Query Answering Explanation in Inconsistent Datalog$$+/-$$ Knowledge Bases

Arioua, Abdallah; Tamani, Nouredine; Croitoru, Madalina

doi:10.1007/978-3-319-22849-5_15

Cited by 17 publications

(27 citation statements)

References 18 publications

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“…Argumentation is indeed well-suited for explainable reasoning [6,40,70] with argumentative explanations proposed in various settings, see e.g. [1][2][3]5,14,16,18,[21][22][23][24]26,28,31,32,[37][38][39]42,43,51,57,58,62,64,66,69,70,77,79,81,[84][85][86][87][88]90,93,101,103,104,[110][111][112]. We hope to exploit the well-established as well as novel ABA + mechanisms to our advantage of providing various explanations to accompany the decisions supported by ABA + G. In addition to several other future work directions mentioned in Sections 6 and 7, we will aim…”

Section: Discussionmentioning

confidence: 99%

“…Currently, an end-to-end proof-of-concept system encompassing electronic health record (EHR) information about patients, TMR via its implementation TMRweb, and ABA + G to provide decision support to clinicians is under development within the ROAD2H project. 5 In this paper we provide the theoretical framework for both ABA + G and its implementation 6 which is compatible with a wrapper interface that integrates TMRweb, EHR hooks and other relevant functionalities (such as for preference elicitation). The specification of algorithms and other engineering details pertaining to this implementation of ABA + G is beyond the scope of this paper and is left for better suited future publications describing the overall decision support system.…”

Section: Introductionmentioning

confidence: 99%

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Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines

Čyras

Oliveira

Karamlou

et al. 2021

AAC

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A paramount, yet unresolved issue in personalised medicine is that of automated reasoning with clinical guidelines in multimorbidity settings. This entails enabling machines to use computerised generic clinical guideline recommendations and patient-specific information to yield patient-tailored recommendations where interactions arising due to multimorbidities are resolved. This problem is further complicated by patient management desiderata, in particular the need to account for patient-centric goals as well as preferences of various parties involved. We propose to solve this problem of automated reasoning with interacting guideline recommendations in the context of a given patient by means of computational argumentation. In particular, we advance a structured argumentation formalism ABA+G (short for Assumption-Based Argumentation with Preferences (ABA+) and Goals) for integrating and reasoning with information about recommendations, interactions, patient’s state, preferences and prioritised goals. ABA+G combines assumption-based reasoning with preferences and goal-driven selection among reasoning outcomes. Specifically, we assume defeasible applicability of guideline recommendations with the general goal of patient well-being, resolve interactions (conflicts and otherwise undesirable situations) among recommendations based on the state and preferences of the patient, and employ patient-centered goals to suggest interaction-resolving, goal-importance maximising and preference-adhering recommendations. We use a well-established Transition-based Medical Recommendation model for representing guideline recommendations and identifying interactions thereof, and map the components in question, together with the given patient’s state, prioritised goals, and preferences over actions, to ABA+G for automated reasoning. In this, we follow principles of patient management and establish corresponding theoretical properties as well as illustrate our approach in realistic personalised clinical reasoning scenaria.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines

Čyras

Oliveira

Karamlou

et al. 2021

AAC

View full text Add to dashboard Cite

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“…The approach has been implemented by exploiting different functionalities of SAT solvers and integrated into the CQAPri system. Closely related is a line of work [3,2] on utilizing argumentation and dialogues with users to explain query answers under various inconsistency-tolerant semantics (ICR, IAR, brave, and AR).…”

Section: Related Reasoning Services For Inconsistency Handlingmentioning

confidence: 99%

A Short Survey on Inconsistency Handling in Ontology-Mediated Query Answering

Bienvenu

2020

Künstl Intell

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This paper provides a concise overview of the literature on inconsistency handling for ontologymediated query answering, a topic which has grown into an active area of research over the last decade. The focus of this survey is on the case where errors are localized in the data (i.e., the ontology is deemed reliable) and where inconsistency-tolerant semantics are employed with the aim of obtaining meaningful information from inconsistent knowledge bases.

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“…Probably the closest related work is by Arioua, Tamani, and Croitoru (2015) who introduce an argumentation framework for explaining positive and negative answers under the inconsistency-tolerant semantics ICR. Their motivations are quite similar to our own, and there are some high-level similarities in the definition of explanations (e.g.…”

Section: Explanation Of Query Answersmentioning

confidence: 99%

Computing and Explaining Query Answers over Inconsistent DL-Lite Knowledge Bases

Bourgaux¹,

Goasdoué²

2019

jair

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Several inconsistency-tolerant semantics have been introduced for querying inconsistent description logic knowledge bases. The first contribution of this paper is a practical approach for computing the query answers under three well-known such semantics, namely the AR, IAR and brave semantics, in the lightweight description logic DL-LiteR. We show that query answering under the intractable AR semantics can be performed efficiently by using IAR and brave semantics as tractable approximations and encoding the AR entailment problem as a propositional satisfiability (SAT) problem. The second issue tackled in this work is explaining why a tuple is a (non-)answer to a query under these semantics. We define explanations for positive and negative answers under the brave, AR and IAR semantics. We then study the computational properties of explanations in DL-LiteR. For each type of explanation, we analyze the data complexity of recognizing (preferred) explanations and deciding if a given assertion is relevant or necessary. We establish tight connections between intractable explanation problems and variants of SAT, enabling us to generate explanations by exploiting solvers for Boolean satisfaction and optimization problems. Finally, we empirically study the efficiency of our query answering and explanation framework using a benchmark we built upon the well-established LUBM benchmark.

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Query Answering Explanation in Inconsistent Datalog$$+/-$$ Knowledge Bases

Cited by 17 publications

References 18 publications

Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines

Assumption-based argumentation with preferences and goals for patient-centric reasoning with interacting clinical guidelines

A Short Survey on Inconsistency Handling in Ontology-Mediated Query Answering

Computing and Explaining Query Answers over Inconsistent DL-Lite Knowledge Bases

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