Papers Knowledge Base Completion
“Knowledge Base Completion” 태그가 달린 논문 156편 · 필터 해제
FALCON: Scalable Reasoning over Inconsistent ALC Ontologies
Ontologies are one of the richest sources of knowledge. Real-world ontologies often contain thousands of axioms and are often human-made. Hence, they may contain inconsistency and incomplete information which may impair …
Knowledge Base CompletionEffective Few-Shot Named Entity Linking by Meta-Learning
Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base, which is significant and fundamental for various downstream applications, e.g., knowledge base completion, question answ…
Entity LinkingKnowledge Base CompletionMeta-LearningQuestion AnsweringHEAT: Hyperedge Attention Networks
Learning from structured data is a core machine learning task. Commonly, such data is represented as graphs, which normally only consider (typed) binary relationships between pairs of nodes. This is a substantial limitat…
Knowledge Base CompletionKGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling
Knowledge base completion is formulated as a binary classification problem in this work, where an XGBoost binary classifier is trained for each relation using relevant links in knowledge graphs (KGs). The new method, nam…
Binary ClassificationKnowledge Base CompletionKnowledge GraphsLink PredictionCapacity and Bias of Learned Geometric Embeddings for Directed Graphs
A wide variety of machine learning tasks such as knowledge base completion, ontology alignment, and multi-label classification can benefit from incorporating into learning differentiable representations of graphs or taxo…
Knowledge Base CompletionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTime in a Box: Advancing Knowledge Graph Completion with Temporal Scopes
Almost all statements in knowledge bases have a temporal scope during which they are valid. Hence, knowledge base completion (KBC) on temporal knowledge bases (TKB), where each statement \textit{may} be associated with a…
Knowledge Base CompletionKnowledge Graph CompletionLink PredictionTime-interval Prediction+1Scalable knowledge base completion with superposition memories
We present Harmonic Memory Networks (HMem), a neural architecture for knowledge base completion that models entities as weighted sums of pairwise bindings between an entity's neighbors and corresponding relations. Since …
Knowledge Base CompletionRelation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations
Learning good representations on multi-relational graphs is essential to knowledge base completion (KBC). In this paper, we propose a new self-supervised training objective for multi-relational graph representation learn…
Graph Representation LearningKnowledge Base CompletionKnowledge Graph CompletionKnowledge Graph Embedding+6Combining Rules and Embeddings via Neuro-Symbolic AI for Knowledge Base Completion
Recent interest in Knowledge Base Completion (KBC) has led to a plethora of approaches based on reinforcement learning, inductive logic programming and graph embeddings. In particular, rule-based KBC has led to interpret…
Inductive logic programmingKnowledge Base CompletionRelationKnowledge Base Completion Meets Transfer Learning
The aim of knowledge base completion is to predict unseen facts from existing facts in knowledge bases. In this work, we introduce the first approach for transfer of knowledge from one collection of facts to another with…
Knowledge Base CompletionRelationTransfer LearningWhy a Naive Way to Combine Symbolic and Latent Knowledge Base Completion Works Surprisingly Well
We compare a rule-based approach for knowledge graph completion against current state-of-the-art, which is based on embbedings. Instead of focusing on aggregated metrics, we look at several examples that illustrate essen…
Knowledge Base CompletionKnowledge Graph CompletionScientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study
Biomedical knowledge graphs (KGs) hold rich information on entities such as diseases, drugs, and genes. Predicting missing links in these graphs can boost many important applications, such as drug design and repurposing.…
Drug DesignKnowledge Base CompletionKnowledge GraphsLink PredictionBERTnesia: Investigating the capture and forgetting of knowledge in BERT
Probing complex language models has recently revealed several insights into linguistic and semantic patterns found in the learned representations. In this article, we probe BERT specifically to understand and measure the…
Knowledge Base CompletionLanguage ModelingLanguage ModellingMasked Language Modeling+2QuatDE: Dynamic Quaternion Embedding for Knowledge Graph Completion
Knowledge graph embedding has been an active research topic for knowledge base completion (KGC), with progressive improvement from the initial TransE, TransH, RotatE et al to the current state-of-the-art QuatE. However, …
Graph EmbeddingKnowledge Base CompletionKnowledge Graph CompletionKnowledge Graph Embedding+3CEAR: Cross-Entity Aware Reranker for Knowledge Base Completion
Pre-trained language models (LMs) like BERT have shown to store factual knowledge about the world. This knowledge can be used to augment the information present in Knowledge Bases, which tend to be incomplete. However, p…
Knowledge Base CompletionLink PredictionK-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce
Existing pre-trained language models (PLMs) have demonstrated the effectiveness of self-supervised learning for a broad range of natural language processing (NLP) tasks. However, most of them are not explicitly aware of …
DecoderKnowledge Base CompletionLanguage ModelingLanguage Modelling+2DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections
This paper explores learning rich self-supervised entity representations from large amounts of the associated text. Once pre-trained, these models become applicable to multiple entity-centric tasks such as ranked retriev…
Knowledge Base CompletionNatural Language QueriesQuestion AnsweringRetrieval+2Ranking vs. Classifying: Measuring Knowledge Base Completion Quality
Knowledge base completion (KBC) methods aim at inferring missing facts from the information present in a knowledge base (KB) by estimating the likelihood of candidate facts. In the prevailing evaluation paradigm, models …
Knowledge Base CompletionModel SelectionK-PLUG: KNOWLEDGE-INJECTED PRE-TRAINED LANGUAGE MODEL FOR NATURAL LANGUAGE UNDERSTANDING AND GENERATION
Existing pre-trained language models (PLMs) have demonstrated the effectiveness of self-supervised learning for a broad range of natural language processing (NLP) tasks. However, most of them are not explicitly aware of …
ChatbotDecoderKnowledge Base CompletionLanguage Modeling+4Modelling General Properties of Nouns by Selectively Averaging Contextualised Embeddings
While the success of pre-trained language models has largely eliminated the need for high-quality static word vectors in many NLP applications, such vectors continue to play an important role in tasks where words need to…
Knowledge Base Completion