SPARQLByE

Diaz, Gonzalo I.; Arenas, Marcelo; Benedikt, Michael

doi:10.14778/3007263.3007302

Cited by 31 publications

(3 citation statements)

References 7 publications

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“…1), we define the following reverse semantic query answering problem: "given a set of exemplars and their semantic description find intuitive and understandable semantic queries that have as certain answers exactly this set out of all the exemplars of the explanation dataset". The specific problem is interesting, with certain difficulties and computationally very demanding [3,11,19,20,59]. Here, by extending a method presented in [48] we present an Explainer (see Fig.…”

Section: Explaining Opaque Machine Learning Classifiersmentioning

confidence: 99%

Searching for explanations of black-box classifiers in the space of semantic queries

Liartis

Dervakos

Menis-Mastromichalakis

et al. 2023

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Deep learning models have achieved impressive performance in various tasks, but they are usually opaque with regards to their inner complex operation, obfuscating the reasons for which they make decisions. This opacity raises ethical and legal concerns regarding the real-life use of such models, especially in critical domains such as in medicine, and has led to the emergence of the eXplainable Artificial Intelligence (XAI) field of research, which aims to make the operation of opaque AI systems more comprehensible to humans. The problem of explaining a black-box classifier is often approached by feeding it data and observing its behaviour. In this work, we feed the classifier with data that are part of a knowledge graph, and describe the behaviour with rules that are expressed in the terminology of the knowledge graph, that is understandable by humans. We first theoretically investigate the problem to provide guarantees for the extracted rules and then we investigate the relation of “explanation rules for a specific class” with “semantic queries collecting from the knowledge graph the instances classified by the black-box classifier to this specific class”. Thus we approach the problem of extracting explanation rules as a semantic query reverse engineering problem. We develop algorithms for solving this inverse problem as a heuristic search in the space of semantic queries and we evaluate the proposed algorithms on four simulated use-cases and discuss the results.

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Section: Explaining Opaque Machine Learning Classifiersmentioning

confidence: 99%

Searching for explanations of black-box classifiers in the space of semantic queries

Liartis

Dervakos

Menis-Mastromichalakis

et al. 2023

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“…Vistrails's Spreadsheet View [9] (see Figure 5) or Graph2Tab [36] are examples. We believe the presentation of (provenance) graphs and presentation of results is separate and very important research thread, which we has not yet received sufficient attention [37]. In this paper, we focus on provenance queries that underpin reports.…”

Section: Using Provenance For Reportingmentioning

confidence: 99%

LabelFlow Framework for Annotating Workflow Provenance

et al. 2018

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Abstract:Scientists routinely analyse and share data for others to use. Successful data (re)use relies on having metadata describing the context of analysis of data. In many disciplines the creation of contextual metadata is referred to as reporting. One method of implementing analyses is with workflows. A stand-out feature of workflows is their ability to record provenance from executions. Provenance is useful when analyses are executed with changing parameters (changing contexts) and results need to be traced to respective parameters. In this paper we investigate whether provenance can be exploited to support reporting. Specifically; we outline a case-study based on a real-world workflow and set of reporting queries. We observe that provenance, as collected from workflow executions, is of limited use for reporting, as it supports queries partially. We identify that this is due to the generic nature of provenance, its lack of domain-specific contextual metadata. We observe that the required information is available in implicit form, embedded in data. We describe LabelFlow, a framework comprised of four Labelling Operators for decorating provenance with domain-specific Labels. LabelFlow can be instantiated for a domain by plugging it with domain-specific metadata extractors. We provide a tool that takes as input a workflow, and produces as output a Labelling Pipeline for that workflow, comprised of Labelling Operators. We revisit the case-study and show how Labels provide a more complete implementation of reporting queries.

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“…To make easier the availability of structured knowledge even to novice users, the research community came up with different techniques. Examples are: exemplar queries [6,7,8] based on the idea that it is enough for users to provide even a single exemplar result to automatically infer the desired answer set, query learning approaches [2] that feature interactive join specification supports with minimal interactions, and reverse engineering of queries [3] where the goal is to learn queries given a set of tuple in the results. Other approaches [4,9] specifically focus on providing relatedness explanations.…”

Section: Introductionmentioning

confidence: 99%

Explaining and querying knowledge graphs by relatedness

Fionda

Pirrò

2017

Proc. VLDB Endow.

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We demonstrate RECAP, a tool that explains relatedness between entities in Knowledge Graphs (KGs) and implements a query by relatedness paradigm that allows to retrieve entities related to those in input. One of the peculiarities of RECAP is that it does not require any data preprocessing and can combine knowledge from multiple KGs. The underlying algorithmic techniques are reduced to the execution of SPARQL queries plus some local refinement. This makes the tool readily available on a large variety of KGs accessible via SPARQL endpoints. To show the general applicability of the tool, we will cover a set of use cases drawn from a variety of knowledge domains (e.g., biology, movies, co-authorship networks) and report on the concrete usage of RECAP in the SENSE4US FP7 project. We will underline the technical aspects of the system and give details on its implementation. The target audience of the demo includes both researchers and practitioners and aims at reporting on the benefits of RECAP in practical knowledge discovery applications.

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SPARQLByE

Cited by 31 publications

References 7 publications

Searching for explanations of black-box classifiers in the space of semantic queries

Searching for explanations of black-box classifiers in the space of semantic queries

LabelFlow Framework for Annotating Workflow Provenance

Explaining and querying knowledge graphs by relatedness

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