Motivation: Ontologies are widely used in biology for data annotation, integration, and analysis. In addition to formally structured axioms, ontologies contain meta-data in the form of annotation axioms which provide valuable pieces of information that characterize ontology classes. Annotations commonly used in ontologies include class labels, descriptions, or synonyms. Despite being a rich source of semantic information, the ontology meta-data are generally unexploited by ontology-based analysis methods such as semantic similarity measures. Results: We propose a novel method, OPA2Vec, to generate vector representations of biological entities in ontologies by combining formal ontology axioms and annotation axioms from the ontology meta-data. We apply a Word2Vec model that has been pre-trained on PubMed abstracts to produce feature vectors from our collected data. We validate our method in two different ways: first, we use the obtained vector representations of proteins as a similarity measure to predict protein-protein interaction (PPI) on two different datasets. Second, we evaluate our method on predicting gene-disease associations based on phenotype similarity by generating vector representations of genes and diseases using a phenotype ontology, and applying the obtained vectors to predict gene-disease associations. These two experiments are just an illustration of the possible applications of our method. OPA2Vec can be used to produce vector representations of any biomedical entity given any type of biomedical ontology. Availability: https://github.com/bio-ontology-research-group/opa2vec Contact: robert.hoehndorf@kaust.edu.sa and xin.gao@kaust.edu.sa.
In this work, we develop an intelligent user interface that allows users to enter biomedical queries in a natural language, and that presents the answers (possibly with explanations if requested) in a natural language. We develop a rule layer over biomedical ontologies and databases, and use automated reasoners to answer queries considering relevant parts of the rule layer.
Despite the large-scale uptake of semantic technologies in the biomedical domain, little is known about common modelling practices in published ontologies. OWL ontologies are often published only in the crude form of sets of axioms leaving the underlying design opaque. However, a principled and systematic ontology development life cycle is likely to be reflected in regularities of the ontology's emergent syntactic structure. To develop an understanding of this emergent structure, we propose to reverse-engineer ontologies taking a syntax-directed approach for identifying and analysing regularit…
Athens University of Economics and Business（雅典经济与商业大学）；Institute for Language and Speech Processing, Research Center ‘Athena’（“雅典娜”研究中心语言与语音处理研究所）；Google Research（谷歌研究院）；CNRS（法国国家科学研究中心）；Univ. Paris Diderot（巴黎第七大学）
Network Embedding (NE) methods, which map network nodes to low-dimensional feature vectors, have wide applications in network analysis and bioinformatics. Many existing NE methods rely only on network structure, overlooking other information associated with the nodes, e.g., text describing the nodes. Recent attempts to combine the two sources of information only consider local network structure. We extend NODE2VEC, a well-known NE method that considers broader network structure, to also consider textual node descriptors using recurrent neural encoders. Our method is evaluated on link predictio…
University of A Coruña（拉科鲁尼亚大学）；University of Montpellier（蒙彼利埃大学）
Biomedical researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. We developed a new …
Institute of Mathematics and Computer Science, University of Latvia（拉脱维亚大学数学与计算机科学研究所）
Controlled natural languages (mostly English-based) recently have emerged as seemingly informal supplementary means for OWL ontology authoring, if compared to the formal notations that are used by professional knowledge engineers. In this paper we present by examples controlled Latvian language that has been designed to be compliant with the state of the art Attempto Controlled English. We also discuss relation with controlled Lithuanian language that is being designed in parallel.
