With the adoption of Semantic Web technologies, an increasing number of vocabularies and ontologies have been developed in different domains, ranging from Biology to Agronomy or Geosciences. However, many of these ontologies are still difficult to find, access and understand by researchers due to a lack of documentation, URI resolving issues, versioning problems, etc. In this chapter we describe guidelines and best practices for creating accessible, understandable and reusable ontologies on the Web, using standard practices and pointing to existing tools and frameworks developed by the Semantic Web community. We illustrate our guidelines with concrete examples, in order to help researchers implement these practices in their future vocabularies.
Ontologies for increasing the FAIRness of plant research data
Forschungszentrum Jülich GmbH（于利希研究中心）；Bioeconomy Science Center (BioSC)（生物经济科学中心）；CEPLAS（植物科学卓越集群）；Bioversity International（生物多样性国际）；Heinrich Heine University Düsseldorf（杜塞尔多夫海因里希海涅大学）；University of Cologne（科隆大学）；Leibniz Institute of Plant Biochemistry（莱布尼茨植物生物化学研究所）；German Centre for Integrative Biodiversity Research (iDiv)（德国综合生物多样性研究中心）
Prospective studies require discussing and collaborating with the stakeholders to create scenarios of the possible evolution of the studied value-chain. However, stakeholders don't always use the same words when referring to one idea. Constructing an ontology and homogenizing vocabularies is thus crucial to identify key variables which serve in the construction of the needed scenarios. Nevertheless, it is a very complex and timeconsuming task. In this paper we present the method we used to manually build ontologies adapted to the needs of two complementary system-analysis models (namely the "G…
University of Colorado Anschutz Medical Campus（科罗拉多大学安舒茨医学校区）；Columbia University Irving Medical Center（哥伦比亚大学欧文医学中心）；National Human Genome Research Institute, National Institutes of Health（美国国立卫生研究院国家人类基因组研究所）；Georgia State University（佐治亚州立大学）；University of Pittsburgh School of Medicine（匹兹堡大学医学院）；Università degli Studi di Milano（米兰大学）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；The Jackson Laboratory for Genomic Medicine（杰克逊基因组医学实验室）；University of Cambridge（剑桥大学）；University of Colorado Anschutz School of Medicine（科罗拉多大学安舒茨医学院）；Semanticly（Semanticly公司）；Yale School of Medicine（耶鲁大学医学院）；HealthLinc（HealthLinc健康中心）；University of Pittsburgh（匹兹堡大学）；Tufts University（塔夫茨大学）；Sema4（Sema4公司）；Janssen Research and Development（杨森研发）
Background: Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate all the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. Objective: We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO…
Stanford University（斯坦福大学）；Graz University of Technology（格拉茨技术大学）；GESIS（莱布尼茨社会科学研究所）；University of Koblenz-Landau（科布伦茨-兰道大学）
Ontologies in the biomedical domain are numerous, highly specialized and very expensive to develop. Thus, a crucial prerequisite for ontology adoption and reuse is effective support for exploring and finding existing ontologies. Towards that goal, the National Center for Biomedical Ontology (NCBO) has developed BioPortal---an online repository designed to support users in exploring and finding more than 500 existing biomedical ontologies. In 2016, BioPortal represents one of the largest portals for exploration of semantic biomedical vocabularies and terminologies, which is used by many researc…
Vrije Universiteit, Amsterdam（阿姆斯特丹自由大学）；International Institute of Social History, Amsterdam（国际社会史研究所）；KNAW Humanities Cluster, DHLab, Amsterdam（荷兰皇家艺术与科学学院人文集群数字人文实验室）；University of Stirling, Stirling（斯特灵大学）；Utrecht University, Utrecht（乌得勒支大学）；Netherlands Institute for Sound and Vision, Hilversum（荷兰声音与视觉研究所）
One of the most important goals of digital humanities is to provide researchers with data and tools for new research questions, either by increasing the scale of scholarly studies, linking existing databases, or improving the accessibility of data. Here, the FAIR principles provide a useful framework as these state that data needs to be: Findable, as they are often scattered among various sources; Accessible, since some might be offline or behind paywalls; Interoperable, thus using standard knowledge representation formats and shared vocabularies; and Reusable, through adequate licensing and p…
Ontologies for Models and Algorithms in Applied Mathematics and Related Disciplines
University of Stuttgart（斯图加特大学）；University of Münster（明斯特大学）；Fraunhofer Institute for Industrial Mathematics（弗劳恩霍夫工业数学研究所）；Zuse Institute Berlin（柏林祖斯研究所）；Weierstrass Institute for Applied Analysis and Stochastics（魏尔斯特拉斯应用分析与随机学研究所）；University Library, University of Stuttgart（斯图加特大学图书馆）；Stuttgart Center for Simulation Science (SC SimTech), University of Stuttgart（斯图加特大学斯图加特模拟科学中心）
Lifelong learners must recognize concept vocabularies that evolve over time. A common yet underexplored scenario is learning with class labels that continually refine/expand old classes. For example, humans learn to recognize ${\tt dog}$ before dog breeds. In practical settings, dataset $\textit{versioning}$ often introduces refinement to ontologies, such as autonomous vehicle benchmarks that refine a previous ${\tt vehicle}$ class into ${\tt school-bus}$ as autonomous operations expand to new cities. This paper formalizes a protocol for studying the problem of $\textit{Learning with Evolving …
