A Variable Occurrence-Centric Framework for Inconsistency Handling (Extended Version)
- URL: http://arxiv.org/abs/2412.11868v2
- Date: Tue, 17 Dec 2024 08:17:54 GMT
- Title: A Variable Occurrence-Centric Framework for Inconsistency Handling (Extended Version)
- Authors: Yakoub Salhi,
- Abstract summary: We introduce a framework for analyzing and handling inconsistencies in propositional bases.
We propose two dual concepts: Minimal Inconsistency Relation (MIR) and Maximal Consistency Relation (MCR)
- Score: 13.706331473063882
- License:
- Abstract: In this paper, we introduce a syntactic framework for analyzing and handling inconsistencies in propositional bases. Our approach focuses on examining the relationships between variable occurrences within conflicts. We propose two dual concepts: Minimal Inconsistency Relation (MIR) and Maximal Consistency Relation (MCR). Each MIR is a minimal equivalence relation on variable occurrences that results in inconsistency, while each MCR is a maximal equivalence relation designed to prevent inconsistency. Notably, MIRs capture conflicts overlooked by minimal inconsistent subsets. Using MCRs, we develop a series of non-explosive inference relations. The main strategy involves restoring consistency by modifying the propositional base according to each MCR, followed by employing the classical inference relation to derive conclusions. Additionally, we propose an unusual semantics that assigns truth values to variable occurrences instead of the variables themselves. The associated inference relations are established through Boolean interpretations compatible with the occurrence-based models.
Related papers
- More than Classification: A Unified Framework for Event Temporal
Relation Extraction [61.44799147458621]
Event temporal relation extraction(ETRE) is usually formulated as a multi-label classification task.
We observe that all relations can be interpreted using the start and end time points of events.
We propose a unified event temporal relation extraction framework, which transforms temporal relations into logical expressions of time points.
arXiv Detail & Related papers (2023-05-28T02:09:08Z) - Relational Sentence Embedding for Flexible Semantic Matching [86.21393054423355]
We present Sentence Embedding (RSE), a new paradigm to discover further the potential of sentence embeddings.
RSE is effective and flexible in modeling sentence relations and outperforms a series of state-of-the-art embedding methods.
arXiv Detail & Related papers (2022-12-17T05:25:17Z) - Rethinking the Evaluation of Unbiased Scene Graph Generation [31.041074897404236]
Scene Graph Generation (SGG) methods tend to predict frequent predicate categories and fail to recognize rare ones.
Recent research has focused on unbiased SGG and adopted mean Recall@K as the main evaluation metric.
We propose two complementary evaluation metrics for unbiased SGG: Independent Mean Recall (IMR) and weighted IMR (wIMR)
arXiv Detail & Related papers (2022-08-03T08:23:51Z) - Causal Inference Through the Structural Causal Marginal Problem [17.91174054672512]
We introduce an approach to counterfactual inference based on merging information from multiple datasets.
We formalise this approach for categorical SCMs using the response function formulation and show that it reduces the space of allowed marginal and joint SCMs.
arXiv Detail & Related papers (2022-02-02T21:45:10Z) - Disentangling Observed Causal Effects from Latent Confounders using
Method of Moments [67.27068846108047]
We provide guarantees on identifiability and learnability under mild assumptions.
We develop efficient algorithms based on coupled tensor decomposition with linear constraints to obtain scalable and guaranteed solutions.
arXiv Detail & Related papers (2021-01-17T07:48:45Z) - Conditional canonical correlation estimation based on covariates with
random forests [0.0]
We propose a new method called Random Forest with Canonical Correlation Analysis (RFCCA) to estimate the conditional canonical correlations between two sets of variables.
The proposed method and the global significance test is evaluated through simulation studies that show it provides accurate canonical correlation estimations and well-controlled Type-1 error.
arXiv Detail & Related papers (2020-11-23T17:09:46Z) - Learning to Decouple Relations: Few-Shot Relation Classification with
Entity-Guided Attention and Confusion-Aware Training [49.9995628166064]
We propose CTEG, a model equipped with two mechanisms to learn to decouple easily-confused relations.
On the one hand, an EGA mechanism is introduced to guide the attention to filter out information causing confusion.
On the other hand, a Confusion-Aware Training (CAT) method is proposed to explicitly learn to distinguish relations.
arXiv Detail & Related papers (2020-10-21T11:07:53Z) - Relation of the Relations: A New Paradigm of the Relation Extraction
Problem [52.21210549224131]
We propose a new paradigm of Relation Extraction (RE) that considers as a whole the predictions of all relations in the same context.
We develop a data-driven approach that does not require hand-crafted rules but learns by itself the relation of relations (RoR) using Graph Neural Networks and a relation matrix transformer.
Experiments show that our model outperforms the state-of-the-art approaches by +1.12% on the ACE05 dataset and +2.55% on SemEval 2018 Task 7.2.
arXiv Detail & Related papers (2020-06-05T22:25:27Z) - Normalizing Compositional Structures Across Graphbanks [67.7047900945161]
We present a methodology for normalizing discrepancies between MRs at the compositional level.
Our work significantly increases the match in compositional structure between MRs and improves multi-task learning.
arXiv Detail & Related papers (2020-04-29T14:35:50Z)
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.