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…
Biomedical ontology refers to a shared conceptualization for a biomedical domain of interest that has vastly improved data management and data sharing through the open data movement. The rapid growth and availability of biomedical data make it impractical and computationally expensive to perform manual analysis and query processing with the large scale ontologies. The lack of ability in analyzing ontologies from such a variety of sources, and supporting knowledge discovery for clinical practice and biomedical research should be overcome with new technologies. In this study, we developed a Medi…
Institute of Mathematics, Novosibirsk State University（新西伯利亚国立大学数学研究所）；Institute of Informatics Systems, Novosibirsk State University（新西伯利亚国立大学信息系统研究所）
We introduce an extension to the Protege ontology editor, which allows for discovering concept definitions, which are not explicitly present in axioms, but are logically implied by an ontology. The plugin supports ontologies formulated in the Description Logic EL, which underpins the OWL 2 EL profile of the Web Ontology Language and despite its limited expressiveness captures most of the biomedical ontologies published on the Web. The developed tool allows to verify whether a concept can be defined using a vocabulary of interest specified by a user. In particular, it allows to decide whether s…
Yale University（耶鲁大学）；University of Edinburgh（爱丁堡大学）
Much of biomedical and healthcare data is encoded in discrete, symbolic form such as text and medical codes. There is a wealth of expert-curated biomedical domain knowledge stored in knowledge bases and ontologies, but the lack of reliable methods for learning knowledge representation has limited their usefulness in machine learning applications. While text-based representation learning has significantly improved in recent years through advances in natural language processing, attempts to learn biomedical concept embeddings so far have been lacking. A recent family of models called knowledge g…
In this article, we investigate our early attempts at building an ontology describing rehabilitation therapies following brain injury. These therapies are wide-ranging, involving interventions of many different kinds. As a result, these therapies are hard to describe. As well as restricting actual practice, this is also a major impediment to evidence-based medicine as it is hard to meaningfully compare two treatment plans. Ontology development requires significant effort from both ontologists and domain experts. Knowledge elicited from domain experts forms the scope of the ontology. The proces…
Graz University of Technology（格拉茨工业大学）；GESIS - Leibniz Institute for the Social Sciences（GESIS——莱布尼茨社会科学研究所）；University of Koblenz-Landau（科布伦茨-兰道大学）；Stanford University（斯坦福大学）
Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases (ICD) as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the ICD, which is currently under active development by the WHO contains nearly 50,000 classes representing a vast variety of different diseases and causes of death. This…
Warsaw University of Technology（华沙理工大学）；Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）；University of Craiova（克拉约瓦大学）
Reusing ontologies in practice is still very challenging, especially when multiple ontologies are (jointly) involved. Moreover, despite recent advances, the realization of systematic ontology quality assurance remains a difficult problem. In this work, the quality of thirty biomedical ontologies, and the Computer Science Ontology are investigated, from the perspective of a practical use case. Special scrutiny is given to cross-ontology references, which are vital for combining ontologies. Diverse methods to detect potential issues are proposed, including natural language processing and network…
Background: Understanding the distinction between function and role is vexing and difficult. While it appears to be useful, in practice this distinction is hard to apply, particularly within biology. Results: I take an evolutionary approach, considering a series of examples, to develop and generate definitions for these concepts. I test them in practice against the Ontology for Biomedical Investigations (OBI). Finally, I give an axiomatisation and discuss methods for applying these definitions in practice. Conclusions: The definitions in this paper are applicable, formalizing current practice.…
GraphPrompt: Graph-Based Prompt Templates for Biomedical Synonym Prediction
University of Washington（华盛顿大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Carnegie Mellon University（卡内基梅隆大学）；Nanyang Technological University（南洋理工大学）；Peking University（北京大学）
Translating biomedical ontologies is an important challenge, but doing it manually requires much time and money. We study the possibility to use open-source knowledge bases to translate biomedical ontologies. We focus on two aspects: coverage and quality. We look at the coverage of two biomedical ontologies focusing on diseases with respect to Wikidata for 9 European languages (Czech, Dutch, English, French, German, Italian, Polish, Portuguese and Spanish) for both ontologies, plus Arabic, Chinese and Russian for the second one. We first use direct links between Wikidata and the studied ontolo…
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…
LaSIGE, Faculty of Sciences of the University of Lisbon（里斯本大学科学学院LaSIGE）
Ontology Matching aims to find a set of semantic correspondences, called an alignment, between related ontologies. In recent years, there has been a growing interest in efficient and effective matching methods for large ontologies. However, most of the alignments produced for large ontologies are logically incoherent. It was only recently that the use of repair techniques to improve the quality of ontology alignments has been explored. In this paper we present a novel technique for detecting incoherent concepts based on ontology modularization, and a new repair algorithm that minimizes the inc…
This report describes a new version of the OntoSpec methodology for ontology building. Defined by the LaRIA Knowledge Engineering Team (University of Picardie Jules Verne, Amiens, France), OntoSpec aims at helping builders to model ontological knowledge (upstream of formal representation). The methodology relies on a set of rigorously-defined modelling primitives and principles. Its application leads to the elaboration of a semi-informal ontology, which is independent of knowledge representation languages. We recently enriched the OntoSpec methodology by endowing it with a new resource, the DO…
University of Oxford（牛津大学）；Samsung Research（三星研究院）
Ontology alignment (a.k.a ontology matching (OM)) plays a critical role in knowledge integration. Owing to the success of machine learning in many domains, it has been applied in OM. However, the existing methods, which often adopt ad-hoc feature engineering or non-contextual word embeddings, have not yet outperformed rule-based systems especially in an unsupervised setting. In this paper, we propose a novel OM system named BERTMap which can support both unsupervised and semi-supervised settings. It first predicts mappings using a classifier based on fine-tuning the contextual embedding model …
Computational Bioscience Research Center, King Abdullah University of Science and Technology（阿卜杜拉国王科技大学计算生物科学研究中心）
We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies. Our method can be applied to a wide range of bioinformatics research problems such as similarity-based prediction of interactions between proteins, classification of interaction types using supervised learning, or clustering.
