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van Damme P, Fernández-Breis JT, Benis N, Miñarro-Gimenez JA, de Keizer NF, Cornet R. Performance assessment of ontology matching systems for FAIR data. J Biomed Semantics 2022; 13:19. [PMID: 35841031 PMCID: PMC9284868 DOI: 10.1186/s13326-022-00273-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 06/15/2022] [Indexed: 11/24/2022] Open
Abstract
Background Ontology matching should contribute to the interoperability aspect of FAIR data (Findable, Accessible, Interoperable, and Reusable). Multiple data sources can use different ontologies for annotating their data and, thus, creating the need for dynamic ontology matching services. In this experimental study, we assessed the performance of ontology matching systems in the context of a real-life application from the rare disease domain. Additionally, we present a method for analyzing top-level classes to improve precision. Results We included three ontologies (NCIt, SNOMED CT, ORDO) and three matching systems (AgreementMakerLight 2.0, FCA-Map, LogMap 2.0). We evaluated the performance of the matching systems against reference alignments from BioPortal and the Unified Medical Language System Metathesaurus (UMLS). Then, we analyzed the top-level ancestors of matched classes, to detect incorrect mappings without consulting a reference alignment. To detect such incorrect mappings, we manually matched semantically equivalent top-level classes of ontology pairs. AgreementMakerLight 2.0, FCA-Map, and LogMap 2.0 had F1-scores of 0.55, 0.46, 0.55 for BioPortal and 0.66, 0.53, 0.58 for the UMLS respectively. Using vote-based consensus alignments increased performance across the board. Evaluation with manually created top-level hierarchy mappings revealed that on average 90% of the mappings’ classes belonged to top-level classes that matched. Conclusions Our findings show that the included ontology matching systems automatically produced mappings that were modestly accurate according to our evaluation. The hierarchical analysis of mappings seems promising when no reference alignments are available. All in all, the systems show potential to be implemented as part of an ontology matching service for querying FAIR data. Future research should focus on developing methods for the evaluation of mappings used in such mapping services, leading to their implementation in a FAIR data ecosystem. Supplementary Information The online version contains supplementary material available at (10.1186/s13326-022-00273-5).
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Affiliation(s)
- Philip van Damme
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Meibergdreef 9, Amsterdam, The Netherlands. .,Amsterdam Public Health, Digital Health & Methodology, Amsterdam, The Netherlands.
| | | | - Nirupama Benis
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Meibergdreef 9, Amsterdam, The Netherlands.,Amsterdam Public Health, Digital Health & Methodology, Amsterdam, The Netherlands
| | | | - Nicolette F de Keizer
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Meibergdreef 9, Amsterdam, The Netherlands.,Amsterdam Public Health, Methodology & Quality of Care, Amsterdam, The Netherlands
| | - Ronald Cornet
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Meibergdreef 9, Amsterdam, The Netherlands.,Amsterdam Public Health, Digital Health & Methodology, Amsterdam, The Netherlands
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Alignment Conservativity under the Ontology Change. JOURNAL OF INFORMATION TECHNOLOGY RESEARCH 2022. [DOI: 10.4018/jitr.299923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Recently, many methods have appeared to solve the problem of the evolution of alignment under the change of ontologies. The main challenge for them is to maintain consistency of alignment after applying the change. An alignment is consistent if and only if the ontologies remain consistent even when used in conjunction with the alignment. The objective of this work is to take a step forward by considering the alignment evolution according to the conservativity principle under the change of ontologies. In this context, an alignment is conservative if the ontological change should not introduce new semantic relationships between concepts from one of the input ontologies. We give methods for the conservativity violation detection and repair under the change of ontologies and we carry out an experiment on a dataset adapted from the Ontology Alignment Evaluation Initiative. The experiment demonstrates both the practical applicability of the proposed approach and shows the limits of the alignment evolution methods compared to the alignment conservativity under the change of ontologies.
