38 research outputs found

    Improving Transitive Embeddings in Neural Reasoning Tasks via Knowledge-Based Policy Networks

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    This paper proposes an approach to embed ontologies in order to deal with reasoning based on transitive relations, using the datasets provided for the SemRec Challenge at ISWC 2022. Knowledge Graph Embedding (KGE) methods provide a low-dimensional representation of the entities and relationships extracted from the knowledge graph and have been successfully used for a variety of applications such as question answering, reasoning, inference, and link prediction. However, most KGE methods cannot handle the underlying constraints and characteristics of ontologies, preventing them from performing important reasoning tasks such as subsumption and instance checking. We propose to extend translation-based embedding methods to include subsumption and instance checking reasoning by leveraging transitive relations. Experimental results show that our approach can achieve Hits@10 as high as %73 using samples generated by a policy network

    Knowledge Base Construction from Pre-trained Language Models by Prompt learning

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    Pre-trained language models (LMs) have advanced the state-of-the-art for many semantic tasks and have also been proven effective for extracting knowledge from the models itself. Although several works have explored the capability of the LMs for constructing knowledge bases, including prompt learning, this potential has not yet been fully explored. In this work, we propose a method of extracting factual knowledge from LMs for given subject-relation pairs and explore the most effective strategy to generate blank object entities for each relation of triples. We design prompt templates for each relation using personal knowledge and the descriptive information available on the web such as WikiData. The probing approach of our proposed LMs is tested on the dataset provided by the International Semantic Web Conference (ISWC 2022) LM-KBC Challenge. To cope with the problem of varying performance for each relation, we designed a parameter selection strategy for each relation. Using the test dataset, we obtain an F1-score of 0.4935%, which is higher than the baseline of 31.08%

    Mobile Prisoner's Dilemma game played on diverse habitats

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    Promotion of cooperative behavior in Prisoner's Dilemma (PD) game while players that are allowed to move between different gaming environments (i.e. habitats) is investigated. The stochastic mobile model under study is realized over connected habitats that are situated on a two dimensional grid environment. The players appearing in the same habitat are allowed to interact with their immediate neighbors. Mobility of a player is defined as movement from its habitat to another based on both obtained payoff and randomly assigned habitat diversity values. By the end of extensive experimentation, it is concluded that player mobility is an effective factor that contributes to promotion of cooperation in spatial evolutionary games. Also, even for higher values of temptation of PD game, habitat diversity supports and triggers a collective resistance for the emergence and promotion of cooperative system behavior

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    Use of open linked data in bioinformatics space: A case study

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