Faithful Embeddings for Knowledge Base Queries
The deductive closure of an ideal knowledge base (KB) contains exactly the logical queries that the KB can answer. However, in practice KBs are both incomplete and over-specified, failing to answer some queries that have real-world answers. \emph{Query embedding} (QE) techniques have been recently proposed where KB entities and KB queries are represented jointly in an embedding space, supporting relaxation and generalization in KB inference. However, experiments in this paper show that QE systems may disagree with deductive reasoning on answers that do not require generalization or relaxation. We address this problem with a novel QE method that is more faithful to deductive reasoning, and show that this leads to better performance on complex queries to incomplete KBs. Finally we show that inserting this new QE module into a neural question-answering system leads to substantial improvements over the state-of-the-art.
Code (1)
Tasks
Question AnsweringSimilar Papers 제목 키워드 기반
Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs
One of the fundamental problems in Artificial Intelligence is to perform complex multi-hop logical reasoning over the facts captured by a knowledge graph (KG). This problem is challenging, because KGs can be massive and …
Complex Query AnsweringKnowledge GraphsLogical ReasoningNegationBoxLitE: A Faithful Knowledge Base Embedding Based on Convex Optimization
Knowledge base (KB) embeddings aim at combining the capability of classical knowledge graph embeddings to generalize the information present in facts, the ABox, with conceptual knowledge represented in an ontology langua…
Query2Particles: Knowledge Graph Reasoning with Particle Embeddings
Answering complex logical queries on incomplete knowledge graphs (KGs) with missing edges is a fundamental and important task for knowledge graph reasoning. The query embedding method is proposed to answer these queries …
Complex Query AnsweringEntity EmbeddingsKnowledge GraphsMulti-Document Financial Question Answering using LLMs
We propose two new methods for multi-document financial question answering. First, a method that uses semantic tagging, and then, queries the index to get the context (RAG_SEM). And second, a Knowledge Graph (KG_RAG) bas…
Knowledge DistillationKnowledge GraphsQuestion AnsweringRAGKGEx: Explaining Knowledge Graph Embeddings via Subgraph Sampling and Knowledge Distillation
Despite being the go-to choice for link prediction on knowledge graphs, research on interpretability of knowledge graph embeddings (KGE) has been relatively unexplored. We present KGEx, a novel post-hoc method that expla…
Knowledge DistillationKnowledge Graph EmbeddingsKnowledge GraphsLink Prediction