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

A Risk Assessment Framework of Hybrid Offshore Wind–Solar PV Power Plants under a Probabilistic Linguistic Environment

Hybrid offshore wind–solar PV power plants have attracted much attention in recent years due to its advantages of saving land resources, high energy efficiency, high power generation efficiency, and stable power output. However, due to the project still being in its infancy, investors will face a series of risks. Hence, a multi-criteria group decision-making framework for hybrid offshore wind–solar PV power plants risk assessment is constructed in this paper. Firstly, 19 risk indicators are identified and divided into five groups. Secondly, probabilistic linguistic term sets are then introduced to evaluate the criteria values to depict uncertainty and fuzziness. Thirdly, the expert weight determination model is built by combining subjective and objective weights based on expert information, the entropy and interaction-entropy measures of probabilistic linguistic term sets. Fourthly, the expert evaluation information is aggregated by transforming probabilistic linguistic term sets into triangular fuzzy numbers based on generalized weighted ordered weighted averaging operator. Additionally, the risk level is determined using the fuzzy synthetic evaluation method. Finally, the proposed method is applied to a case study and the risk level is slightly high with the similarity measure result of 0.938. Then, the risk indicator system and corresponding countermeasures can provide scientific reference for investment decisions and risk prevention