Knowledge Base Completion
64 papers with code • 0 benchmarks • 2 datasets
Knowledge base completion is the task which automatically infers missing facts by reasoning about the information already present in the knowledge base. A knowledge base is a collection of relational facts, often represented in the form of "subject", "relation", "object"-triples.
Benchmarks
These leaderboards are used to track progress in Knowledge Base Completion
Latest papers with no code
Evaluating the Knowledge Base Completion Potential of GPT
Structured knowledge bases (KBs) are an asset for search engines and other applications, but are inevitably incomplete.
Predicting affinity ties in a surname network
From administrative registers of last names in Santiago, Chile, we create a surname affinity network that encodes socioeconomic data.
Causal Lifting and Link Prediction
Existing causal models for link prediction assume an underlying set of inherent node factors -- an innate characteristic defined at the node's birth -- that governs the causal evolution of links in the graph.
Query-Driven Knowledge Base Completion using Multimodal Path Fusion over Multimodal Knowledge Graph
Over the past few years, large knowledge bases have been constructed to store massive amounts of knowledge.
Knowledge Base Completion using Web-Based Question Answering and Multimodal Fusion
Over the past few years, large knowledge bases have been constructed to store massive amounts of knowledge.
HEAT: Hyperedge Attention Networks
Learning from structured data is a core machine learning task.
KGBoost: 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).
Combining 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.
Why 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.
CEAR: Cross-Entity Aware Reranker for Knowledge Base Completion
Pre-trained language models (LMs) like BERT have shown to store factual knowledge about the world.