Logical theories in the form of ontologies and similar artefacts in computing and IT are used for structuring, annotating, and querying data, among others, and therewith influence data analytics regarding what is fed into the algorithms. Algorithmic bias is a well-known notion, but what does bias mean in the context of ontologies that provide a structuring mechanism for an algorithm's input? What are the sources of bias there and how would they manifest themselves in ontologies? We examine and enumerate types of bias relevant for ontologies, and whether they are explicit or implicit. These eight types are illustrated with examples from extant production-level ontologies and samples from the literature. We then assessed three concurrently developed COVID-19 ontologies on bias and detected different subsets of types of bias in each one, to a greater or lesser extent. This first characterisation aims contribute to a sensitisation of ethical aspects of ontologies primarily regarding representation of information and knowledge.
York University（约克大学）；Kingsdale Shareholder Services（金斯代尔股东服务公司）；Canadian Tire（加拿大轮胎公司）
We explore how computational ontologies can be impactful vis-a-vis the developing discipline of "data science." We posit an approach wherein management theories are represented as formal axioms, and then applied to draw inferences about data that reside in corporate databases. That is, management theories would be implemented as rules within a data analytics engine. We demonstrate a case study development of such an ontology by formally representing an accounting theory in First-Order Logic. Though quite preliminary, the idea that an information technology, namely ontologies, can potentially a…
In recent years ontologies enjoyed a growing popularity outside specialized AI communities. System engineering is no exception to this trend, with ontologies being proposed as a basis for several tasks in complex industrial implements, including system design, monitoring and diagnosis. In this paper, we consider four different contributions to system engineering wherein ontologies are instrumental to provide enhancements over traditional ad-hoc techniques. For each application, we briefly report the methodologies, the tools and the results obtained with the goal to provide an assessment of mer…
The Multilingual Semantic Web has been in focus for over a decade. Multilingualism in Linked Data and RDF has shown substantial adoption, but this is unclear for ontologies since the last review 15 years ago. One of the design goals for OWL was internationalisation, with the aim that an ontology is usable across languages and cultures. Much research to improve on multilingual ontologies has taken place in the meantime, and presumably multilingual linked data could use multilingual ontologies. Therefore, this review seeks to (i) elucidate and compare the modelling options for multilingual ontol…
Universitat Jaume I de Castelló（卡斯特利翁海梅一世大学）；European Bioinformatics Institute（欧洲生物信息研究所）；University of Oxford（牛津大学）
In this paper we present a preliminary logic-based evaluation of the integration of post-composed phenotypic descriptions with domain ontologies. The evaluation has been performed using a description logic reasoner together with scalable techniques: ontology modularization and approximations of the logical difference between ontologies.
Smart and continuous commissioning (SCCx) of buildings can result in a significant reduction in the gap between design and operational performance. Ontologies play an important role in SCCx as they facilitate data readability and reasoning by machines. A better understanding of ontologies is required in order to develop and incorporate them in SCCx. This paper critically reviews the state-of-the-art research on building data ontologies since 2014 within the SCCx domain through sorting them based on building data types, general approaches, and applications. The data types of two main domains of…
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Arizona（亚利桑那大学）；Mississippi State University（密西西比州立大学）
Community annotation of biological entities with concepts from multiple bio-ontologies has created large and growing repositories of ontology-based annotation data with embedded implicit relationships among orthogonal ontologies. Development of efficient data mining methods and metrics to mine and assess the quality of the mined relationships has not kept pace with the growth of annotation data. In this study, we present a data mining method that uses ontology-guided generalization to discover relationships across ontologies along with a new interestingness metric based on information theory. …
Université de Lorraine, CNRS, Inria, LORIA（洛林大学、法国国家科学研究中心、法国国家信息与自动化研究所、洛林计算机科学与应用研究实验室）
Features mined from knowledge graphs are widely used within multiple knowledge discovery tasks such as classification or fact-checking. Here, we consider a given set of vertices, called seed vertices, and focus on mining their associated neighboring vertices, paths, and, more generally, path patterns that involve classes of ontologies linked with knowledge graphs. Due to the combinatorial nature and the increasing size of real-world knowledge graphs, the task of mining these patterns immediately entails scalability issues. In this paper, we address these issues by proposing a pattern mining ap…
