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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. [ABSTRACT FROM AUTHOR]

Cognitive agents often acquire knowledge of how devices work by reading a book. We describe a computational theory of understanding a natural language description of a device, comprehending how the device works, and acquiring a device model. The theory posits a complex interplay between language, memory, comprehension, problem-solving and learning faculties. Long-term memory contains cases of previously encountered devices and associated structure-behavior-function (SBF) models that explain how the known device works. Language processing is both bottom-up and top-down. Bottom-up processing is done through spreading-activation networks. Where the semantics of the nodes and links in the network arises from the SBF ontology. The comprehension process constructs a SBF model for the new device by adapting the known device models - we call this process adaptive modeling. This multifaculty computational theory is instantiated in an operational computer system called KA that (i) reads and understands English language descriptions of devices from David Macaulay's popular science book The Way Things Work, (ii) comprehends how the described device works, and (iii) acquires a SBF model for the device.

In this paper, we investigate the interaction between linguistic and ncn-linguistic processes by considering the role of functional reasoning in understanding design specifications written in natural language. W e desaibe KA , an experimental modelbased interpretation and design system which understands English language descriptions of the design problems it solves, and examine whether KA's problem-solving amiabilities help i) ascertain the relevance of ambiguous design ^lecifications and ii) identify unspecified relations between design requirements. Our results demonstrate that augmenting language processing with the ability to reason about function along the lines suggested in K A provides effective solutions to these problems in particular as well as to other problems in natural language understanding.

Traditional Cognitive Science has studied variouscognitive components in isolation. Our project at-tempts to alleviate some of the problems with thisseparation by focusing on the role of problem solv-ing in language comprehension. Specifically, theKA project integrates six areas of current investi-gation in Cognitive Science: knowledge represen-tation, m e m o r y organization, language compre-hension, knowledge acquisition, problem solving,and control architectures. W e are developing amodel-based text interpretation and knowledge ac-quisition system which, when completed, will beable to read and interpret descriptions of physicaldevices, construct models of the devices, and usethe acquired models to solve novel design prob-lems. This paper presents three areas in which weuse problem solving to constrain natural languageunderstanding: (1) the use of mental models asa foundation for both problem solving and natu-ral language understanding, (2) the use of designexperience to influence the understanding process,and (3) the use of the design process to establishthe cost of linguistic decisions.

The development of models of human sentence processing hais traditionally followed one of two paths. Either the model posited a sequence of processing modules, each with its own taskspecific knowledge (e.g., syntax and semantics), or it posited a single processor utilizing different types of knowledge inextricably integrated into a monolithic knowledge base. Our previous work in modeling the sentence processor resulted in a model in which different processing modules used separate knowledge sources but operated in paral el to arrive at the interpretation of a sentence. One highlight of this model is that it offered an explanation of how the sentence processor might recover from an error in choosing the meaning of an ambiguous word: the semantic processor briefly pursued the different interpretations associated with the different meanings of the word in question until additional text confirmed one of them, or until processing limitations were exceeded. Errors in syntactic ambiguity resolution were assumed to be handled in some other way by a separate syntactic module. Recent experimental work by Laurie Stowe strongly suggests that the human sentence processor deals with syntactic error recovery using a mechanism very much like that proposed by our model of semantic error recovery. Another way to interpret Stowe's finding that two significantly different kinds of errors are handled in the same way is this: the human sentence processor consists of a single unified processing module utilizing multiple independent knowledge sources in parallel. A sentence processor built upon this architecture should at times exhibit behavior eissociated with modular approaches, and at other times act like an integrated system. In this paper we explore some of these ideas via a prototype computational model of sentence processing ca led C O M P E R E , and propose a set of psychological experiments for testing our theories.