from unstructured text. This paper describes the process of ontology learning and further classification of ontology learning techniques into three classes (linguistics, statistical and logical) and discusses many algorithms under each category. This paper also explores ontology evaluation techniques by highlighting their pros and cons. Moreover, it describes the scope and use of ontology learning in several industries. Finally, the paper discusses challenges of ontology learning along with their corresponding future directions.

Building Ontology-Driven Tutoring Models for Intelligent Tutoring Systems Using Data Mining

“…Association Rule Mining (ARM) has been used in scientific literature to build or enrich an existing ontology. Asim et al [47] in a recent survey on ontology learning techniques analyzed some works regarding the use of ARM in the ontology building process. The authors argue that ARM is usually used to find non-taxonomic relations that can enrich an existing ontology.…”

“…Paiva et al [50] and Ghezaiel et al [51] have proposed approaches based on Frequent Pattern growth algorithm to enrich existing ontologies. What seems to emerge by this analysis is that, although approaches able to build an ontology from unstructured corpora could have a great utility in the ontology learning process, they are still far from achieving very good results [46], [47]. As also highlighted by Asim et al [47] and Singh et al [52] the quality of the learned ontologies is usually improved by means of human intervention.…”

Uso de ontologías y tecnologías de la web semántica para la integración, recuperación e intercambio de conocimiento de arquitecturas de software

“…A partir de esas fuentes se trabajará en la identificación de los conceptos, relaciones, taxonomías y se definirán las reglas que se establecen entre los conceptos y relaciones. Se explorará la posibilidad de automatizar la generación de las ontologías y su población, empleando técnicas de ontology learning (aprendizaje automático de ontologías) (Asim et al, 2018). A partir de ello, se avanzará en la propuesta de un marco de trabajo, basado en una red de ontologías, que posibilite la integración de conocimiento de arquitecturas de software (CAS) proveniente de fuentes de conocimiento heterogéneas, tanto internas como externas a la organización que desea obtener y recuperar tal conocimiento.…”

A Method for Extending Ontologies with Application to the Materials Science Domain

“…Therefore, most of the related work comes from that area. For instance, a recent survey (Asim et al 2018) discusses 140 research papers. Different techniques can be used for concept and relationship extraction.…”