Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors
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）
Sotiris Kotitsas, Dimitris Pappas, Ion Androutsopoulos, Ryan McDonald, Marianna Apidianaki
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Proceedings of the 18th Workshop on Biomedical Natural Language Processing (BioNLP 2019) of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), Florence, Italy, 2019
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 prediction in two networks derived from UMLS. Experimental results demonstrate the effectiveness of the proposed approach compared to previous work.
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…
King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
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 gene…
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 …
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…
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…
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…
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…
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…
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…
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）
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.…
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…
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 …
Faculty of Electrical Engineering and Computing, University of Zagreb（萨格勒布大学电气工程与计算学院）
Ever growing number of image documents available on the Internet continuously motivates research in better annotation models and more efficient retrieval methods. Formal knowledge representation of objects and events in pictures, their interaction as well as context complexity becomes no longer an option for a quality image repository, but a necessity. We present an ontology-based online image annotation tool WNtags and demonstrate its usefulness in several typical multimedia retrieval tasks using International Affective Picture System emotionally annotated image database. WNtags is built arou…
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…
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）
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…
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）
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）
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…
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））
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）
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…
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）
