An Automatic Ontology Generation Framework with An Organizational Perspective

Ontologies have been known for their powerful semantic representation of knowledge. However, 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

Ontology-based approach to semantically enhanced question answering for closed domain: a review

Abstract: For many users of natural language processing (NLP), it can be challenging to obtain concise, accurate and precise answers to a question. Systems such as question answering (QA) enable users to ask questions and receive feedback in the form of quick answers to questions posed in natural language, rather than in the form of lists of documents delivered by search engines. This task is challenging and involves complex semantic annotation and knowledge representation. This study reviews the literature detailing ontology-based methods that semantically enhance QA for a closed domain, by presenting a literature review of the relevant studies published between 2000 and 2020. The review reports that 83 of the 124 papers considered acknowledge the QA approach, and recommend its development and evaluation using different methods. These methods are evaluated according to accuracy, precision, and recall. An ontological approach to semantically enhancing QA is found to be adopted in a limited way, as many of the studies reviewed concentrated instead on NLP and information retrieval (IR) processing. While the majority of the studies reviewed focus on open domains, this study investigates the closed domain

An enhanced term weighting scheme method of identifying and extracting terms for ontology learning and development

Social media is crucial in facilitating the Disaster Management (DM) communication process. However, the knowledge representation of DM Social Media (DMSM) is inadequate especially in ontology representation. Given to huge volume of DMSM unstructured text, information extraction for ontology development is achieved through text mining. However, existing works on text mining-based ontology development utilizes a well-known unsupervised scheme, TF-IDF that ignore document distribution and leads to high dimensionality of features. The main objectives of the study are to improve ontology development by enhancing supervised term weighting scheme (TWS) and developing DMSM ontology. The enhancement is achieved by identifying the existing supervised TWS and giving higher weightage to the positive category instead of the negative one, which results in the removal of irrelevant terms. The study is conducted by gathering DMSM scientific publications, performing pre-processing, and calculating the eight selected supervised TWS. All the schemes obtained high weightage on the negative category, instead of the positive category. An enhancement is performed by introducing a positive term frequency ratio and positive category ratio, whereby the enhanced schemes extract relevant terms to the positive category. The DMSM ontology is generated and evaluated using a gold-standard-based evaluation method for syntactic comparison, designing the ontology, and evaluating the learned ontology. From the results, it is found that good score is achieved for TF. IDFEC-based. Enhanced and TF. RF. Enhanced with 93.33% and 91.03% for precision, 80.8% and 78.02% for recall, and 0.87 and 0.84 for F-measure, respectively. Theoretically, this study contributes an enhanced supervised TWS by emphasizing the classification information of a corpus, hence features dimensionality can be reduced and boosts the importance of words that are distributed between the positive and the negative class. Practically the enhanced scheme provides an improved technique for ontology developers to extract relevant terms from unstructured scientific publication text especially for DMSM domain