Which bird does not have wings: Negative-constrained KGQA with Schema-guided Semantic Matching and Self-directed Refinement
- URL: http://arxiv.org/abs/2604.14749v1
- Date: Thu, 16 Apr 2026 08:02:55 GMT
- Title: Which bird does not have wings: Negative-constrained KGQA with Schema-guided Semantic Matching and Self-directed Refinement
- Abstract summary: We introduce a new task, NEgative-conSTrained (NEST) KGQA, where each question contains at least one negative constraint.<n>We also design PyLF, a Python-formatted logical form, since existing logical forms are hardly suitable to express negation clearly.<n>We present a novel framework named CUCKOO, specialized to multiple-constrained questions and ensuring semantic executability.
- Score: 5.784146913646395
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large language models still struggle with faithfulness and hallucinations despite their remarkable reasoning abilities. In Knowledge Graph Question Answering (KGQA), semantic parsing-based approaches address the limitations by understanding constraints in a user's question and converting them into a logical form to execute on a knowledge graph. However, existing KGQA benchmarks and methods are biased toward positive and calculation constraints. Negative constraints are neglected, although they frequently appear in real-world questions. In this paper, we introduce a new task, NEgative-conSTrained (NEST) KGQA, where each question contains at least one negative constraint, and a corresponding dataset, NestKGQA. We also design PyLF, a Python-formatted logical form, since existing logical forms are hardly suitable to express negation clearly while maintaining readability. Furthermore, NEST questions naturally contain multiple constraints. To mitigate their semantic complexity, we present a novel framework named CUCKOO, specialized to multiple-constrained questions and ensuring semantic executability. CUCKOO first generates a constraint-aware logical form draft and performs schema-guided semantic matching. It then selectively applies self-directed refinement only when executing improper logical forms yields an empty result, reducing cost while improving robustness. Experimental results demonstrate that CUCKOO consistently outperforms baselines on both conventional KGQA and NEST-KGQA benchmarks under few-shot settings.
Related papers
- Constrained Entity Selection under Partial Knowledge for LLM-Based Knowledge Graph QA [0.0]
We introduce emphConstrained Entity Selection under Partial Knowledge (CES-PK), a problem formulation that focuses on eliminating invalid answers.<n> Experiments show that precision improves by filtering invalid candidates, while recall is preserved due to retaining candidates whose constraints are not explicitly violated.
arXiv Detail & Related papers (2026-08-25T17:04:32Z) - Better Later Than Sooner: Neuro-Symbolic Knowledge Graph Construction via Ontology-grounded Post-extraction Correction [6.031254272577503]
Question answering (QA) is a core challenge in AI, particularly for complex queries requiring multi-hop reasoning.<n>We propose a neuro-symbolic framework for ontology-grounded KG construction combining open-domain extraction, embedding-based canonicalization of types and predicates.<n>We show that the extracted KGs are well suited for symbolic querying by measuring the occurrence of SPARQL graph patterns.
arXiv Detail & Related papers (2026-05-27T23:09:10Z) - OrLog: Resolving Complex Queries with LLMs and Probabilistic Reasoning [51.58235452818926]
We introduce OrLog, a neuro-symbolic retrieval framework that decouples predicate-level plausibility estimation from logical reasoning.<n>A large language model (LLM) provides plausibility scores for atomic predicates in one decoding-free forward pass, from which a probabilistic reasoning engine derives the posterior probability of query satisfaction.
arXiv Detail & Related papers (2026-01-30T15:31:58Z) - KBQA-R1: Reinforcing Large Language Models for Knowledge Base Question Answering [64.62317305868264]
We present textbfKBQA-R1, a framework that shifts the paradigm from text imitation to interaction optimization via Reinforcement Learning.<n>Treating KBQA as a multi-turn decision process, our model learns to navigate the knowledge base using a list of actions.<n>Experiments on WebQSP, GrailQA, and GraphQuestions demonstrate that KBQA-R1 achieves state-of-the-art performance.
arXiv Detail & Related papers (2025-12-10T17:45:42Z) - ComLQ: Benchmarking Complex Logical Queries in Information Retrieval [26.606215927237248]
Information retrieval systems play a critical role in navigating information overload across various applications.<n>These benchmarks can not be used to sufficiently evaluate the performance of IR models on complex queries in real-world scenarios.<n>We propose a novel method leveraging large language models (LLMs) to construct a new IR dataset textbfComLQ for textbfComplex textbfLogical textbfQueries.
arXiv Detail & Related papers (2025-11-15T02:58:21Z) - Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints [10.209426700875866]
We propose a Neural Query Reranker (NQR) designed to adjust query answer scores by incorporating soft constraints without disrupting the original answers to a query.<n>NQR operates interactively, refining answers based on incremental examples of preferred and non-preferred entities.<n>Our experiments demonstrate that NQR can capture soft constraints while maintaining robust query answering performance.
arXiv Detail & Related papers (2025-08-19T09:09:07Z) - Effective Instruction Parsing Plugin for Complex Logical Query Answering on Knowledge Graphs [51.33342412699939]
Knowledge Graph Query Embedding (KGQE) aims to embed First-Order Logic (FOL) queries in a low-dimensional KG space for complex reasoning over incomplete KGs.
Recent studies integrate various external information (such as entity types and relation context) to better capture the logical semantics of FOL queries.
We propose an effective Query Instruction Parsing (QIPP) that captures latent query patterns from code-like query instructions.
arXiv Detail & Related papers (2024-10-27T03:18:52Z) - DecAF: Joint Decoding of Answers and Logical Forms for Question
Answering over Knowledge Bases [81.19499764899359]
We propose a novel framework DecAF that jointly generates both logical forms and direct answers.
DecAF achieves new state-of-the-art accuracy on WebQSP, FreebaseQA, and GrailQA benchmarks.
arXiv Detail & Related papers (2022-09-30T19:51:52Z) - Neural-Symbolic Entangled Framework for Complex Query Answering [22.663509971491138]
We propose a Neural and Entangled framework (ENeSy) for complex query answering.
It enables the neural and symbolic reasoning to enhance each other to alleviate the cascading error and KG incompleteness.
ENeSy achieves the SOTA performance on several benchmarks, especially in the setting of the training model only with the link prediction task.
arXiv Detail & Related papers (2022-09-19T06:07:10Z) - Logic Embeddings for Complex Query Answering [56.25151854231117]
We propose Logic Embeddings, a new approach to embedding complex queries that uses Skolemisation to eliminate existential variables for efficient querying.
We show that Logic Embeddings are competitively fast and accurate in query answering over large, incomplete knowledge graphs, outperform on negation queries, and in particular, provide improved modeling of answer uncertainty.
arXiv Detail & Related papers (2021-02-28T07:52:37Z) - Logically Consistent Loss for Visual Question Answering [66.83963844316561]
The current advancement in neural-network based Visual Question Answering (VQA) cannot ensure such consistency due to identically distribution (i.i.d.) assumption.
We propose a new model-agnostic logic constraint to tackle this issue by formulating a logically consistent loss in the multi-task learning framework.
Experiments confirm that the proposed loss formulae and introduction of hybrid-batch leads to more consistency as well as better performance.
arXiv Detail & Related papers (2020-11-19T20:31:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.