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Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Network. PROCEEDINGS OF THE CONFERENCE. ASSOCIATION FOR COMPUTATIONAL LINGUISTICS. MEETING 2021; 2021:1016-1029. [PMID: 35821978 DOI: 10.18653/v1/2021.acl-long.82] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Knowledge Graph (KG) completion research usually focuses on densely connected benchmark datasets that are not representative of real KGs. We curate two KG datasets that include biomedical and encyclopedic knowledge and use an existing commonsense KG dataset to explore KG completion in the more realistic setting where dense connectivity is not guaranteed. We develop a deep convolutional network that utilizes textual entity representations and demonstrate that our model outperforms recent KG completion methods in this challenging setting. We find that our model's performance improvements stem primarily from its robustness to sparsity. We then distill the knowledge from the convolutional network into a student network that re-ranks promising candidate entities. This re-ranking stage leads to further improvements in performance and demonstrates the effectiveness of entity re-ranking for KG completion.
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Nguyen V, Yip HY, Bodenreider O. Biomedical Vocabulary Alignment at Scale in the UMLS Metathesaurus. PROCEEDINGS OF THE ... INTERNATIONAL WORLD-WIDE WEB CONFERENCE. INTERNATIONAL WWW CONFERENCE 2021; 2021:2672-2683. [PMID: 34514472 PMCID: PMC8434895 DOI: 10.1145/3442381.3450128] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
With 214 source vocabularies, the construction and maintenance process of the UMLS (Unified Medical Language System) Metathesaurus terminology integration system is costly, time-consuming, and error-prone as it primarily relies on (1) lexical and semantic processing for suggesting groupings of synonymous terms, and (2) the expertise of UMLS editors for curating these synonymy predictions. This paper aims to improve the UMLS Metathesaurus construction process by developing a novel supervised learning approach for improving the task of suggesting synonymous pairs that can scale to the size and diversity of the UMLS source vocabularies. We evaluate this deep learning (DL) approach against a rule-based approach (RBA) that approximates the current UMLS Metathesaurus construction process. The key to the generalizability of our approach is the use of various degrees of lexical similarity in negative pairs during the training process. Our initial experiments demonstrate the strong performance across multiple datasets of our DL approach in terms of recall (91-92%), precision (88-99%), and F1 score (89-95%). Our DL approach largely outperforms the RBA method in recall (+23%), precision (+2.4%), and F1 score (+14.1%). This novel approach has great potential for improving the UMLS Metathesaurus construction process by providing better synonymy suggestions to the UMLS editors.
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Affiliation(s)
- Vinh Nguyen
- National Library of Medicine, Bethesda, Maryland, USA
| | - Hong Yung Yip
- University of South Carolina, Columbia, South Carolina, USA
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Li W, Zhang S. Repairing mappings across biomedical ontologies by probabilistic reasoning and belief revision. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.106436] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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Zheng L, He Z, Wei D, Keloth V, Fan JW, Lindemann L, Zhu X, Cimino JJ, Perl Y. A review of auditing techniques for the Unified Medical Language System. J Am Med Inform Assoc 2020; 27:1625-1638. [PMID: 32766692 PMCID: PMC7566540 DOI: 10.1093/jamia/ocaa108] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 05/05/2020] [Accepted: 05/13/2020] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVE The study sought to describe the literature related to the development of methods for auditing the Unified Medical Language System (UMLS), with particular attention to identifying errors and inconsistencies of attributes of the concepts in the UMLS Metathesaurus. MATERIALS AND METHODS We applied the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach by searching the MEDLINE database and Google Scholar for studies referencing the UMLS and any of several terms related to auditing, error detection, and quality assurance. A qualitative analysis and summarization of articles that met inclusion criteria were performed. RESULTS Eighty-three studies were reviewed in detail. We first categorized techniques based on various aspects including concepts, concept names, and synonymy (n = 37), semantic type assignments (n = 36), hierarchical relationships (n = 24), lateral relationships (n = 12), ontology enrichment (n = 8), and ontology alignment (n = 18). We also categorized the methods according to their level of automation (ie, automated systematic, automated heuristic, or manual) and the type of knowledge used (ie, intrinsic or extrinsic knowledge). CONCLUSIONS This study is a comprehensive review of the published methods for auditing the various conceptual aspects of the UMLS. Categorizing the auditing techniques according to the various aspects will enable the curators of the UMLS as well as researchers comprehensive easy access to this wealth of knowledge (eg, for auditing lateral relationships in the UMLS). We also reviewed ontology enrichment and alignment techniques due to their critical use of and impact on the UMLS.
