Journal of Biomedical Semantics最新文献

{"title":"Features of a FAIR vocabulary.","authors":"Fuqi Xu,&nbsp;Nick Juty,&nbsp;Carole Goble,&nbsp;Simon Jupp,&nbsp;Helen Parkinson,&nbsp;Mélanie Courtot","doi":"10.1186/s13326-023-00286-8","DOIUrl":"https://doi.org/10.1186/s13326-023-00286-8","url":null,"abstract":"<p><strong>Background: </strong>The Findable, Accessible, Interoperable and Reusable(FAIR) Principles explicitly require the use of FAIR vocabularies, but what precisely constitutes a FAIR vocabulary remains unclear. Being able to define FAIR vocabularies, identify features of FAIR vocabularies, and provide assessment approaches against the features can guide the development of vocabularies.</p><p><strong>Results: </strong>We differentiate data, data resources and vocabularies used for FAIR, examine the application of the FAIR Principles to vocabularies, align their requirements with the Open Biomedical Ontologies principles, and propose FAIR Vocabulary Features. We also design assessment approaches for FAIR vocabularies by mapping the FVFs with existing FAIR assessment indicators. Finally, we demonstrate how they can be used for evaluating and improving vocabularies using exemplary biomedical vocabularies.</p><p><strong>Conclusions: </strong>Our work proposes features of FAIR vocabularies and corresponding indicators for assessing the FAIR levels of different types of vocabularies, identifies use cases for vocabulary engineers, and guides the evolution of vocabularies.</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"14 1","pages":"6"},"PeriodicalIF":1.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10236849/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9672525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiple sampling schemes and deep learning improve active learning performance in drug-drug interaction information retrieval analysis from the literature.","authors":"Weixin Xie, Kunjie Fan, Shijun Zhang, Lang Li","doi":"10.1186/s13326-023-00287-7","DOIUrl":"10.1186/s13326-023-00287-7","url":null,"abstract":"<p><strong>Background: </strong>Drug-drug interaction (DDI) information retrieval (IR) is an important natural language process (NLP) task from the PubMed literature. For the first time, active learning (AL) is studied in DDI IR analysis. DDI IR analysis from PubMed abstracts faces the challenges of relatively small positive DDI samples among overwhelmingly large negative samples. Random negative sampling and positive sampling are purposely designed to improve the efficiency of AL analysis. The consistency of random negative sampling and positive sampling is shown in the paper.</p><p><strong>Results: </strong>PubMed abstracts are divided into two pools. Screened pool contains all abstracts that pass the DDI keywords query in PubMed, while unscreened pool includes all the other abstracts. At a prespecified recall rate of 0.95, DDI IR analysis precision is evaluated and compared. In screened pool IR analysis using supporting vector machine (SVM), similarity sampling plus uncertainty sampling improves the precision over uncertainty sampling, from 0.89 to 0.92 respectively. In the unscreened pool IR analysis, the integrated random negative sampling, positive sampling, and similarity sampling improve the precision over uncertainty sampling along, from 0.72 to 0.81 respectively. When we change the SVM to a deep learning method, all sampling schemes consistently improve DDI AL analysis in both screened pool and unscreened pool. Deep learning has significant improvement of precision over SVM, 0.96 vs. 0.92 in screened pool, and 0.90 vs. 0.81 in the unscreened pool, respectively.</p><p><strong>Conclusions: </strong>By integrating various sampling schemes and deep learning algorithms into AL, the DDI IR analysis from literature is significantly improved. The random negative sampling and positive sampling are highly effective methods in improving AL analysis where the positive and negative samples are extremely imbalanced.</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"14 1","pages":"5"},"PeriodicalIF":1.9,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10228061/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9740363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Constructing a knowledge graph for open government data: the case of Nova Scotia disease datasets.","authors":"Enayat Rajabi,&nbsp;Rishi Midha,&nbsp;Jairo Francisco de Souza","doi":"10.1186/s13326-023-00284-w","DOIUrl":"https://doi.org/10.1186/s13326-023-00284-w","url":null,"abstract":"<p><p>The majority of available datasets in open government data are statistical. They are widely published by various governments to be used by the public and data consumers. However, most open government data portals do not provide the five-star Linked Data standard datasets. The published datasets are isolated from one another while conceptually connected. This paper constructs a knowledge graph for the disease-related datasets of a Canadian government data portal, Nova Scotia Open Data. We leveraged the Semantic Web technologies to transform the disease-related datasets into Resource Description Framework (RDF) and enriched them with semantic rules. An RDF data model using the RDF Cube vocabulary was designed in this work to develop a graph that adheres to best practices and standards, allowing for expansion, modification and flexible re-use. The study also discusses the lessons learned during the cross-dimensional knowledge graph construction and integration of open statistical datasets from multiple sources.