An Automatic Ontology Generation Framework with An Organizational Perspective
This work addresses the need for scalable, high-quality knowledge representation in AI and semantic web applications, though it appears incremental by combining existing KG and ontology approaches.
The paper tackles the problem of automatically generating domain-independent ontologies from unstructured text, addressing limitations in existing systems like domain specificity and manual intervention, by proposing a framework that converts text into KGs, refines them, and integrates dynamic KG features with ontology quality.
Ontologies have been known for their semantic representation of knowledge. ontologies cannot automatically evolve to reflect updates that occur in respective domains. To address this limitation, researchers have called for automatic ontology generation from unstructured text corpus. Unfortunately, systems that aim to generate ontologies from unstructured text corpus are domain-specific and require manual intervention. In addition, they suffer from uncertainty in creating concept linkages and difficulty in finding axioms for the same concept. Knowledge Graphs (KGs) has emerged as a powerful model for the dynamic representation of knowledge. However, KGs have many quality limitations and need extensive refinement. This research aims to develop a novel domain-independent automatic ontology generation framework that converts unstructured text corpus into domain consistent ontological form. The framework generates KGs from unstructured text corpus as well as refine and correct them to be consistent with domain ontologies. The power of the proposed automatically generated ontology is that it integrates the dynamic features of KGs and the quality features of ontologies.