Queries With Exact Truth Values in Paraconsistent Description Logics

• URL: http://arxiv.org/abs/2408.07283v2
• Date: Thu, 15 Aug 2024 07:33:58 GMT
• Title: Queries With Exact Truth Values in Paraconsistent Description Logics
• Authors: Meghyn Bienvenu, Camille Bourgaux, Daniil Kozhemiachenko,
• Abstract summary: We present a novel approach to querying classical inconsistent description logic (DL) knowledge bases. We adopt aparaconsistent semantics with the four Belnapian values: exactly true ($mathbfT$), exactly false ($mathbfF$), both ($mathbfB$), and neither ($mathbfN$)
• Score: 5.222978725954348
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a novel approach to querying classical inconsistent description logic (DL) knowledge bases by adopting a~paraconsistent semantics with the four Belnapian values: exactly true ($\mathbf{T}$), exactly false ($\mathbf{F}$), both ($\mathbf{B}$), and neither ($\mathbf{N}$). In contrast to prior studies on paraconsistent DLs, we allow truth value operators in the query language, which can be used to differentiate between answers having contradictory evidence and those having only positive evidence. We present a reduction to classical DL query answering that allows us to pinpoint the precise combined and data complexity of answering queries with values in paraconsistent $\mathcal{ALCHI}$ and its sublogics. Notably, we show that tractable data complexity is retained for Horn DLs. We present a comparison with repair-based inconsistency-tolerant semantics, showing that the two approaches are incomparable.

Related papers

• 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)
• Uncovering Hidden Correctness in LLM Causal Reasoning via Symbolic Verification [56.51953062869371]
  DoVerifier is a symbolic verifier that checks whether causal expressions are derivable from a given causal graph using rules from do-calculus and probability theory.<n>Our evaluations on synthetic data and causal QA benchmarks show that DoVerifier more accurately captures semantic correctness of causal reasoning traces.
  arXiv  Detail & Related papers  (2026-01-29T03:22:58Z)
• Data Complexity of Querying Description Logic Knowledge Bases under Cost-Based Semantics [4.511923587827302]
  We study the data complexity of querying inconsistent weighted description logic (DL) knowledge bases under cost-based semantics.<n>In a nutshell, the idea is to assign each interpretation a cost based upon the weights of the violated axioms and assertions.<n>We find that certain answers for instance queries and possible answers for conjunctive queries can be computed using first-order rewriting.
  arXiv  Detail & Related papers  (2025-11-10T13:34:24Z)
• Out-of-Context Reasoning in Large Language Models [21.57320110518431]
  We study how large language models (LLMs) reason about memorized knowledge through simple binary relations.<n>Unlike in-context reasoning, the axioms are only seen during training and not provided in the task prompt.<n>We introduce a lightweight technique, out-of-context representation learning, which trains only new token embeddings on axioms and evaluates them on unseen tasks.
  arXiv  Detail & Related papers  (2025-03-13T14:32:30Z)
• FLARE: Faithful Logic-Aided Reasoning and Exploration [50.9814063216852]
  We introduce a novel approach for traversing the problem space using task decompositions. We use the Large Language Models to plan a solution, soft-formalise the query into facts and predicates using a logic programming code. Our method allows us to compute the faithfulness of the reasoning process w.r.t. the generated code and analyse the steps of the multi-hop search without relying on external solvers.
  arXiv  Detail & Related papers  (2024-10-14T19:39:11Z)
• Generating $SROI^-$ Ontologies via Knowledge Graph Query Embedding Learning [26.905431434167536]
  We propose a novel query embedding method, AConE, which explains the knowledge learned from the graph in the form of $SROI-$ description logic axioms. AConE achieves superior results over previous baselines with fewer parameters. We provide comprehensive analyses showing that the capability to represent axioms positively impacts the results of query answering.
  arXiv  Detail & Related papers  (2024-07-12T12:20:39Z)
• Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations [71.2950434944196]
  We develop Maieutic Prompting, which infers a correct answer to a question even from the noisy and inconsistent generations of language models. Maieutic Prompting achieves up to 20% better accuracy than state-of-the-art prompting methods.
  arXiv  Detail & Related papers  (2022-05-24T06:36:42Z)
• On the Paradox of Learning to Reason from Data [86.13662838603761]
  We show that BERT can attain near-perfect accuracy on in-distribution test examples while failing to generalize to other data distributions over the exact same problem space. Our study provides an explanation for this paradox: instead of learning to emulate the correct reasoning function, BERT has in fact learned statistical features that inherently exist in logical reasoning problems.
  arXiv  Detail & Related papers  (2022-05-23T17:56:48Z)
• Answering Fuzzy Queries over Fuzzy DL-Lite Ontologies [0.2741266294612776]
  We study the problem of answering conjunctive queries and threshold queries in fuzzy DL-Lite. For the idemdent G"odel t-norm, we provide an effective method based on a reduction to the classical case.
  arXiv  Detail & Related papers  (2021-11-23T10:45:54Z)
• How to Query An Oracle? Efficient Strategies to Label Data [59.89900843097016]
  We consider the basic problem of querying an expert oracle for labeling a dataset in machine learning. We present a randomized batch algorithm that operates on a round-by-round basis to label the samples and achieves a query rate of $O(fracNk2)$. In addition, we present an adaptive greedy query scheme, which achieves an average rate of $approx 0.2N$ queries per sample with triplet queries.
  arXiv  Detail & Related papers  (2021-10-05T20:15:35Z)
• 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)
• ConjNLI: Natural Language Inference Over Conjunctive Sentences [89.50542552451368]
  Reasoning about conjuncts in conjunctive sentences is important for a deeper understanding of conjunctions. Existing NLI stress tests do not consider non-boolean usages of conjunctions. We introduce ConjNLI, a challenge stress-test for natural language inference over conjunctive sentences.
  arXiv  Detail & Related papers  (2020-10-20T16:29:13Z)
• CQE in Description Logics Through Instance Indistinguishability (extended version) [0.0]
  We study privacy-preserving query answering in Description Logics (DLs) We derive data complexity results query answering over DL-Lite$_mathcal$$. We identify a semantically well-founded notion of approximated confidentiality answering for CQE.
  arXiv  Detail & Related papers  (2020-04-24T17:28:24Z)
• Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings [84.0206612938464]
  query2box is an embedding-based framework for reasoning over arbitrary queries on incomplete knowledge graphs. We show that query2box is capable of handling arbitrary logical queries with $wedge$, $vee$, $exists$ in a scalable manner. We demonstrate the effectiveness of query2box on three large KGs and show that query2box achieves up to 25% relative improvement over the state of the art.
  arXiv  Detail & Related papers  (2020-02-14T11:20:10Z)

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.