</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"14 1","pages":"4"},"PeriodicalIF":1.9,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10111831/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9478716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of Incremental and Superficial Processing in the Depth Charge Illusion: Experimental and Modeling Evidence","authors":"Dario Paape","doi":"10.1093/jos/ffad003","DOIUrl":"https://doi.org/10.1093/jos/ffad003","url":null,"abstract":"\u0000 The depth charge illusion occurs when compositionally incongruous sentences such as No detail is too unimportant to be left out are assigned plausible non-compositional meanings (Don’t leave out details). Results of two online reading and judgment experiments show that moving the incongruous degree phrase to the beginning of the sentence in German (lit. “Too unimportant to be left out is surely no detail”) results in an attenuation of this semantic illusion, implying a role for incremental processing. Two further experiments show that readers cannot consistently turn the communicated meaning of depth charge sentences into its opposite, and that acceptability varies greatly between sentences and subjects, which is consistent with superficial interpretation. A meta-analytic fit of the Wiener diffusion model to data from six experiments shows that world knowledge is a systematic driver of the illusion, leading to stable acceptability judgments. Other variables, such as sentiment polarity, influence subjects’ depth of processing. Overall, the results shed new light on the role of superficial processing on the one hand and of communicative competence on the other hand in creating the depth charge illusion. I conclude that the depth charge illusion combines aspects of being a persistent processing “bug” with aspects of being a beneficial communicative “feature”, making it a fascinating object of study.","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"40 1","pages":"93-125"},"PeriodicalIF":1.9,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77665550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plural and Quantified Protagonists in Free Indirect Discourse and Protagonist Projection","authors":"Márta Abrusán","doi":"10.1093/jos/ffad004","DOIUrl":"https://doi.org/10.1093/jos/ffad004","url":null,"abstract":"\u0000 In this paper I observe a number of new plural and (apparently) quantified examples of free indirect discourse (FID) and protagonist projection (PP). I analyse them within major current theoretical approaches, proposing extensions to these approaches where needed. In order to derive the wide range of readings observed with plural protagonists, I show how we can exploit existing mechanisms for the interpretation of plural anaphora and plural predication. The upshot is that the interpretation of plural examples of perspective shift relies on a remarkable concert of covert semantic and pragmatic operations.","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"18 1","pages":"127-151"},"PeriodicalIF":1.9,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77176856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Are There Pluralities of Worlds?","authors":"V. Schmitt","doi":"10.1093/jos/ffad002","DOIUrl":"https://doi.org/10.1093/jos/ffad002","url":null,"abstract":"\u0000 Indicative conditionals and configurations with neg-raising predicates have been brought up as potential candidates for constructions involving world pluralities. I argue against this hypothesis, showing that cumulativity and quantifiers targeting a plurality’s part structure cannot access the presumed world pluralities. I furthermore argue that this makes worlds special in the sense that the same tests provide evidence for pluralities in various other semantic domains.","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"7 1","pages":"153-178"},"PeriodicalIF":1.9,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81520300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Copredication and Meaning Transfer","authors":"David Liebesman, Ofra Magidor","doi":"10.1093/jos/ffad001","DOIUrl":"https://doi.org/10.1093/jos/ffad001","url":null,"abstract":"\u0000 Copredication occurs when a sentence receives a true reading despite prima facie ascribing categorically incompatible properties to a single entity. For example, ‘The red book is by Tolstoy’ can have a true reading even though it seems that being red is only a property of physical copies, while being by Tolstoy is only a property of informational texts.