Revisiting Simple Neural Networks for Learning Representations of Knowledge Graphs
We address the problem of learning vector representations for entities and relations in Knowledge Graphs (KGs) for Knowledge Base Completion (KBC). This problem has received significant attention in the past few years and multiple methods have been proposed. Most of the existing methods in the literature use a predefined characteristic scoring function for evaluating the correctness of KG triples. These scoring functions distinguish correct triples (high score) from incorrect ones (low score). However, their performance vary across different datasets. In this work, we demonstrate that a simple neural network based score function can consistently achieve near start-of-the-art performance on multiple datasets. We also quantitatively demonstrate biases in standard benchmark datasets, and highlight the need to perform evaluation spanning various datasets.
Code (1)
Tasks
Knowledge Base CompletionKnowledge GraphsSimilar Papers 제목 키워드 기반
GNN is a Counter? Revisiting GNN for Question Answering
Question Answering (QA) has been a long-standing research topic in AI and NLP fields, and a wealth of studies have been conducted to attempt to equip QA systems with human-level reasoning capability. To approximate the c…
Knowledge GraphsQuestion AnsweringRe-revisiting Learning on Hypergraphs: Confidence Interval and Subgradient Method
We revisit semi-supervised learning on hypergraphs. Same as previous approaches, our method uses a convex program whose objective function is not everywhere differentiable. We exploit the non-uniqueness of the optim…
An Empirical Revisiting of Linguistic Knowledge Fusion in Language Understanding Tasks
Though linguistic knowledge emerges during large-scale language model pretraining, recent work attempt to explicitly incorporate human-defined linguistic priors into task-specific fine-tuning. Infusing language models wi…
Language ModelingLanguage ModellingRevisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training
Recently, knowledge-enhanced pre-trained language models (KEPLMs) improve context-aware representations via learning from structured relations in knowledge graphs, and/or linguistic knowledge from syntactic or dependency…
GPUKnowledge GraphsLanguage ModelingLanguage Modelling+2Revisiting Role Discovery in Networks: From Node to Edge Roles
Previous work in network analysis has focused on modeling the mixed-memberships of node roles in the graph, but not the roles of edges. We introduce the edge role discovery problem and present a generalizable framework f…