A Knowledge-Based Computational Environment for Real-World Data Processing

Tianxing, Man; Zhukova, Nataly; Meltsov, Vasily; Shichkina, Yulia

doi:10.1007/978-3-030-24289-3_20

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Ontologies which describe AI systems and processes that lead to their creation have also been proposed (e.g. MEX [9], ML Schema [22], and KBCE [27]). However, tools to support community uptake (e.g.…”

Section: Related Workmentioning

confidence: 99%

A Semantic Framework to Support AI System Accountability and Audit

Naja

Marković

Edwards

et al. 2021

Lecture Notes in Computer Science

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To realise accountable AI systems, different types of information from a range of sources need to be recorded throughout the system life cycle. We argue that knowledge graphs can support capture and audit of such information; however, the creation of such accountability records must be planned and embedded within different life cycle stages, e.g. during the design of a system, during implementation, etc. We propose a provenance based approach to support not only the capture of accountability information, but also abstract descriptions of accountability plans that guide the data collection process, all as part of a single knowledge graph. In this paper we introduce the SAO ontology, a lightweight generic ontology for describing accountability plans and corresponding provenance traces of computational systems; the RAInS ontology, which extends SAO to model accountability information relevant to the design stage of AI systems; and a proof-of-concept implementation utilising the proposed ontologies to provide a visual interface for designing accountability plans, and managing accountability records.

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Section: Related Workmentioning

confidence: 99%

A Semantic Framework to Support AI System Accountability and Audit

Naja

Marković

Edwards

et al. 2021

Lecture Notes in Computer Science

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“…Hilario et al (2009) present the data mining optimization ontology (DMOP), which provides a unified conceptual framework for analyzing data mining tasks, algorithms, models, datasets, workflows, and performance metrics, as well as their relationships ( Keet et al, 2015 ). There are several other data mining ontologies currently existing, such as the Knowledge Discovery (KD) Ontology ( Žáková et al, 2010 ; Tianxing et al, 2019 ), the OntoDTA ontology ( Benali & Rahal, 2017 ), the KDDONTO Ontology ( Diamantini, Potena & Storti, 2009 ), the Data Mining Workflow (DMWF) Ontology ( Kietz et al, 2009 ), which are based on similar ideas. These ontologies present the description of DM knowledge in general, for specific domain data they don’t provide targeted support.…”

Section: Introductionmentioning

confidence: 99%

An ontology-based approach to the analysis of the acid-base state of patients at operative measures

Tianxing

Lushnov

Ignatov

et al. 2021

PeerJ Computer Science

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Researchers working in various domains are focusing on extracting information from data sets by data mining techniques. However, data mining is a complicated task, including multiple complex processes, so that it is unfriendly to non-computer researchers. Due to the lack of experience, they cannot design suitable workflows that lead to satisfactory results. This article proposes an ontology-based approach to help users choose appropriate data mining techniques for analyzing domain data. By merging with domain ontology and extracting the corresponding sub-ontology based on the task requirements, an ontology oriented to a specific domain is generated that can be used for algorithm selection. Users can query for suitable algorithms according to the current data characteristics and task requirements step by step. We build a workflow to analyze the Acid-Base State of patients at operative measures based on the proposed approach and obtain appropriate conclusions.

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