\u0000 A tempting strategy for resolving this tension is to claim that at least one of the predicates has a non-standard interpretation, with the salient proposal involving reinterpretation via meaning transfer. For example, in ‘The red book is by Tolstoy’, one could hold that the predicate ‘by Tolstoy’ is reinterpreted (or on the more specific proposal, transferred) to ascribe a property that physical copies can uncontroversially instantiate, such as expresses an informational text by Tolstoy. On this view, the truth of the copredicational sentence is no longer mysterious. Furthermore, such a reinterpretation view can give a straightforward account of a range of puzzling copredicational sentences involving counting an individuation.\u0000 Despite these substantial virtues, we will argue that reinterpretation approaches to copredication are untenable. In §1 we introduce reinterpretation views of copredication and contrast them with key alternatives. In §2 we argue against a general reinterpretation theory of copredication on which every copredicational sentence contains at least one reinterpreted predicate. We also raise additional problems for the more specific proposal of implementing reinterpretation via meaning transfer. In §3 we argue against more limited appeals to reinterpretation on which only some copredicational sentences contain reinterpretation. In §4 we criticize a series of arguments in favour of reinterpretation theories. The upshot is that reinterpretation theories of copredication, and in particular, meaning transfer-based accounts, should be rejected.","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"3 1","pages":"69-91"},"PeriodicalIF":1.9,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81558248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Environmental Conditions, Treatments, and Exposures Ontology (ECTO): connecting toxicology and exposure to human health and beyond.","authors":"Lauren E Chan, Anne E Thessen, William D Duncan, Nicolas Matentzoglu, Charles Schmitt, Cynthia J Grondin, Nicole Vasilevsky, Julie A McMurry, Peter N Robinson, Christopher J Mungall, Melissa A Haendel","doi":"10.1186/s13326-023-00283-x","DOIUrl":"10.1186/s13326-023-00283-x","url":null,"abstract":"<p><strong>Background: </strong>Evaluating the impact of environmental exposures on organism health is a key goal of modern biomedicine and is critically important in an age of greater pollution and chemicals in our environment. Environmental health utilizes many different research methods and generates a variety of data types. However, to date, no comprehensive database represents the full spectrum of environmental health data. Due to a lack of interoperability between databases, tools for integrating these resources are needed. In this manuscript we present the Environmental Conditions, Treatments, and Exposures Ontology (ECTO), a species-agnostic ontology focused on exposure events that occur as a result of natural and experimental processes, such as diet, work, or research activities. ECTO is intended for use in harmonizing environmental health data resources to support cross-study integration and inference for mechanism discovery.</p><p><strong>Methods and findings: </strong>ECTO is an ontology designed for describing organismal exposures such as toxicological research, environmental variables, dietary features, and patient-reported data from surveys. ECTO utilizes the base model established within the Exposure Ontology (ExO). ECTO is developed using a combination of manual curation and Dead Simple OWL Design Patterns (DOSDP), and contains over 2700 environmental exposure terms, and incorporates chemical and environmental ontologies. ECTO is an Open Biological and Biomedical Ontology (OBO) Foundry ontology that is designed for interoperability, reuse, and axiomatization with other ontologies. ECTO terms have been utilized in axioms within the Mondo Disease Ontology to represent diseases caused or influenced by environmental factors, as well as for survey encoding for the Personalized Environment and Genes Study (PEGS).</p><p><strong>Conclusions: </strong>We constructed ECTO to meet Open Biological and Biomedical Ontology (OBO) Foundry principles to increase translation opportunities between environmental health and other areas of biology. ECTO has a growing community of contributors consisting of toxicologists, public health epidemiologists, and health care providers to provide the necessary expertise for areas that have been identified previously as gaps.