Iconology is a branch of art history that investigates the meaning of artworks in relation to their social and cultural background. Nowadays, several interdisciplinary research fields leverage theoretical frameworks close to iconology to pursue quantitative Art History with data science methods and Semantic Web technologies. However, while Iconographic studies have been recently addressed in ontologies, a complete description of aspects relevant to iconological studies is still missing. In this article, we present a preliminary study on eleven case studies selected from the literature and we e…
Ontology matching is a core task when creating interoperable and linked open datasets. In this paper, we explore a novel structure-based mapping approach which is based on knowledge graph embeddings: The ontologies to be matched are embedded, and an approach known as absolute orientation is used to align the two embedding spaces. Next to the approach, the paper presents a first, preliminary evaluation using synthetic and real-world datasets. We find in experiments with synthetic data, that the approach works very well on similarly structured graphs; it handles alignment noise better than size …
Bosch Center for Artificial Intelligence（博世人工智能中心）；Oslo Metropolitan University（奥斯陆城市大学）；University of Oslo（奥斯陆大学）；Nanjing University（南京大学）；City, University of London（伦敦城市大学）
Industrial analytics that includes among others equipment diagnosis and anomaly detection heavily relies on integration of heterogeneous production data. Knowledge Graphs (KGs) as the data format and ontologies as the unified data schemata are a prominent solution that offers high quality data integration and a convenient and standardised way to exchange data and to layer analytical applications over it. However, poor design of ontologies of high degree of mismatch between them and industrial data naturally lead to KGs of low quality that impede the adoption and scalability of industrial analy…
AIdentifyAGE Ontology for Decision Support in Forensic Dental Age Assessment
InESC-ID Lisboa（里斯本INESC-ID）；Instituto Superior Técnico（技术学院）；Faculdade de Medicina Dentária da Universidade de Lisboa（里斯本大学牙医学院）；FCiências.ID - Centro de Estatística e Aplicações da Universidade de Lisboa (CEAUL)（里斯本大学FCiências.ID）；Instituto Politécnico de Leiria - Escola Superior de Tecnologia e Gestão（莱里亚技术学院）；IST-ID；Instituto Superior de Engenharia de Lisboa（里斯本工程学院）
Technical University Eindhoven（埃因霍温理工大学）；Jheronimus Academy of Data Science（杰罗尼穆斯数据科学学院）；Tilburg University（蒂尔堡大学）
Big Data analytics supported by AI algorithms can support skills localization and retrieval in the context of a labor market intelligence problem. We formulate and solve this problem through specific DataOps models, blending data sources from administrative and technical partners in several countries into cooperation, creating shared knowledge to support policy and decision-making. We then focus on the critical task of skills extraction from resumes and vacancies featuring state-of-the-art machine learning models. We showcase preliminary results with applied machine learning on real data from …
Data quality assessment and data cleaning are context-dependent activities. Motivated by this observation, we propose the Ontological Multidimensional Data Model (OMD model), which can be used to model and represent contexts as logic-based ontologies. The data under assessment is mapped into the context, for additional analysis, processing, and quality data extraction. The resulting contexts allow for the representation of dimensions, and multidimensional data quality assessment becomes possible. At the core of a multidimensional context we include a generalized multidimensional data model and…
The strength of chess engines together with the availability of numerous chess games have attracted the attention of chess players, data scientists, and researchers during the last decades. State-of-the-art engines now provide an authoritative judgement that can be used in many applications like cheating detection, intrinsic ratings computation, skill assessment, or the study of human decision-making. A key issue for the research community is to gather a large dataset of chess games together with the judgement of chess engines. Unfortunately the analysis of each move takes lots of times. In th…
In online advertising, our aim is to match the advertisers with the most relevant users to optimize the campaign performance. In the pursuit of achieving this goal, multiple data sources provided by the advertisers or third-party data providers are utilized to choose the set of users according to the advertisers' targeting criteria. In this paper, we present a framework that can be applied to assess the quality of such data sources in large scale. This framework efficiently evaluates the similarity of a specific data source categorization to that of the ground truth, especially for those cases…
Predictive Modeling and Explainable AI for Veterinary Safety Profiles, Residue Assessment, and Health Outcomes Using Real-World Data and Physicochemical Properties
DATA Consortium and FARAD Program, Kansas State University, Olathe, KS, USA（1DATA Consortium和FARAD计划，堪萨斯州立大学）；Department of Computer Science, Kansas State University, Manhattan, KS, USA（计算机科学系，堪萨斯州立大学）；Department of Statistics, Kansas State University, Manhattan, KS, USA（统计学系，堪萨斯州立大学）；Department of Mathematics, Kansas State University, Olathe, KS, USA（数学系，堪萨斯州立大学）
