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.

Latest papers with no code

Evaluating the Knowledge Base Completion Potential of GPT

no code yet • 23 Oct 2023

Structured knowledge bases (KBs) are an asset for search engines and other applications, but are inevitably incomplete.

Predicting affinity ties in a surname network

no code yet • 2 Jun 2023

From administrative registers of last names in Santiago, Chile, we create a surname affinity network that encodes socioeconomic data.

Causal Lifting and Link Prediction

no code yet • 2 Feb 2023

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

no code yet • 4 Dec 2022

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

no code yet • 14 Nov 2022

Over the past few years, large knowledge bases have been constructed to store massive amounts of knowledge.

HEAT: Hyperedge Attention Networks

no code yet • 28 Jan 2022

Learning from structured data is a core machine learning task.

KGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling

no code yet • 17 Dec 2021

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

no code yet • 16 Sep 2021

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

no code yet • AKBC 2021

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

no code yet • 18 Apr 2021

Pre-trained language models (LMs) like BERT have shown to store factual knowledge about the world.