The Description Logic EL has recently drawn considerable attention since, on the one hand, important inference problems such as the subsumption problem are polynomial. On the other hand, EL is used to define large biomedical ontologies. Unification in Description Logics has been proposed as a novel inference service that can, for example, be used to detect redundancies in ontologies. The main result of this paper is that unification in EL is decidable. More precisely, EL-unification is NP-complete, and thus has the same complexity as EL-matching. We also show that, w.r.t. the unification type,…
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
Many ontologies, in particular in the biomedical domain, are based on the Description Logic EL++. Several efforts have been made to interpret and exploit EL++ ontologies by distributed representation learning. Specifically, concepts within EL++ theories have been represented as n-balls within an n-dimensional embedding space. However, the intersectional closure is not satisfied when using n-balls to represent concepts because the intersection of two n-balls is not an n-ball. This leads to challenges when measuring the distance between concepts and inferring equivalence between concepts. To thi…
Knowledge Graph Embeddings (KGE) have become a quite popular class of models specifically devised to deal with ontologies and graph structure data, as they can implicitly encode statistical dependencies between entities and relations in a latent space. KGE techniques are particularly effective for the biomedical domain, where it is quite common to deal with large knowledge graphs underlying complex interactions between biological and chemical objects. Recently in the literature, the PharmKG dataset has been proposed as one of the most challenging knowledge graph biomedical benchmark, with hund…
National Center for Ontological Research（国家本体研究中心）；University at Buffalo, State University of New York（纽约州立大学布法罗分校）；KadSci LLC（卡德科学有限责任公司）；CUBRC, Inc.（CUBRC公司）；University of Michigan-Ann Arbor（密歇根大学安娜堡分校）
Predicting Failures of LLMs to Link Biomedical Ontology Terms to Identifiers Evidence Across Models and Ontologies
Department of Neurology（神经学系）；Rehabilitation University of Illinois at Chicago Chicago, IL, USA（伊利诺伊大学芝加哥分校康复学院）；Lab for Applied Artificial Intelligence（应用人工智能实验室）；Engineering Program（工程学院）
Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；The Jackson Laboratory（杰克逊实验室）；University of Arizona（亚利桑那大学）；Simon Fraser University（西蒙弗雷泽大学）；The Jackson Laboratory for Genomic Medicine（杰克逊基因组医学实验室）；University at Buffalo（布法罗大学）；University of Florida（佛罗里达大学）；Northwestern University（西北大学）；SIB Swiss Institute of Bioinformatics（SIB瑞士生物信息学研究所）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；University of São Paulo（圣保罗大学）；University of Colorado Anschutz Medical Campus（科罗拉多大学安舒茨医学中心）；Sanger Institute（桑格研究所）；European Bioinformatics Institute (EMBL-EBI)（欧洲生物信息研究所（EMBL-EBI））；Phoenix Bioinformatics（菲尼克斯生物信息学公司）；Stowers Institute for Medical Research（斯托尔斯医学研究所）；IC-FOODS；Rothamsted Research（洛桑研究所）；National Center for Advancing Translational Sciences（国家推进转化科学中心）；INRAE, French National Research Institute for Agriculture, Food and Environment（法国国家农业、食品与环境研究院（INRAE））；University of Cambridge（剑桥大学）
The metadata about scientific experiments published in online repositories have been shown to suffer from a high degree of representational heterogeneity---there are often many ways to represent the same type of information, such as a geographical location via its latitude and longitude. To harness the potential that metadata have for discovering scientific data, it is crucial that they be represented in a uniform way that can be queried effectively. One step toward uniformly-represented metadata is to normalize the multiple, distinct field names used in metadata (e.g., lat lon, lat and long) …
Unifying biomedical knowledge in a modern multimodal graph
Department of Biomedical Informatics, Harvard Medical School（哈佛医学院生物医学信息学系）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Edison Scientific Inc.（Edison科学公司）；Oxford Suzhou Centre for Advanced Research, University of Oxford（牛津大学苏格兰研究中心）；Broad Institute of MIT and Harvard（MIT与哈佛大学Broad研究所）；Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University（哈佛大学自然与人工智能研究所）；Harvard Data Science Initiative（哈佛大学数据科学计划）
