H.3. Artificial Intelligence
Afrooz Moradbeiky; Farzin Yaghmaee
Abstract
Knowledge graphs are widely used tools in the field of reasoning, where reasoning is facilitated through link prediction within the knowledge graph. However, traditional methods have limitations, such as high complexity or an inability to effectively capture the structural features of the graph. The ...
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Knowledge graphs are widely used tools in the field of reasoning, where reasoning is facilitated through link prediction within the knowledge graph. However, traditional methods have limitations, such as high complexity or an inability to effectively capture the structural features of the graph. The main challenge lies in simultaneously handling both the structural and similarity features of the graph. In this study, we employ a constraint satisfaction approach, where each proposed link must satisfy both structural and similarity constraints. For this purpose, each constraint is considered from a specific perspective, referred to as a view. Each view computes a probability score using a GRU-RNN, which satisfies its own predefined constraint. In the first constraint, the proposed node must have a probability of over 0.5 with frontier nodes. The second constraint computes the Bayesian graph, and the proposed node must have a link in the Bayesian graph. The last constraint requires that a proposed node must fall within an acceptable fault. This allows for N-N relationships to be accurately determined, while also addressing the limitations of embedding. The results of the experiments showed that the proposed method improved performance on two standard datasets.
M. Shokohi nia; A. Dideban; F. Yaghmaee
Abstract
Despite success of ontology in knowledge representation, its reasoning is still challenging. The most important challenge in reasoning of ontology-based methods is improving realization in the reasoning process. The time complexity of the realization problem-solving process is equal to that of NEXP Time. ...
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Despite success of ontology in knowledge representation, its reasoning is still challenging. The most important challenge in reasoning of ontology-based methods is improving realization in the reasoning process. The time complexity of the realization problem-solving process is equal to that of NEXP Time. This can be done by solving the subsumption and satisfiability problems. On the other hand, uncertainty and ambiguity in these characteristics are unavoidable. Considering these requirements, use of the Fuzzy theory is necessary. This study proposed a method for overcoming this problem, which offers a new solution with a suitable time position. The purpose of this study is to model and improve reasoning and realization in an ontology using Fuzzy-Colored Petri Nets (FCPNs).To this end, an algorithm is presented for improve the realization problem. Then, unified modelling language (UML) class diagram is used for standard description and representing efficiency characteristics; RDFS representation is converted to UML diagram. Then fuzzy concepts in Fuzzy-colored Petri nets are further introduced. In the next step, an algorithm is presented to convert ontology description based on UML class diagram to an executive model based on FCPNs. Using this approach, a simple method is developed to from an executive model and reasoning based on FCPNs which can be employed to obtain the results of interest by applying different queries. Finally, the efficiency of the proposed method is evaluated with the results indicating the improve the performance of the proposed method from different aspects.
