Papers Complex Query Answering
“Complex Query Answering” 태그가 달린 논문 39편 · 필터 해제
Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)
Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing (NLP), although they remain susceptible to errors. Retrieval-augmented generation (RAG) systems have emerged as a c…
Complex Query AnsweringQuestion AnsweringNeural Scalable Symbolic Search Framework for Complex Logical Queries with Multiple Free Variables
Complex Query Answering (CQA) is a fundamental knowledge representation and reasoning task over incomplete knowledge graphs (KGs). Answering existential first-order queries with $k$ free variables (i.e., $\text{EFO}_k$ q…
Complex Query AnsweringKnowledge GraphsSubSearch: Intermediate Rewards for Unsupervised Guided Reasoning in Complex Retrieval
Large language models (LLMs) are probabilistic in nature and perform more reliably when augmented with external information. As complex queries often require multi-step reasoning over the retrieved information, with no c…
Complex Query AnsweringReinforcement LearningCounting Still Counts: Understanding Neural Complex Query Answering Through Query Relaxation
Neural methods for Complex Query Answering (CQA) over knowledge graphs (KGs) are widely believed to learn patterns that generalize beyond explicit graph structure, allowing them to infer answers that are unreachable thro…
Complex Query AnsweringKnowledge GraphsCQD-SHAP: Explainable Complex Query Answering via Shapley Values
Complex query answering (CQA) goes beyond the widely studied link prediction task by addressing more sophisticated queries that require multi-hop reasoning over incomplete knowledge graphs (KGs). Research on neural and n…
Complex Query AnsweringKnowledge GraphsLink PredictionExploring the Paradigm Shift from Grounding to Skolemization for Complex Query Answering on Knowledge Graphs
Complex Query Answering (CQA) over incomplete Knowledge Graphs (KGs), typically formalized as reasoning with Existential First-Order predicate logic with one free variable (EFO\textsubscript{1}), faces a fundamental trad…
Computational EfficiencyComplex Query AnsweringKnowledge GraphsEfficient and Scalable Neural Symbolic Search for Knowledge Graph Complex Query Answering
Complex Query Answering (CQA) aims to retrieve answer sets for complex logical formulas from incomplete knowledge graphs, which is a crucial yet challenging task in knowledge graph reasoning. While neuro-symbolic search …
Complex Query AnsweringKnowledge GraphsTransformers for Complex Query Answering over Knowledge Hypergraphs
Complex Query Answering (CQA) has been extensively studied in recent years. In order to model data that is closer to real-world distribution, knowledge graphs with different modalities have been introduced. Triple KGs, a…
Complex Query AnsweringKnowledge GraphsNegationNeural-Symbolic Message Passing with Dynamic Pruning
Complex Query Answering (CQA) over incomplete Knowledge Graphs (KGs) is a challenging task. Recently, a line of message-passing-based research has been proposed to solve CQA. However, they perform unsatisfactorily on neg…
Complex Query AnsweringKnowledge GraphsIs Complex Query Answering Really Complex?
Complex query answering (CQA) on knowledge graphs (KGs) is gaining momentum as a challenging reasoning task. In this paper, we show that the current benchmarks for CQA are not really complex, and the way they are built d…
Complex Query AnsweringKnowledge GraphsLink PredictionA Foundation Model for Zero-shot Logical Query Reasoning
Complex logical query answering (CLQA) in knowledge graphs (KGs) goes beyond simple KG completion and aims at answering compositional queries comprised of multiple projections and logical operations. Existing CLQA method…
Complex Query AnsweringKnowledge Graph CompletionKnowledge GraphsMeta Operator for Complex Query Answering on Knowledge Graphs
Knowledge graphs contain informative factual knowledge but are considered incomplete. To answer complex queries under incomplete knowledge, learning-based Complex Query Answering (CQA) models are proposed to directly lea…
Complex Query AnsweringKnowledge GraphsMeta-LearningMulti-Task LearningFederated Neural Graph Databases
The increasing demand for large-scale language models (LLMs) has highlighted the importance of efficient data retrieval mechanisms. Neural graph databases (NGDBs) have emerged as a promising approach to storing and query…
Complex Query AnsweringFederated LearningKnowledge Graph EmbeddingsKnowledge Graphs+2Conditional Logical Message Passing Transformer for Complex Query Answering
Complex Query Answering (CQA) over Knowledge Graphs (KGs) is a challenging task. Given that KGs are usually incomplete, neural models are proposed to solve CQA by performing multi-hop logical reasoning. However, most of …
Complex Query AnsweringKnowledge GraphsLogical ReasoningType-based Neural Link Prediction Adapter for Complex Query Answering
Answering complex logical queries on incomplete knowledge graphs (KGs) is a fundamental and challenging task in multi-hop reasoning. Recent work defines this task as an end-to-end optimization problem, which significantl…
Complex Query AnsweringKnowledge GraphsLink PredictionUnderstanding Inter-Session Intentions via Complex Logical Reasoning
Understanding user intentions is essential for improving product recommendations, navigation suggestions, and query reformulations. However, user intentions can be intricate, involving multiple sessions and attribute req…
AttributeComplex Query AnsweringLogical ReasoningMMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
We introduce MMMU: a new benchmark designed to evaluate multimodal models on massive multi-discipline tasks demanding college-level subject knowledge and deliberate reasoning. MMMU includes 11.5K meticulously collected m…
Complex Query AnsweringLogical ReasoningVisual ReasoningQuery2Triple: Unified Query Encoding for Answering Diverse Complex Queries over Knowledge Graphs
Complex Query Answering (CQA) is a challenge task of Knowledge Graph (KG). Due to the incompleteness of KGs, query embedding (QE) methods have been proposed to encode queries and entities into the same embedding space, a…
Complex Query AnsweringKnowledge Graphs$\text{EFO}_{k}$-CQA: Towards Knowledge Graph Complex Query Answering beyond Set Operation
To answer complex queries on knowledge graphs, logical reasoning over incomplete knowledge is required due to the open-world assumption. Learning-based methods are essential because they are capable of generalizing over …
Complex Query AnsweringKnowledge GraphsLogical ReasoningKnowledge Graph Reasoning over Entities and Numerical Values
A complex logic query in a knowledge graph refers to a query expressed in logic form that conveys a complex meaning, such as where did the Canadian Turing award winner graduate from? Knowledge graph reasoning-based appli…
AttributeComplex Query AnsweringKnowledge Graphs