</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"14 1","pages":"3"},"PeriodicalIF":1.6,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9951428/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9257159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Focused NPIs in Statements and Questions","authors":"Sunwoo Jeong, F. Roelofsen","doi":"10.1093/jos/ffac014","DOIUrl":"https://doi.org/10.1093/jos/ffac014","url":null,"abstract":"\u0000 Negative Polarity Items (NPIs) with emphatic prosody such as ANY or EVER, and minimizers such as lift a finger or sleep a wink are known to generate particular contextual inferences that are absent in the case of non-emphatic NPIs such as unstressed any or ever. It remains an open question, however, what the exact status of these inferences is and how they come about. In this paper, we analyze these cases as NPIs bearing focus, and examine the interaction between focus semantics and the lexical semantics of NPIs across statements and questions. In the process, we refine and expand the empirical landscape by demonstrating that focused NPIs give rise to a variety of apparently heterogeneous contextual inferences, including domain widening in statements and inferences of negative bias in questions. These inferences are further shown to be modulated in subtle ways depending on the specific clause-type in which the NPI occurs (e.g., polar questions vs. wh-questions) and the type of emphatic NPI involved (e.g., ANY vs. lift a finger). Building on these empirical observations, we propose a unified account of NPIs which posits a single core semantic operator, even, across both focused and unfocused NPIs. What plays a central role in our account is the additive component of even, which we formulate in such a way that it applies uniformly across statements and questions. This additive component of even, intuitively paraphrased as the implication that all salient focus alternatives of the prejacent of the operator must be settled in the doxastic state of the speaker, is selectively activated depending on the presence of focus alternatives, and is shown to be able to derive all the observed contextual inferences stemming from focused NPIs, both in statements and in questions.","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"34 1","pages":"1-68"},"PeriodicalIF":1.9,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81055982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MedLexSp - a medical lexicon for Spanish medical natural language processing.","authors":"Leonardo Campillos-Llanos","doi":"10.1186/s13326-022-00281-5","DOIUrl":"10.1186/s13326-022-00281-5","url":null,"abstract":"<p><strong>Background: </strong>Medical lexicons enable the natural language processing (NLP) of health texts. Lexicons gather terms and concepts from thesauri and ontologies, and linguistic data for part-of-speech (PoS) tagging, lemmatization or natural language generation. To date, there is no such type of resource for Spanish.</p><p><strong>Construction and content: </strong>This article describes an unified medical lexicon for Medical Natural Language Processing in Spanish. MedLexSp includes terms and inflected word forms with PoS information and Unified Medical Language System[Formula: see text] (UMLS) semantic types, groups and Concept Unique Identifiers (CUIs). To create it, we used NLP techniques and domain corpora (e.g. MedlinePlus). We also collected terms from the Dictionary of Medical Terms from the Spanish Royal Academy of Medicine, the Medical Subject Headings (MeSH), the Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT), the Medical Dictionary for Regulatory Activities Terminology (MedDRA), the International Classification of Diseases vs. 10, the Anatomical Therapeutic Chemical Classification, the National Cancer Institute (NCI) Dictionary, the Online Mendelian Inheritance in Man (OMIM) and OrphaData. Terms related to COVID-19 were assembled by applying a similarity-based approach with word embeddings trained on a large corpus. MedLexSp includes 100 887 lemmas, 302 543 inflected forms (conjugated verbs, and number/gender variants), and 42 958 UMLS CUIs. We report two use cases of MedLexSp. First, applying the lexicon to pre-annotate a corpus of 1200 texts related to clinical trials. Second, PoS tagging and lemmatizing texts about clinical cases. MedLexSp improved the scores for PoS tagging and lemmatization compared to the default Spacy and Stanza python libraries.</p><p><strong>Conclusions: </strong>The lexicon is distributed in a delimiter-separated value file; an XML file with the Lexical Markup Framework; a lemmatizer module for the Spacy and Stanza libraries; and complementary Lexical Record (LR) files. The embeddings and code to extract COVID-19 terms, and the Spacy and Stanza lemmatizers enriched with medical terms are provided in a public repository.</p>","PeriodicalId":15055,"journal":{"name":"Journal of Biomedical Semantics","volume":"14 1","pages":"2"},"PeriodicalIF":1.9,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9892682/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9619937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}