This paper describes our system, dubbed WS4A (Web Services for All), that participated in the fourth edition of the BioASQ challenge (2016). We used WS4A to perform the Question and Answering (QA) task 4b, which consisted on the retrieval of relevant concepts, documents, snippets, RDF triples, exact answers and ideal answers for each given question. The novelty in our approach consists on the maximum exploitation of existing web services in each step of WS4A, such as the annotation of text, and the retrieval of metadata for each annotation. The information retrieved included concept identifier…
Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；University of Washington（华盛顿大学）
Ontology alignment is the task of identifying semantically equivalent entities from two given ontologies. Different ontologies have different representations of the same entity, resulting in a need to de-duplicate entities when merging ontologies. We propose a method for enriching entities in an ontology with external definition and context information, and use this additional information for ontology alignment. We develop a neural architecture capable of encoding the additional information when available, and show that the addition of external data results in an F1-score of 0.69 on the Ontolo…
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…
RWTH Aachen University（亚琛工业大学）；Fraunhofer FIT（弗劳恩霍夫应用信息技术研究所）；ZBMED - Information Center for Life Sciences（ZB MED——生命科学信息中心）；University of Cologne（科隆大学）
Structured and unstructured data and facts about drugs, genes, protein, viruses, and their mechanism are spread across a huge number of scientific articles. These articles are a large-scale knowledge source and can have a huge impact on disseminating knowledge about the mechanisms of certain biological processes. A domain-specific knowledge graph~(KG) is an explicit conceptualization of a specific subject-matter domain represented w.r.t semantically interrelated entities and relations. A KG can be constructed by integrating such facts and data and be used for data integration, exploration, and…
Requirement volatility is an issue in software engineering in general, and in Web-based clinical applications in particular, which often originates from an incomplete knowledge of the domain of interest. With advances in the health science, many features and functionalities need to be added to, or removed from, existing software applications in the biomedical domain. At the same time, the increasing complexity of biomedical systems makes them more difficult to understand, and consequently it is more difficult to define their requirements, which contributes considerably to their volatility. In …
PrimeKG-CL: A Continual Graph Learning Benchmark on Evolving Biomedical Knowledge Graphs
Technology, Innovation, Entrepreneurship Department（技术、创新与创业系）；King Abdullah University of Science and Technology（国王阿卜杜勒阿齐兹大学）；School of Computing and Information Systems（计算与信息系）；The University of Melbourne（墨尔本大学）；College of Computer Science and Technology（计算机科学与技术学院）；School of Computing Technologies（计算技术学院）；Jilin University（吉林大学）；RMIT University（皇家墨尔本理工大学）；Department of Systems Engineering and Engineering Management（系统工程与工程管理系）；The Chinese University of Hong Kong（香港中文大学）；Department of Data Science and Artificial Intelligence（数据科学与人工智能系）；Hong Kong Polytechnic University（香港理工大学）
This work introduces BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts. State-of-the-art methodologies operate by maximizing the similarity in representation of names referring to the same concept, and preventing collapse through contrastive learning. However, because biomedical names are not always self-explanatory, it sometimes results in non-semantic representations. BioLORD overcomes this issue by grounding its concept representations using definitions, as well as short descriptions derived from a multi-relational k…
Boosting Adverse Drug Event Normalization on Social Media: General-Purpose Model Initialization and Biomedical Semantic Text Similarity Benefit Zero-Shot Linking in Informal Contexts
University of Ghent（根特大学）；University of Udine（乌迪内大学）
LingShu: A Large-Scale Symptom-Centric Contextualized Knowledge Graph Bridging Traditional Chinese Medicine and Modern Biomedicine
Beijing Jiaotong University（北京交通大学）；Hubei Provincial Hospital of Traditional Chinese Medicine（湖北省中医院）；Hubei University of Chinese Medicine（湖北中医药大学）；Hubei Province Academy of Traditional Chinese Medicine（湖北省中医药研究院）；China Academy of Chinese Medical Sciences（中国中医科学院）；Xiyuan Hospital（西苑医院）；National Clinical Research Center for Chinese Medicine Cardiology（国家中医心血管病临床医学研究中心）；Hubei Provincial Clinical Research Center for Acupuncture and Moxibustion in Obesity Treatment（湖北省肥胖病针灸临床研究中心）；Hubei Shizhen Laboratory（湖北时珍实验室）；Tianjin Ta（天津（此处原文未完整，按原文提取））
