A method for using temporal reasoners to answer the history of science questions

Bahadorani, Bahareh; Zaeri, Ahmad

doi:10.1007/s41870-019-00373-z

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…The authors of [7] suggest a modular reasoning system that reuses code by using uninterpreted predicates. A collection of publications on the history of medical sciences to find recurrent patterns of queries is examined in [8]. According to the suggested paradigm, the queries are represented in SQWRL, which provides a mechanism for applying temporal reasoners to answer inquiries about the history of science.…”

Section: Literature Reviewmentioning

confidence: 99%

A Method for Improving Reasoning and Realization Problem Solving in Descriptive Logic- Based and Ontology-Based Reasoners

Shokohinia¹,

Dideban²

2022

MJCS

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Recently, many methods have been developed for representing knowledge, reasoning, and result extraction extracting results based on the respective domain knowledge in question. Despite the ontological success in knowledge representation, the reasoning method has faces some challenges. The main challenge in ontology reasoning methods is the failure in solving realization problems in the reasoning process. Apart from the complexity of solving realization problems, this already daunting challenge is compounded by computational complexity the time complexity of the solving realization problem solving process problems is equal to that of NEXP TIME. This important issue problem is achieved solved by solving the subsumption and satisfiability problems. Thus, to solve the realization problem, we first partition the ontology or extract partitions related to the query. Then, the satisfiability problem is solved by extracting partitions, and all concepts related to the query are extracted. This study proposes a method to overcome this problem, where a new solution is proposed with an appropriate time position. Finally, the efficiency of the proposed method, is evaluated against other reasoning engines, and the results show optimized performance vis-a-vis previous studies.

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Section: Literature Reviewmentioning

confidence: 99%