A Semantically Enhanced Knowledge Discovery Method for Knowledge Graph Based on Adjacency Fuzzy Predicates Reasoning

Abstract

Discover the deep semantics from the massively structured data in knowledge graph and provide reasonable explanations are a series of important foundational research issues of artificial intelligence. However, the deep semantics hidden between entities in knowledge graph cannot be well expressed. Moreover, considering many predicates express fuzzy relationships, the existing reasoning methods cannot effectively deal with these fuzzy semantics and interpret the corresponding reasoning process. To counter the above problems, in this article, a new interpretable reasoning schema is proposed by introducing fuzzy theory. The presented method focuses on analyzing the fuzzy semantic between related entities in a knowledge graph. By annotating the fuzzy semantic features of adjacency predicates, a novel semantic reasoning model is designed to realize the fuzzy semantic extension over knowledge graph. The evaluation, based on both visualization and query experiments, shows that this proposal has advantages over the initial knowledge graph and can discover more valid semantic information.

A Web Semantic-Based Text Analysis Approach for Enhancing Named Entity Recognition Using PU-Learning and Negative Sampling

“…NLP is a pivotal domain within deep learning and encompasses various developmental trajectories and applications (Ismail et al, 2022;Vats et al, 2023), such as text classification (Singh & Sachan, 2021;Miri et al, 2022), text-to-image synthesis (Chopra et al, 2022), and unsupervised information extraction (Sarkissian & Tekli, 2021;Hajjar & Tekli, 2022). Among these domains, knowledge graphs are an indispensable component and have extensive applications in various fields (Zhao et al, 2022;Zhou et al, 2022a;Li et al, 2023), such as health care and cybersecurity (Gou et al, 2017;Sahoo & Gupta, 2019). NER is a crucial task in knowledge graphs and has received significant attention.…”