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Affiliation(s)
- Ling Zheng
- Department of Computer Science and Software Engineering, Monmouth University, West Long Branch, New Jersey, USA
| | - Zhe He
- School of Information, Florida State University, Tallahassee, Florida, USA
| | - Duo Wei
- School of Business, Stockton University, Galloway, New Jersey, USA
| | - Vipina Keloth
- Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Jung-Wei Fan
- Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Luke Lindemann
- Center for Biomedical Data Science, Yale School of Medicine, New Haven, Connecticut, USA
| | - Xinxin Zhu
- Center for Biomedical Data Science, Yale School of Medicine, New Haven, Connecticut, USA
| | - James J Cimino
- Informatics Institute, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Yehoshua Perl
- Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, USA
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Crowd-assessing quality in uncertain data linking datasets. KNOWL ENG REV 2020. [DOI: 10.1017/s0269888920000363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Abstract
The quality of a dataset used for evaluating data linking methods, techniques, and tools depends on the availability of a set of mappings, called reference alignment, that is known to be correct. In particular, it is crucial that mappings effectively represent relations between pairs of entities that are indeed similar due to the fact that they denote the same object. Since the reliability of mappings is decisive in order to perform a fair evaluation of automatic linking methods and tools, we call this property of mappings as mapping fairness. In this article, we propose a crowd-based approach, called Crowd Quality (CQ), for assessing the quality of data linking datasets by measuring the fairness of the mappings in the reference alignment. Moreover, we present a real experiment, where we evaluate two state-of-the-art data linking tools before and after the refinement of the reference alignment based on the CQ approach, in order to present the benefits deriving from the crowd assessment of mapping fairness.
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Abstract
Abstract
The development of semi-automated and automated ontology alignment techniques is an important part of realizing the potential of the Semantic Web. Until very recently, most existing work in this area was focused on finding simple (1:1) equivalence correspondences between two ontologies. However, many real-world ontology pairs involve correspondences that contain multiple entities from each ontology. These ‘complex’ alignments pose a challenge for existing evaluation approaches, which hinders the development of new systems capable of finding such correspondences. This position paper surveys and analyzes the requirements for effective evaluation of complex ontology alignments and assesses the degree to which these requirements are met by existing approaches. It also provides a roadmap for future work on this topic taking into consideration emerging community initiatives and major challenges that need to be addressed.
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Phillips MH, Serra LM, Dekker A, Ghosh P, Luk SMH, Kalet A, Mayo C. Ontologies in radiation oncology. Phys Med 2020; 72:103-113. [PMID: 32247963 DOI: 10.1016/j.ejmp.2020.03.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 03/17/2020] [Accepted: 03/19/2020] [Indexed: 01/27/2023] Open
Abstract
Ontologies are a formal, computer-compatible method for representing scientific knowledge about a given domain. They provide a standardized vocabulary, taxonomy and set of relations between concepts. When formatted in a standard way, they can be read and reasoned upon by computers as well as by humans. At the 2019 International Conference on the Use of Computers in Radiation Therapy, there was a session devoted to ontologies in radiation therapy. This paper is a compilation of the material presented, and is meant as an introduction to the subject. This is done by means of a didactic introduction to the topic followed by a series of applications in radiation therapy. The goal of this article is to provide the medical physicist and related professionals with sufficient background that they can understand their construction as well as their practical uses.
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Affiliation(s)
- Mark H Phillips
- Department of Radiation Oncology, University of Washington, Seattle, WA 91895, United States.