Pattern-based, modular ontologies have several beneficial properties that lend themselves to FAIR data practices, especially as it pertains to Interoperability and Reusability. However, developing such ontologies has a high upfront cost, e.g. reusing a pattern is predicated upon being aware of its existence in the first place. Thus, to help overcome these barriers, we have developed MODL: a modular ontology design library. MODL is a curated collection of well-documented ontology design patterns, drawn from a wide variety of interdisciplinary use-cases. In this paper we present MODL as a resour…
Towards a Knowledge Graph for Models and Algorithms in Applied Mathematics
University of Stuttgart（斯图加特大学）；University of Münster（明斯特大学）；Weierstrass Institute for Applied Analysis and Stochastics（魏尔斯特拉斯应用分析与随机学研究所）；Zuse Institute Berlin（柏林朱利叶斯·普朗克计算机科学研究所）；Fraunhofer Institute for Industrial Mathematics（弗劳恩霍夫工业数学研究所）
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）
Institute of Data Science, Maastricht University（马斯特里赫特大学数据科学学院）；VU University Amsterdam（阿姆斯特丹自由大学）；Pharmacology & Personalised Medicine, Maastricht University（马斯特里赫特大学药理学与个性化医学学院）；Stanford University（斯坦福大学）；Netherlands eScience Center（荷兰eScience中心）
It is essential for the advancement of science that scientists and researchers share, reuse and reproduce workflows and protocols used by others. The FAIR principles are a set of guidelines that aim to maximize the value and usefulness of research data, and emphasize a number of important points regarding the means by which digital objects are found and reused by others. The question of how to apply these principles not just to the static input and output data but also to the dynamic workflows and protocols that consume and produce them is still under debate and poses a number of challenges. I…
Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledge
Fraunhofer Institute for Applied Information Technology FIT（弗劳恩霍夫应用信息技术研究所）；University Hospital Cologne UKK（科隆大学医院UKK）；University of Cologne, Faculty of Medicine and University Hospital Cologne（科隆大学医学院及科隆大学医院）；School of Future Science and Engineering, Soochow University（苏州大学未来科学与工程学院）；RWTH Aachen University（亚琛工业大学）
Knowledge Graph Re-engineering Along the Ontological Continuum (extended version)
The Open University, Walton Hall, Milton Keynes, United Kingdom（开放大学）；School of Computer Science and Informatics, University of Liverpool, UK（利兹大学计算机科学与信息学学院）
Fraunhofer Institute for Algorithms and Scientific Computing（弗劳恩霍夫算法与科学计算研究所）；University of Cologne（科隆大学）
Here we present a holistic approach for data exploration on dense knowledge graphs as a novel approach with a proof-of-concept in biomedical research. Knowledge graphs are increasingly becoming a vital factor in knowledge mining and discovery as they connect data using technologies from the semantic web. In this paper we extend a basic knowledge graph extracted from biomedical literature by context data like named entities and relations obtained by text mining and other linked data sources like ontologies and databases. We will present an overview about this novel network. The aim of this work…
ISTC-CNR（意大利国家研究委员会认知科学与技术研究所）；FIZ Karlsruhe（卡尔斯鲁厄专业信息中心）；University of Bari（巴里大学）；Cefriel（切夫里尔研究中心）；Open University（开放大学）；Maastricht University（马斯特里赫特大学）；KNAW Humanities Cluster（荷兰皇家艺术与科学院人文集群）；University of Mannheim（曼海姆大学）；Vienna University of Economics and Business（维也纳经济大学）；TU Dresden（德累斯顿工业大学）；INRIA（法国国家信息与自动化研究所）；The University of Southampton（南安普顿大学）；University of Genoa（热那亚大学）；University of Torino（都灵大学）；CELI - Language Technology（CELI语言技术公司）；Free University of Bozen-Bolzano（博尔扎诺自由大学）；GESIS - Leibniz Institute for the Social Sciences（GESIS莱布尼茨社会科学研究所）；University of California, Santa Barbara（加州大学圣巴巴拉分校）；Linköping University（林雪平大学）；Eurecom（欧洲通信研究院）；University of Bologna（博洛尼亚大学）；Ghent University（根特大学）；imec（比利时微电子研究中心）；Institute of Data Science, Maastricht University（马斯特里赫特大学数据科学研究所）；Universidad Politécnica de Madrid（马德里理工大学）；University of Sheffield（谢菲尔德大学）；University of Chile（智利大学）；University of Bergen（卑尔根大学）；University of Huddersfield（哈德斯菲尔德大学）
One of the grand challenges discussed during the Dagstuhl Seminar "Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web" and described in its report is that of a: "Public FAIR Knowledge Graph of Everything: We increasingly see the creation of knowledge graphs that capture information about the entirety of a class of entities. [...] This grand challenge extends this further by asking if we can create a knowledge graph of "everything" ranging from common sense concepts to location based entities. This knowledge graph should be "open to the public" in a FAIR manner de…
Since the Simple Knowledge Organization System (SKOS) specification and its SKOS eXtension for Labels (SKOS-XL) became formal W3C recommendations in 2009 a significant number of conventional knowledge organization systems (KOS) (including thesauri, classification schemes, name authorities, and lists of codes and terms, produced before the arrival of the ontology-wave) have made their journeys to join the Semantic Web mainstream. This paper uses "LOD KOS" as an umbrella term to refer to all of the value vocabularies and lightweight ontologies within the Semantic Web framework. The paper provide…