AstraZeneca（阿斯利康）；The University of Liverpool（利物浦大学）
Duplication of nodes is a common problem encountered when building knowledge graphs (KGs) from heterogeneous datasets, where it is crucial to be able to merge nodes having the same meaning. OntoMerger is a Python ontology integration library whose functionality is to deduplicate KG nodes. Our approach takes a set of KG nodes, mappings and disconnected hierarchies and generates a set of merged nodes together with a connected hierarchy. In addition, the library provides analytic and data testing functionalities that can be used to fine-tune the inputs, further reducing duplication, and to increa…
Chan Zuckerberg Initiative（陈·扎克伯格倡议）；University of Massachusetts（马萨诸塞大学）
Tools to explore scientific literature are essential for scientists, especially in biomedicine, where about a million new papers are published every year. Many such tools provide users the ability to search for specific entities (e.g. proteins, diseases) by tracking their mentions in papers. PubMed, the most well known database of biomedical papers, relies on human curators to add these annotations. This can take several weeks for new papers, and not all papers get tagged. Machine learning models have been developed to facilitate the semantic indexing of scientific papers. However their perfor…
The ever-increasing amount of data in biomedical research, and in cancer research in particular, needs to be managed to support efficient data access, exchange and integration. Existing software infrastructures, such caGrid, support access to distributed information annotated with a domain ontology. However, caGrid's current querying functionality depends on the structure of individual data resources without exploiting the semantic annotations. In this paper, we present the design and development of an ontology-based querying functionality that consists of: the generation of OWL2 ontologies fr…
Benchmarking Resource-Efficient LLMs for Research Topic Ontology Generation in the Biomedical Field
Knowledge Media Institute, The Open University, Milton Keynes, UK（开放大学知识媒体研究所）；Department of Business and Law, University of Milano-Bicocca, Milan, IT（米兰-比科卡大学商业与法律系）
University of Cambridge（剑桥大学）；Monash University（莫纳什大学）；Amazon Alexa（亚马逊Alexa）
Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remains a challenge. This is of paramount importance for entity-level tasks such as entity linking where the ability to model entity relations (especially synonymy) is pivotal. To address this challenge, we propose SapBERT, a pretraining scheme that self-aligns the representation space of biomedical entities. We design a scalable metric learning framework that can leverage UMLS, a massive collection of biomedical ontologi…
In this paper, we discuss the utility and deficiencies of existing ontology resources for a number of language processing applications. We describe a technique for increasing the semantic type coverage of a specific ontology, the National Library of Medicine's UMLS, with the use of robust finite state methods used in conjunction with large-scale corpus analytics of the domain corpus. We call this technique "semantic rerendering" of the ontology. This research has been done in the context of Medstract, a joint Brandeis-Tufts effort aimed at developing tools for analyzing biomedical language (i.…
Max Planck Institute for Dynamics and Self-Organization（马克斯·普朗克动力学与自组织研究所）；Georg-August-Universität（格奥尔格·奥古斯特大学）；German Center for Cardiovascular Research (DZHK)（德国心血管研究中心（DZHK））；University Medical Center Göttingen（哥廷根大学医学中心）；Northeastern University（东北大学）
Here we present CaosDB, a Research Data Management System (RDMS) designed to ensure seamless integration of inhomogeneous data sources and repositories of legacy data. Its primary purpose is the management of data from biomedical sciences, both from simulations and experiments during the complete research data lifecycle. An RDMS for this domain faces particular challenges: Research data arise in huge amounts, from a wide variety of sources, and traverse a highly branched path of further processing. To be accepted by its users, an RDMS must be built around workflows of the scientists and practi…
SSBD Ontology: A Two-Tier Approach for Interoperable Bioimaging Metadata
RIKEN Information R&D and Strategy Headquarters（日本理化学研究所信息研发与战略本部）；RIKEN Bioresource Research Center（日本理化学研究所生物资源研究中心）；RIKEN Center for Biosystems Dynamics Research（日本理化学研究所生物系统动力学研究中心）
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）
