Automated reasoning based user interface

Kapłański, Paweł; Seganti, Alessandro; Cieźliński, Krzysztof; Chrabrowa, Aleksandra; Ługowska, Iwona

doi:10.1016/j.eswa.2016.11.033

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…This model starts with detailed and specific rules and ends with general rules. The authors of [16] describe an automated user interface based on reasoning, a free and opensource Java programming language. This reasoner is an OWL 2 DL reasoner with excellent efficiency on small data sets.…”

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%

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

Shokohinia¹,

Dideban²

2022

MJCS

View full text Add to dashboard Cite

show abstract

“…The former refers to that which is unarticulated, undocumented and held in peoples' heads, while the latter is expressed, structured, codified and accessible for those other than the individuals originating it [32]. Thus, knowledge exists on the spectrum of these extremes and its transformation means moving from one extreme to another [33,34,35].…”

Section: Knowledge Transformationmentioning

confidence: 99%

Dynamical Aspects of Knowledge Evolution

Owoc

Weichbroth

2019

IFIP Advances in Information and Communication Technology

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Undeniably, in this rapidly changing world some knowledge granules change over time. Tracking these changes seems to be one of the most crucial processes in knowledge management. Every potential change is a result of knowledge adoption and application to solve a given problem or task in particular domains. However, there is a lack of a model that provides an eventdriven framework, along with the core adoption process explicitly expressed with related factors, which together serve as an efficient tool to adopt and reuse knowledge on one hand, while on the other, to measure and evaluate the various aspects of knowledge quality and usefulness. This paper aims to fill this gap by introducing a knowledge adoption process and an ontology-aided encapsulation knowledge (OAKE) model. While the former breaks down the tacit adoption process into two explicit sub-processes and measurable factors, the latter exposes knowledge evolution over time by a sequence of recorded events.

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“…Our study is also driven by a concern for the implications of the human factor due to the increasing evidence of the apprehension raised against the proliferation of artificial intelligence since the variety of its applications seems to be immeasurable, ranging from computer games [6,7], decision-making [8]- [10], education [11]- [16], enterprise management [17]- [19], grid computing [20]- [23], knowledge management [24]- [27], learning systems [28]- [31], ontologies [32]- [34], smart cities [35]- [37], and software engineering [38]- [40], to name just a few. On the contrary, we attempt to argue that AI can amplify educational effectiveness, concerned with sharing, developing and disseminating knowledge, at the same time preserving human autonomy, agency and capabilities.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Technologies in Education: Benefits, Challenges and Strategies of Implementation

Owoc,

Sawicka,

Weichbroth

2021

Preprint

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Since the education sector is associated with highly dynamic business environments which are controlled and maintained by information systems, recent technological advancements and the increasing pace of adopting artificial intelligence (AI) technologies constitute a need to identify and analyze the issues regarding their implementation in education sector. However, a study of the contemporary literature reveled that relatively little research has been undertaken in this area. To fill this void, we have identified the benefits and challenges of implementing artificial intelligence in the education sector, preceded by a short discussion on the concepts of AI and its evolution over time. Moreover, we have also reviewed modern AI technologies for learners and educators, currently available on the software market, evaluating their usefulness. Last but not least, we have developed a strategy implementation model, described by a five-stage, generic process, along with the corresponding configuration guide. To verify and validate their design, we separately developed three implementation strategies for three different higher education organizations. We believe that the obtained results will contribute to better understanding the specificities of AI systems, services and tools, and afterwards pave a smooth way in their implementation.

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Automated reasoning based user interface

Cited by 8 publications

References 12 publications

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

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

Dynamical Aspects of Knowledge Evolution

Artificial Intelligence Technologies in Education: Benefits, Challenges and Strategies of Implementation

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