Papers Complex Query Answering
“Complex Query Answering” 태그가 달린 논문 39편 · 필터 해제
Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraints
Querying knowledge graphs (KGs) using deep learning approaches can naturally leverage the reasoning and generalization ability to learn to infer better answers. Traditional neural complex query answering (CQA) approaches…
Complex Query AnsweringKnowledge GraphsSequential Query Encoding For Complex Query Answering on Knowledge Graphs
Complex Query Answering (CQA) is an important and fundamental task for knowledge graph (KG) reasoning. Query encoding (QE) is proposed as a fast and robust solution to CQA. In the encoding process, most existing QE metho…
Complex Query AnsweringKnowledge GraphsAdapting Neural Link Predictors for Data-Efficient Complex Query Answering
Answering complex queries on incomplete knowledge graphs is a challenging task where a model needs to answer complex logical queries in the presence of missing knowledge. Prior work in the literature has proposed to addr…
Complex Query AnsweringKnowledge GraphsLink PredictionLogical Message Passing Networks with One-hop Inference on Atomic Formulas
Complex Query Answering (CQA) over Knowledge Graphs (KGs) has attracted a lot of attention to potentially support many applications. Given that KGs are usually incomplete, neural models are proposed to answer the logical…
Complex Query AnsweringGraph Representation LearningKnowledge GraphsLogical Reasoning+1Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization
Answering complex logical queries on incomplete knowledge graphs is a challenging task, and has been widely studied. Embedding-based methods require training on complex queries, and cannot generalize well to out-of-distr…
Complex Query AnsweringKnowledge GraphsNQE: N-ary Query Embedding for Complex Query Answering over Hyper-Relational Knowledge Graphs
Complex query answering (CQA) is an essential task for multi-hop and logical reasoning on knowledge graphs (KGs). Currently, most approaches are limited to queries among binary relational facts and pay less attention to …
Complex Query AnsweringKnowledge GraphsLogical ReasoningNegationInductive Logical Query Answering in Knowledge Graphs
Formulating and answering logical queries is a standard communication interface for knowledge graphs (KGs). Alleviating the notorious incompleteness of real-world KGs, neural methods achieved impressive results in link p…
Complex Query AnsweringEntity EmbeddingsKnowledge GraphsLink Prediction+1Neural-Symbolic Entangled Framework for Complex Query Answering
Answering complex queries over knowledge graphs (KG) is an important yet challenging task because of the KG incompleteness issue and cascading errors during reasoning. Recent query embedding (QE) approaches to embed the …
Complex Query AnsweringKnowledge GraphsLink PredictionNegationNeural-Symbolic Models for Logical Queries on Knowledge Graphs
Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental task for multi-hop reasoning. Traditional symbolic methods traverse a complete knowledge graph to extract the answers, which provides…
Complex Query AnsweringGraph Neural NetworkKnowledge GraphsRelationQuery2Particles: 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 GraphsKnowledge Graph Papers @ ICLR 2021
This post aims at providing an overview of ICLR 2021 papers focusing on knowledge graphs (KGs). In particular, we highlight the research in four wide areas: complex query answering and reasoning in KGs, temporal logics a…
Complex Query AnsweringEntity LinkingKnowledge GraphsQuestion AnsweringQubitE: Qubit Embedding for Knowledge Graph Completion
Knowledge graph embeddings (KGEs) learn low-dimensional representations of entities and relations to predict missing facts based on existing ones. Quantum-based KGEs utilise variational quantum circuits for link predict…
Complex Query AnsweringKnowledge Graph CompletionKnowledge Graph EmbeddingsKnowledge Graphs+1Benchmarking the Combinatorial Generalizability of Complex Query Answering on Knowledge Graphs
Complex Query Answering (CQA) is an important reasoning task on knowledge graphs. Current CQA learning models have been shown to be able to generalize from atomic operators to more complex formulas, which can be regarded…
BenchmarkingComplex Query AnsweringKnowledge GraphsLogic Embeddings for Complex Query Answering
Answering logical queries over incomplete knowledge bases is challenging because: 1) it calls for implicit link prediction, and 2) brute force answering of existential first-order logic queries is exponential in the numb…
Complex Query AnsweringKnowledge GraphsLink PredictionNegation+1Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification
Large, heterogeneous datasets are characterized by missing or even erroneous information. This is more evident when they are the product of community effort or automatic fact extraction methods from external sources, suc…
Binary ClassificationClassificationComplex Query AnsweringGeneral Classification+3Complex Query Answering with Neural Link Predictors
Neural link predictors are immensely useful for identifying missing edges in large scale Knowledge Graphs. However, it is still not clear how to use these models for answering more complex queries that arise in a number …
Complex Query AnsweringKnowledge GraphsBeta 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 ReasoningNegationQuery2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings
Answering complex logical queries on large-scale incomplete knowledge graphs (KGs) is a fundamental yet challenging task. Recently, a promising approach to this problem has been to embed KG entities as well as the query …
Complex Query AnsweringKnowledge GraphsEmbedding Logical Queries on Knowledge Graphs
Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go be…
Complex Query AnsweringKnowledge Graphs