Connection-minimal Abduction in EL via Translation to FOL -- Technical
Report
- URL: http://arxiv.org/abs/2205.08449v1
- Date: Tue, 17 May 2022 15:50:27 GMT
- Title: Connection-minimal Abduction in EL via Translation to FOL -- Technical
Report
- Authors: Fajar Haifani, Patrick Koopmann, Sophie Tourret and Christoph
Weidenbach
- Abstract summary: We show how to compute a class of connection-minimal hypotheses in a sound and complete way.
Our technique is based on a translation to first-order logic, and constructs hypotheses based on prime implicates.
- Score: 12.90382979353427
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Abduction in description logics finds extensions of a knowledge base to make
it entail an observation. As such, it can be used to explain why the
observation does not follow, to repair incomplete knowledge bases, and to
provide possible explanations for unexpected observations. We consider TBox
abduction in the lightweight description logic EL, where the observation is a
concept inclusion and the background knowledge is a TBox, i.e., a set of
concept inclusions. To avoid useless answers, such problems usually come with
further restrictions on the solution space and/or minimality criteria that help
sort the chaff from the grain. We argue that existing minimality notions are
insufficient, and introduce connection minimality. This criterion follows
Occam's razor by rejecting hypotheses that use concept inclusions unrelated to
the problem at hand. We show how to compute a special class of
connection-minimal hypotheses in a sound and complete way. Our technique is
based on a translation to first-order logic, and constructs hypotheses based on
prime implicates. We evaluate a prototype implementation of our approach on
ontologies from the medical domain.
Related papers
- Advancing Abductive Reasoning in Knowledge Graphs through Complex Logical Hypothesis Generation [43.26412690886471]
This paper introduces the task of complex logical hypothesis generation, as an initial step towards abductive logical reasoning with Knowledge Graph.
We find that the supervised trained generative model can generate logical hypotheses that are structurally closer to the reference hypothesis.
We introduce the Reinforcement Learning from Knowledge Graph (RLF-KG) method, which minimizes differences between observations and conclusions drawn from generated hypotheses according to the KG.
arXiv Detail & Related papers (2023-12-25T08:06:20Z) - Eliminating Unintended Stable Fixpoints for Hybrid Reasoning Systems [5.208405959764274]
We introduce a methodology resembling AFT that can utilize priorly computed upper bounds to more precisely capture semantics.
We demonstrate our framework's applicability to hybrid MKNF (minimal knowledge and negation as failure) knowledge bases by extending the state-of-the-art approximator.
arXiv Detail & Related papers (2023-07-21T01:08:15Z) - Learnability with PAC Semantics for Multi-agent Beliefs [38.88111785113001]
The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence.
Valiant recognised that the challenge of learning should be integrated with deduction.
Although weaker than classical entailment, it allows for a powerful model-theoretic framework for answering queries.
arXiv Detail & Related papers (2023-06-08T18:22:46Z) - Abductive Commonsense Reasoning Exploiting Mutually Exclusive
Explanations [118.0818807474809]
Abductive reasoning aims to find plausible explanations for an event.
Existing approaches for abductive reasoning in natural language processing often rely on manually generated annotations for supervision.
This work proposes an approach for abductive commonsense reasoning that exploits the fact that only a subset of explanations is correct for a given context.
arXiv Detail & Related papers (2023-05-24T01:35:10Z) - Log-linear Guardedness and its Implications [116.87322784046926]
Methods for erasing human-interpretable concepts from neural representations that assume linearity have been found to be tractable and useful.
This work formally defines the notion of log-linear guardedness as the inability of an adversary to predict the concept directly from the representation.
We show that, in the binary case, under certain assumptions, a downstream log-linear model cannot recover the erased concept.
arXiv Detail & Related papers (2022-10-18T17:30:02Z) - NELLIE: A Neuro-Symbolic Inference Engine for Grounded, Compositional, and Explainable Reasoning [59.16962123636579]
This paper proposes a new take on Prolog-based inference engines.
We replace handcrafted rules with a combination of neural language modeling, guided generation, and semi dense retrieval.
Our implementation, NELLIE, is the first system to demonstrate fully interpretable, end-to-end grounded QA.
arXiv Detail & Related papers (2022-09-16T00:54:44Z) - Joint Abductive and Inductive Neural Logical Reasoning [44.36651614420507]
We formulate the problem of the joint abductive and inductive neural logical reasoning (AI-NLR)
First, we incorporate description logic-based ontological axioms to provide the source of concepts.
Then, we represent concepts and queries as fuzzy sets, i.e., sets whose elements have degrees of membership, to bridge concepts and queries with entities.
arXiv Detail & Related papers (2022-05-29T07:41:50Z) - 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) - Visual Abductive Reasoning [85.17040703205608]
Abductive reasoning seeks the likeliest possible explanation for partial observations.
We propose a new task and dataset, Visual Abductive Reasoning ( VAR), for examining abductive reasoning ability of machine intelligence in everyday visual situations.
arXiv Detail & Related papers (2022-03-26T10:17:03Z) - Abstract Reasoning via Logic-guided Generation [65.92805601327649]
Abstract reasoning, i.e., inferring complicated patterns from given observations, is a central building block of artificial general intelligence.
This paper aims to design a framework for the latter approach and bridge the gap between artificial and human intelligence.
We propose logic-guided generation (LoGe), a novel generative DNN framework that reduces abstract reasoning as an optimization problem in propositional logic.
arXiv Detail & Related papers (2021-07-22T07:28:24Z) - Signature-Based Abduction with Fresh Individuals and Complex Concepts
for Description Logics (Extended Version) [12.107259467873092]
ABox abduction aims at computing a hypothesis that, when added to the knowledge base, is sufficient to entail the observation.
In signature-based ABox abduction, the hypothesis is further required to use only names from a given set.
It is possible that hypotheses for a given observation only exist if we admit the use of fresh individuals and/or complex concepts built from the given signature.
arXiv Detail & Related papers (2021-05-01T14:55:46Z)
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.