The COVID-19 pandemic has significantly impacted the global population, and understanding the spread and impact of the disease is crucial to managing the crisis. In this paper, we propose the Link Ontology Analytics approach for creating an RDF (Resource Description Framework) dataset of COVID-19 data to facilitate knowledge discovery and improve the decision-making process. The dataset includes confirmed cases, deaths, recoveries, and testing data from various sources. First step pre-processes the collected big data, using Natural Language Processing (NLP) techniques to extract relevant information from unstructured data sources. Second step uses rule mining to retrieve HeathCare Rules and ontologies to represent the big data in a consistent format and to provide a common vocabulary for querying the big data. Third, our resulting dataset provides a comprehensive and structured representation of COVID-19 data that can be used to analyze HeathCare Rules and relationships related to the spread and impact of the disease. We evaluate the dataset by performing various queries and analyzing the results to demonstrate its usefulness for knowledge discovery and decision-making.

The COVID-19 pandemic has generated massive data in the healthcare sector in recent years, encouraging researchers and scientists to uncover the underlying facts. Mining interesting patterns in the large COVID-19 corpora is very important and useful for the decision makers. This paper presents a novel approach for uncovering interesting insights in large datasets using ontologies and BERT models. The research proposes a framework for extracting semantically rich facts from data by incorporating domain knowledge into the data mining process through the use of ontologies. An improved Apriori algorithm is employed for mining semantic association rules, while the interestingness of the rules is evaluated using BERT models for semantic richness. The results of the proposed framework are compared with state-of-the-art methods and evaluated using a combination of domain expert evaluation and statistical significance testing. The study offers a promising solution for finding meaningful relationships and facts in large datasets, particularly in the healthcare sector.

Semantic feedback is an important source of information that a parser could use to deal with local ambiguities in syntax. However, it is difficult to devise a systematic communication mechanism for interactive syntax and semantics. In this article, I propose a variant of left-corner parsing to define the points at which syntax and semantics should interact, an account of grammatical relations and thematic roles to define the content of the communication, and a conflict resolution strategy based on independent preferences from syntax and semantics. The resulting interactive model has been implemented in a program called COMPERE and shown to account for a wide variety of psycholinguistic data on structural and lexical ambiguities.