12 research outputs found

    Redundant Elements in SNOMED CT Concept Definitions

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    Deductive Module Extraction for Expressive Description Logics: Extended Version

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    In deductive module extraction, we determine a small subset of an ontology for a given vocabulary that preserves all logical entailments that can be expressed in that vocabulary. While in the literature stronger module notions have been discussed, we argue that for applications in ontology analysis and ontology reuse, deductive modules, which are decidable and potentially smaller, are often sufficient. We present methods based on uniform interpolation for extracting different variants of deductive modules, satisfying properties such as completeness, minimality and robustness under replacements, the latter being particularly relevant for ontology reuse. An evaluation of our implementation shows that the modules computed by our method are often significantly smaller than those computed by existing methods.This is an extended version of the article in the proceedings of IJCAI 2020

    Specifying who delivers behaviour change interventions: development of an Intervention Source Ontology [version 1; peer review: awaiting peer review]

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    BACKGROUND: Identifying how behaviour change interventions are delivered, including by whom, is key to understanding intervention effectiveness. However, information about who delivers interventions is reported inconsistently in intervention evaluations, limiting communication and knowledge accumulation. This paper reports a method for consistent reporting: The Intervention Source Ontology. This forms one part of the Behaviour Change Intervention Ontology, which aims to cover all aspects of behaviour change interventions. METHODS: The Intervention Source Ontology was developed following methods for ontology development and maintenance used in the Human Behaviour-Change Project, with seven key steps: 1) define the scope of the ontology, 2) identify key entities and develop their preliminary definitions by reviewing existing classification systems (top-down) and reviewing 100 behaviour change intervention reports (bottom-up), 3) refine the ontology by piloting the preliminary ontology on 100 reports, 4) stakeholder review by 34 behavioural science and public health experts, 5) inter-rater reliability testing of annotating intervention reports using the ontology, 6) specify ontological relationships between entities and 7) disseminate and maintain the Intervention Source Ontology. RESULTS: The Intervention Source Ontology consists of 140 entities. Key areas of the ontology include Occupational Role of Source, Relatedness between Person Source and the Target Population, Sociodemographic attributes and Expertise. Inter-rater reliability was found to be 0.60 for those familiar with the ontology and 0.59 for those unfamiliar with it, levels of agreement considered ‘acceptable’. CONCLUSIONS: Information about who delivers behaviour change interventions can be reliably specified using the Intervention Source Ontology. For human-delivered interventions, the ontology can be used to classify source characteristics in existing behaviour change reports and enable clearer specification of intervention sources in reporting

    Interactive semantic feedback for intuitive ontology authoring

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    The complexity of ontology authoring and the difficulty to master the use of existing ontology authoring tools, put significant constraints on the involvement of both domain experts and knowledge engineers in ontology authoring. This often requires substantial effort for fixing ontologies defects (e.g. inconsistency, unsatisfiability, missing or unintended implications, redundancy, isolated entities). The paper argues that ontology authoring tools should provide immediate semantic feedback upon entering ontological constructs. We present a framework to analyse input axioms and provide meaningful feedback at a semantic level. The framework has been used to augment an existing Controlled Natural Language-based ontology authoring tool – ROO. An experimental study with ROO has been conducted to examine users' reactions to the semantic feedback and the effect on their ontology authoring behaviour. The study strongly supported responsive intuitive ontology authoring tools, and identified future directions to extend and integrate semantic feedback

    Specifying who delivers behaviour change interventions: development of an Intervention Source Ontology

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    Copyright: © 2021 Norris, E. et al. Background:  Identifying how behaviour change interventions are delivered, including by whom, is key to understanding intervention effectiveness. However, information about who delivers interventions is reported inconsistently in intervention evaluations, limiting communication and knowledge accumulation. This paper reports a method for consistent reporting: The Intervention Source Ontology. This forms one part of the Behaviour Change Intervention Ontology, which aims to cover all aspects of behaviour change interventions. Methods: The Intervention Source Ontology was developed following methods for ontology development and maintenance used in the Human Behaviour-Change Project, with seven key steps: 1) define the scope of the ontology, 2) identify key entities and develop their preliminary definitions by reviewing existing classification systems (top-down) and reviewing 100 behaviour change intervention reports (bottom-up), 3) refine the ontology by piloting the preliminary ontology on 100 reports, 4) stakeholder review by 34 behavioural science and public health experts, 5) inter-rater reliability testing of annotating intervention reports using the ontology, 6) specify ontological relationships between entities and 7) disseminate and maintain the Intervention Source Ontology. Results: The Intervention Source Ontology consists of 140 entities. Key areas of the ontology include Occupational Role of Source, Relatedness between Person Source and the Target Population, Sociodemographic attributes and Expertise. Inter-rater reliability was found to be 0.60 for those familiar with the ontology and 0.59 for those unfamiliar with it, levels of agreement considered ‘acceptable’. Conclusions: Information about who delivers behaviour change interventions can be reliably specified using the Intervention Source Ontology. For human-delivered interventions, the ontology can be used to classify source characteristics in existing behaviour change reports and enable clearer specification of intervention sources in reporting.Wellcome Trust collaborative award to the Human Behaviour-Change Project [201524]

    Automatic & Semi-Automatic Methods for Supporting Ontology Change

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    Active Learning for Reducing Labeling Effort in Text Classification Tasks

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    Labeling data can be an expensive task as it is usually performed manually by domain experts. This is cumbersome for deep learning, as it is dependent on large labeled datasets. Active learning (AL) is a paradigm that aims to reduce labeling effort by only using the data which the used model deems most informative. Little research has been done on AL in a text classification setting and next to none has involved the more recent, state-of-the-art Natural Language Processing (NLP) models. Here, we present an empirical study that compares different uncertainty-based algorithms with BERTbase_{base} as the used classifier. We evaluate the algorithms on two NLP classification datasets: Stanford Sentiment Treebank and KvK-Frontpages. Additionally, we explore heuristics that aim to solve presupposed problems of uncertainty-based AL; namely, that it is unscalable and that it is prone to selecting outliers. Furthermore, we explore the influence of the query-pool size on the performance of AL. Whereas it was found that the proposed heuristics for AL did not improve performance of AL; our results show that using uncertainty-based AL with BERTbase_{base} outperforms random sampling of data. This difference in performance can decrease as the query-pool size gets larger.Comment: Accepted as a conference paper at the joint 33rd Benelux Conference on Artificial Intelligence and the 30th Belgian Dutch Conference on Machine Learning (BNAIC/BENELEARN 2021). This camera-ready version submitted to BNAIC/BENELEARN, adds several improvements including a more thorough discussion of related work plus an extended discussion section. 28 pages including references and appendice
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