Semantic Bridges Between First Order c-Representations and Cost-Based Semantics: An Initial Perspective
- URL: http://arxiv.org/abs/2510.00817v2
- Date: Thu, 02 Oct 2025 09:38:17 GMT
- Title: Semantic Bridges Between First Order c-Representations and Cost-Based Semantics: An Initial Perspective
- Authors: Nicholas Leisegang, Giovanni Casini, Thomas Meyer,
- Abstract summary: We show that a weighted knowledge base and a set of defeasible conditionals can generate the same ordering on interpretations.<n>Our results have the potential to benefit further work on both cost-based semantics and c-representations.
- Score: 0.45880283710344055
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Weighted-knowledge bases and cost-based semantics represent a recent formalism introduced by Bienvenu et al. for Ontology Mediated Data Querying in the case where a given knowledge base is inconsistent. This is done by adding a weight to each statement in the knowledge base (KB), and then giving each DL interpretation a cost based on how often it breaks rules in the KB. In this paper we compare this approach with c-representations, a form of non-monotonic reasoning originally introduced by Kern-Isberner. c-Representations describe a means to interpret defeasible concept inclusions in the first-order case. This is done by assigning a numerical ranking to each interpretations via penalties for each violated conditional. We compare these two approaches on a semantic level. In particular, we show that under certain conditions a weighted knowledge base and a set of defeasible conditionals can generate the same ordering on interpretations, and therefore an equivalence of semantic structures up to relative cost. Moreover, we compare entailment described in both cases, where certain notions are equivalently expressible in both formalisms. Our results have the potential to benefit further work on both cost-based semantics and c-representations
Related papers
- Enhancing Pre-trained Representation Classifiability can Boost its Interpretability [112.296393156262]
We quantify the representation interpretability by leveraging its correlation with the ratio of interpretable semantics within the representations.<n>We propose the Inherent Interpretability Score (IIS) that evaluates the information loss, measures the ratio of interpretable semantics, and quantifies the representation interpretability.
arXiv Detail & Related papers (2025-10-28T06:21:06Z) - Rational Inference in Formal Concept Analysis [0.4499833362998487]
Defeasible conditionals are a form of non-monotonic inference.<n>KLM framework defines a semantics for the propositional case of defeasible conditionals.
arXiv Detail & Related papers (2025-04-07T20:15:20Z) - Cost-Based Semantics for Querying Inconsistent Weighted Knowledge Bases [5.222978725954348]
We consider weighted knowledge bases in which both axioms and assertions have (possibly infinite) weights.
Two notions of certain and possible answer are defined by either considering interpretations whose cost does not exceed a given bound.
Our main contribution is a comprehensive analysis of the combined and data complexity of bounded cost satisfiability and certain and possible answer recognition.
arXiv Detail & Related papers (2024-07-30T11:56:02Z) - DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment [55.91429725404988]
We introduce DELTA, a discriminative model designed for legal case retrieval.
We leverage shallow decoders to create information bottlenecks, aiming to enhance the representation ability.
Our approach can outperform existing state-of-the-art methods in legal case retrieval.
arXiv Detail & Related papers (2024-03-27T10:40:14Z) - AMR4NLI: Interpretable and robust NLI measures from semantic graphs [28.017617759762278]
Natural language inference asks whether a given premise entails a given hypothesis.
We compare semantic structures to represent premise and hypothesis, including sets of contextualized embeddings and semantic graphs.
Our evaluation finds value in both contextualized embeddings and semantic graphs.
arXiv Detail & Related papers (2023-06-01T17:39:40Z) - RankCSE: Unsupervised Sentence Representations Learning via Learning to
Rank [54.854714257687334]
We propose a novel approach, RankCSE, for unsupervised sentence representation learning.
It incorporates ranking consistency and ranking distillation with contrastive learning into a unified framework.
An extensive set of experiments are conducted on both semantic textual similarity (STS) and transfer (TR) tasks.
arXiv Detail & Related papers (2023-05-26T08:27:07Z) - Enriching Disentanglement: From Logical Definitions to Quantitative Metrics [59.12308034729482]
Disentangling the explanatory factors in complex data is a promising approach for data-efficient representation learning.
We establish relationships between logical definitions and quantitative metrics to derive theoretically grounded disentanglement metrics.
We empirically demonstrate the effectiveness of the proposed metrics by isolating different aspects of disentangled representations.
arXiv Detail & Related papers (2023-05-19T08:22:23Z) - Logical Satisfiability of Counterfactuals for Faithful Explanations in
NLI [60.142926537264714]
We introduce the methodology of Faithfulness-through-Counterfactuals.
It generates a counterfactual hypothesis based on the logical predicates expressed in the explanation.
It then evaluates if the model's prediction on the counterfactual is consistent with that expressed logic.
arXiv Detail & Related papers (2022-05-25T03:40:59Z) - Supporting Vision-Language Model Inference with Confounder-pruning Knowledge Prompt [71.77504700496004]
Vision-language models are pre-trained by aligning image-text pairs in a common space to deal with open-set visual concepts.
To boost the transferability of the pre-trained models, recent works adopt fixed or learnable prompts.
However, how and what prompts can improve inference performance remains unclear.
arXiv Detail & Related papers (2022-05-23T07:51:15Z) - An ASP approach for reasoning on neural networks under a finitely
many-valued semantics for weighted conditional knowledge bases [0.0]
We consider conditional ALC knowledge bases with typicality in the finitely many-valued case.
We exploit ASP and "asprin" for reasoning with the concept-wise multipreferences.
arXiv Detail & Related papers (2022-02-02T16:30:28Z) - Probabilistic modelling of rational communication with conditionals [0.0]
We take a probabilistic approach to pragmatic reasoning about conditionals.
We show that our model uniformly explains a number of inferences attested in the literature.
arXiv Detail & Related papers (2021-05-12T08:21:25Z)
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.