This paper presents an ontology of portions of matter with practical implications across scientific and industrial domains. The ontology is developed under the Unified Foundational Ontology (UFO), which uses the concept of quantity to represent topologically maximally self-connected portions of matter. The proposed ontology introduces the granuleOf parthood relation, holding between objects and portions of matter. It also discusses the constitution of quantities by collections of granules, the representation of sub-portions of matter, and the tracking of matter provenance between quantities using historical relations. Lastly, a case study is presented to demonstrate the use of the portion of matter ontology in the geology domain for an Oil & Gas industry application. In the case study, we model how to represent the historical relation between an original portion of rock and the sub-portions created during the industrial process. Lastly, future research directions are outlined, including investigating granularity levels and defining a taxonomy of events.
Free University of Bozen-Bolzano（博尔扎诺自由大学）；University of Milano-Bicocca（米兰比可卡大学）
Ontology-based data access (OBDA) is a popular paradigm for querying heterogeneous data sources by connecting them through mappings to an ontology. In OBDA, it is often difficult to reconstruct why a tuple occurs in the answer of a query. We address this challenge by enriching OBDA with provenance semirings, taking inspiration from database theory. In particular, we investigate the problems of (i) deciding whether a provenance annotated OBDA instance entails a provenance annotated conjunctive query, and (ii) computing a polynomial representing the provenance of a query entailed by a provenance…
Schulich School of Business, York University（约克大学舒立克商学院）；York University（约克大学）
An interesting research problem in our age of Big Data is that of determining provenance. Granular evaluation of provenance of physical goods--e.g. tracking ingredients of a pharmaceutical or demonstrating authenticity of luxury goods--has often not been possible with today's items that are produced and transported in complex, inter-organizational, often internationally-spanning supply chains. Recent adoption of Internet of Things and Blockchain technologies give promise at better supply chain provenance. We are particularly interested in the blockchain as many favoured use cases of blockchain…
PSL University（巴黎文理研究大学）；Inria（法国国家信息与自动化研究所）；University of Bergen（卑尔根大学）；Free University of Bozen-Bolzano（博尔扎诺自由大学）；University of Milano-Bicocca（米兰比可卡大学）
A semantic approach to mapping the Provenance Ontology to Basic Formal Ontology
National Center for Ontological Research（国家本体论研究中心）；Conceptual Systems（概念系统公司）；University at Buffalo（布法罗大学）；Summit Knowledge Solutions（顶峰知识解决方案公司）；University of Vienna（维也纳大学）；Institute for Artificial Intelligence and Data Science（人工智能与数据科学研究所）
Université de Lyon（里昂大学）；CNRS（法国国家科学研究中心）；École Nationale Supérieure des Mines（国立高等矿业学院）
Annotating semantic data with metadata is becoming more and more important to provide information about the statements being asserted. While initial solutions proposed a data model to represent a specific dimension of meta-information (such as time or provenance), the need for a general annotation framework which allows representing different context dimensions is needed. In this paper, we extend the 4dFluents ontology by Welty and Fikes---on associating temporal validity to statements---to any dimension of context, and discuss possible issues that multidimensional context representations have…
Virtual Process Dossier: A Process-Aware Data Catalogue
Institute of Applied Informatics and Formal Description Methods（应用信息学与形式描述方法研究所）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Institute of Production Sciences（生产科学研究所）；Institute for Anthropomatics and Robotics（人机系统与机器人研究所）；Fraunhofer Institute of Optronics, System Technologies and Image Exploitation（弗劳恩霍夫光学技术、系统技术与图像利用研究所）；Institute of Vehicle System Technology（车辆系统技术研究所）；Institute of Information Security and Dependability（信息安全与可靠性研究所）；Fraunhofer Institute for Chemical Technology（弗劳恩霍夫化学技术研究所）
Authoring and Management of Transparent Research Integrity Assessments of Randomised Clinical Trial Publications Using LLM-assisted Tools and Provenance Knowledge Graphs
Interdisciplinary Institute, University of Aberdeen（阿伯丁大学跨学科研究所）；University of Aberdeen（阿伯丁大学）；Indiana University（印第安纳大学）；University of Manchester（曼彻斯特大学）；Aberdeen Centre for Evaluation, University of Aberdeen（阿伯丁大学阿伯丁评估中心）；University of Auckland（奥克兰大学）
IDEA2: Expert-in-the-loop competency question elicitation for collaborative ontology engineering
School of Computer Science and Informatics, University of Liverpool（利物浦大学计算机科学与信息学院）；College of Computer Science and Engineering, Taibah University（塔伊巴大学计算机科学与工程学院）；Unilever Plc., Materials Innovation Factory, University of Liverpool（联合利华公司，利物浦大学材料创新工厂）
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Universidade de Fortaleza（福塔莱萨大学）
Significant efforts have been made to understand and document knowledge related to scientific measurements. Many of those efforts resulted in one or more high-quality ontologies that describe some aspects of scientific measurements, but not in a comprehensive and coherently integrated manner. For instance, we note that many of these high-quality ontologies are not properly aligned, and more challenging, that they have different and often conflicting concepts and approaches for encoding knowledge about empirical measurements. As a result of this lack of an integrated view, it is often challengi…
OntoKG-EQ: A provenance-grounded, competency-question-governed knowledge graph for auditable analyst querying
City University of Science and Information Technology (CUSIT)（城市科学与信息技术大学（CUSIT））；National University of Computer and Emerging Sciences (FAST-NUCES)（国立计算机与新兴科学大学（FAST-NUCES））；University of Engineering and Technology (UET)（工程技术大学（UET））
To provide a foundation for conceptual modeling, ontologies have been introduced to specify the entities, the existences of which are acknowledged in the model. Ontologies are essential components as mechanisms to model a portion of reality in software engineering. In this context, a model refers to a description of objects and processes that populate a system. Developing such a description constrains and directs the design, development, and use of the corresponding system, thus avoiding such difficulties as conflicts and lack of a common understanding. In this cross-area research between mode…
OntoMetric: An Ontology-Driven LLM-Assisted Framework for Automated ESG Metric Knowledge Graph Generation
School of Computer Science and Engineering, University of New South Wales（新南威尔士大学计算机科学与工程学院）；Faculty of Sciences, Engineering and Technology, The University of Adelaide（阿德莱德大学科学、工程与技术学院）；School of Information Systems and Technology Management, University of New South Wales（新南威尔士大学信息系统与技术管理学院）
Clinical factors account only for a small portion, about 10-30%, of the controllable factors that affect an individual's health outcomes. The remaining factors include where a person was born and raised, where he/she pursued their education, what their work and family environment is like, etc. These factors are collectively referred to as Social Determinants of Health (SDoH). The majority of SDoH data is recorded in unstructured clinical notes by physicians and practitioners. Recording SDoH data in a structured manner (in an EHR) could greatly benefit from a dedicated ontology of SDoH terms. O…
Analytic software tools and workflows are increasing in capability, complexity, number, and scale, and the integrity of our workflows is as important as ever. Specifically, we must be able to inspect the process of analytic workflows to assess (1) confidence of the conclusions, (2) risks and biases of the operations involved, (3) sensitivity of the conclusions to sources and agents, (4) impact and pertinence of various sources and agents, and (5) diversity of the sources that support the conclusions. We present an approach that tracks agents' provenance with PROV-O in conjunction with agents' …