| | - Lucas M Serra
- Department of Biomedical Informatics, University at Buffalo, 77 Goodell Street, Buffalo, NY 14260, United States
| | - Andre Dekker
- Medical Physics Department, Maastro Clinic, DR. Tanslaan 12, Maastrich 6229 ET, Netherlands
| | - Preetam Ghosh
- Department of Computer Science, Engineering East Hall, Virginia Commonwealth University, Richmond, VA, United States
| | - Samuel M H Luk
- Department of Radiation Oncology, University of Washington, Seattle, WA 91895, United States
| | - Alan Kalet
- Department of Radiation Oncology, University of Washington, Seattle, WA 91895, United States
| | - Charles Mayo
- Radiation Oncology, University of Michigan, 1500 E Medical Center Dr, SPC 5010, Ann Arbor, MI, United States
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Abstract
Software engineering employs different benchmarks for a software evaluation. This enables software developers to continuously improve their product. The same needs are intrinsic for software tools in the semantic web field. While there are many different benchmarks already available, there has not been their overview and categorization yet. This work provides such an overview and categorization of benchmarks specifically oriented on benchmarks where an ontology plays an important role. Benchmarks are naturally categorized in line with ontology tool categorization along with an indication which activities those benchmarks are deliberate and which are non-deliberative. Although the article itself can already navigate a reader to an adequate benchmark, we moreover automatically designed a flexible rule-based recommendation tool based on the analysis of existing benchmarks.
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Abstract
Abstract
User validation is one of the challenges facing the ontology alignment community, as there are limits to the quality of the alignments produced by automated alignment algorithms. In this paper, we present a broad study on user validation of ontology alignments that encompasses three distinct but inter-related aspects: the profile of the user, the services of the alignment system, and its user interface. We discuss key issues pertaining to the alignment validation process under each of these aspects and provide an overview of how current systems address them. Finally, we use experiments from the Interactive Matching track of the Ontology Alignment Evaluation Initiative 2015–2018 to assess the impact of errors in alignment validation, and how systems cope with them as function of their services.
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Kolyvakis P, Kalousis A, Smith B, Kiritsis D. Biomedical ontology alignment: an approach based on representation learning. J Biomed Semantics 2018; 9:21. [PMID: 30111369 PMCID: PMC6094585 DOI: 10.1186/s13326-018-0187-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 07/16/2018] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance. RESULTS An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results. CONCLUSIONS Our proposed representation learning approach leverages terminological embeddings to capture semantic similarity. Our results provide evidence that the approach produces embeddings that are especially well tailored to the ontology matching task, demonstrating a novel pathway for the problem.
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Affiliation(s)
- Prodromos Kolyvakis
- École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, Lausanne, 1015 Switzerland
| | - Alexandros Kalousis
- Business Informatics Department, University of Applied Sciences, HES-SO, Western Switzerland Carouge, Switzerland
| | - Barry Smith
- Department of Philosophy and Department of Biomedical Informatics, 104 Park Hall, University at Buffalo, Buffalo, 14260 NY USA
| | - Dimitris Kiritsis
- École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, Lausanne, 1015 Switzerland
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Oliveira D, Pesquita C. Improving the interoperability of biomedical ontologies with compound alignments. J Biomed Semantics 2018; 9:1. [PMID: 29316968 PMCID: PMC5761129 DOI: 10.1186/s13326-017-0171-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Accepted: 12/21/2017] [Indexed: 12/29/2022] Open
Abstract
Background Ontologies are commonly used to annotate and help process life sciences data. Although their original goal is to facilitate integration and interoperability among heterogeneous data sources, when these sources are annotated with distinct ontologies, bridging this gap can be challenging. In the last decade, ontology matching systems have been evolving and are now capable of producing high-quality mappings for life sciences ontologies, usually limited to the equivalence between two ontologies. However, life sciences research is becoming increasingly transdisciplinary and integrative, fostering the need to develop matching strategies that are able to handle multiple ontologies and more complex relations between their concepts. Results We have developed ontology matching algorithms that are able to find compound mappings between multiple biomedical ontologies, in the form of ternary mappings, finding for instance that “aortic valve stenosis”(HP:0001650) is equivalent to the intersection between “aortic valve”(FMA:7236) and “constricted” (PATO:0001847). The algorithms take advantage of search space filtering based on partial mappings between ontology pairs, to be able to handle the increased computational demands. The evaluation of the algorithms has shown that they are able to produce meaningful results, with precision in the range of 60-92% for new mappings. The algorithms were also applied to the potential extension of logical definitions of the OBO and the matching of several plant-related ontologies. Conclusions This work is a first step towards finding more complex relations between multiple ontologies. The evaluation shows that the results produced are significant and that the algorithms could satisfy specific integration needs.
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Affiliation(s)
- Daniela Oliveira
- Insight Centre for Data Analytics, NUI Galway, Galway Business Park, Dangan, Galway, H91 AEX4, Ireland. .,LaSIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, 1749-016, Portugal.
| | - Catia Pesquita
- LaSIGE, Faculdade de Ciências, Universidade de Lisboa, Lisboa, 1749-016, Portugal
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Harrow I, Jiménez-Ruiz E, Splendiani A, Romacker M, Woollard P, Markel S, Alam-Faruque Y, Koch M, Malone J, Waaler A. Matching disease and phenotype ontologies in the ontology alignment evaluation initiative. J Biomed Semantics 2017; 8:55. [PMID: 29197409 PMCID: PMC5712086 DOI: 10.1186/s13326-017-0162-9] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2017] [Accepted: 10/27/2017] [Indexed: 11/30/2022] Open
Abstract
Background The disease and phenotype track was designed to evaluate the relative performance of ontology matching systems that generate mappings between source ontologies. Disease and phenotype ontologies are important for applications such as data mining, data integration and knowledge management to support translational science in drug discovery and understanding the genetics of disease. Results Eleven systems (out of 21 OAEI participating systems) were able to cope with at least one of the tasks in the Disease and Phenotype track. AML, FCA-Map, LogMap(Bio) and PhenoMF systems produced the top results for ontology matching in comparison to consensus alignments. The results against manually curated mappings proved to be more difficult most likely because these mapping sets comprised mostly subsumption relationships rather than equivalence. Manual assessment of unique equivalence mappings showed that AML, LogMap(Bio) and PhenoMF systems have the highest precision results. Conclusions Four systems gave the highest performance for matching disease and phenotype ontologies. These systems coped well with the detection of equivalence matches, but struggled to detect semantic similarity. This deserves more attention in the future development of ontology matching systems. The findings of this evaluation show that such systems could help to automate equivalence matching in the workflow of curators, who maintain ontology mapping services in numerous domains such as disease and phenotype.
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Affiliation(s)
- Ian Harrow
- Pistoia Alliance Ontologies Mapping Project, Pistoia Alliance Inc, USA.
| | | | | | - Martin Romacker
- Roche Pharma Research and Early Development, pRED Informatics, Roche Innovation Center, Basel, Switzerland
| | | | | | | | | | | | - Arild Waaler
- Department of Informatics, University of Oslo, Oslo, Norway
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Xue X, Liu J. Collaborative ontology matching based on compact interactive evolutionary algorithm. Knowl Based Syst 2017. [DOI: 10.1016/j.knosys.2017.09.017] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Lamy JB, Soualmia LF, Kerdelhué G, Venot A, Duclos C. Validating the semantics of a medical iconic language using ontological reasoning. J Biomed Inform 2013; 46:56-67. [PMID: 22975315 DOI: 10.1016/j.jbi.2012.08.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2012] [Revised: 07/19/2012] [Accepted: 08/24/2012] [Indexed: 11/15/2022]
Affiliation(s)
- Jean-Baptiste Lamy
- LIM&BIO (EA3969), UFR SMBH, University Paris 13, Sorbonne Paris Cité, Bobigny, France.
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Splendiani A, Burger A, Paschke A, Romano P, Marshall MS. Biomedical semantics in the Semantic Web. J Biomed Semantics 2011; 2 Suppl 1:S1. [PMID: 21388570 PMCID: PMC3105493 DOI: 10.1186/2041-1480-2-s1-s1] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
The Semantic Web offers an ideal platform for representing and linking biomedical information, which is a prerequisite for the development and application of analytical tools to address problems in data-intensive areas such as systems biology and translational medicine. As for any new paradigm, the adoption of the Semantic Web offers opportunities and poses questions and challenges to the life sciences scientific community: which technologies in the Semantic Web stack will be more beneficial for the life sciences? Is biomedical information too complex to benefit from simple interlinked representations? What are the implications of adopting a new paradigm for knowledge representation? What are the incentives for the adoption of the Semantic Web, and who are the facilitators? Is there going to be a Semantic Web revolution in the life sciences? We report here a few reflections on these questions, following discussions at the SWAT4LS (Semantic Web Applications and Tools for Life Sciences) workshop series, of which this Journal of Biomedical Semantics special issue presents selected papers from the 2009 edition, held in Amsterdam on November 20th.
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