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19 Infectious Disease Ontology
"... Abstract In the last decade, technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to ..."
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Abstract In the last decade, technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. More recently, ontologies have been shown to have significant benefits both for the analysis of data resulting from high-throughput technologies and for automated reasoning applications, and this has led to organized attempts to improve the structure and formal rigor of ontologies in ways that will better support computational analysis and reasoning. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology suite of interoperable ontology modules that together cover the entire infectious disease domain. Acknowledgments: LGC’s contributions were supported by a Career Award from the Burroughs-Wellcome
N.H.: NCBO Resource Index: Ontology-based search and mining of biomedical resources.
- Journal of Web Semantics (JWS)
, 2011
"... Abstract The volume of publicly available data in biomedicine is constantly increasing. However, these data are stored in different formats and on different platforms. Integrating these data will enable us to facilitate the pace of medical discoveries by providing scientists with a unified view of ..."
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Abstract The volume of publicly available data in biomedicine is constantly increasing. However, these data are stored in different formats and on different platforms. Integrating these data will enable us to facilitate the pace of medical discoveries by providing scientists with a unified view of this diverse information. Under the auspices of the National Center for Biomedical Ontology (NCBO), we have developed the Resource Index-a growing, large-scale ontology-based index of more than twenty heterogeneous biomedical resources. The resources come from a variety of repositories maintained by organizations from around the world. We use a set of over 200 publicly available ontologies contributed by researchers in various domains to annotate the elements in these resources. We use the semantics that the ontologies encode, such as different properties of classes, the class hierarchies, and the mappings between ontologies, in order to improve the search experience for the Resource Index user. Our user interface enables scientists to search the multiple resources quickly and efficiently using domain terms, without even being aware that there is semantics "under the hood."
N: A System for Ontology-Based Annotation of Biomedical Data
- Proceedings of the International Workshop on Data Integration in The Life Sciences 2008
"... Abstract. We present a system for ontology based annotation and indexing of biomedical data; the key functionality of this system is to provide a service that enables users to locate biomedical data resources related to particular ontology concepts. The system's indexing workflow processes the ..."
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Abstract. We present a system for ontology based annotation and indexing of biomedical data; the key functionality of this system is to provide a service that enables users to locate biomedical data resources related to particular ontology concepts. The system's indexing workflow processes the text metadata of diverse resource elements such as gene expression data sets, descriptions of radiology images, clinical-trial reports, and PubMed article abstracts to annotate and index them with concepts from appropriate ontologies. The system enables researchers to search biomedical data sources using ontology concepts. What distinguishes this work from other biomedical search tools is:(i) the use of ontology semantics to expand the initial set of annotations automatically generated by a concept recognition tool; (ii) the unique ability to use almost all publicly available biomedical ontologies in the indexing workflow; (iii) the ability to provide the user with integrated results from different biomedical resource in one place. We discuss the system architecture as well as our experiences during its prototype implementation (http://www.bioontology.org/ tools.html).
Selecting an Ontology for Biomedical Text Mining
"... Text mining for biomedicine requires a significant amount of domain knowledge. Much of this information is contained in biomedical ontologies. Developers of text mining applications often look for appropriate ontologies that can be integrated into their systems, rather than develop new ontologies fr ..."
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Text mining for biomedicine requires a significant amount of domain knowledge. Much of this information is contained in biomedical ontologies. Developers of text mining applications often look for appropriate ontologies that can be integrated into their systems, rather than develop new ontologies from scratch. However, there is often a lack of documentation of the qualities of the ontologies. A number of methodologies for evaluating ontologies have been developed, but it is difficult for users by using these methods to select an ontology. In this paper, we propose a framework for selecting the most appropriate ontology for a particular text mining application. The framework comprises three components, each of which considers different aspects of requirements of text mining applications on ontologies. We also present an experiment based on the framework choosing an ontology for a gene normalization system. 1
Enhancement of chemical entity identification in text using semantic similarity validation
- PloS ONE
, 2013
"... With the amount of chemical data being produced and reported in the literature growing at a fast pace, it is increasingly important to efficiently retrieve this information. To tackle this issue text mining tools have been applied, but despite their good performance they still provide many errors th ..."
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With the amount of chemical data being produced and reported in the literature growing at a fast pace, it is increasingly important to efficiently retrieve this information. To tackle this issue text mining tools have been applied, but despite their good performance they still provide many errors that we believe can be filtered by using semantic similarity. Thus, this paper proposes a novel method that receives the results of chemical entity identification systems, such as Whatizit, and exploits the semantic relationships in ChEBI to measure the similarity between the entities found in the text. The method assigns a single validation score to each entity based on its similarities with the other entities also identified in the text. Then, by using a given threshold, the method selects a set of validated entities and a set of outlier entities. We evaluated our method using the results of two state-of-the-art chemical entity identification tools, three semantic similarity measures and two text window sizes. The method was able to increase precision without filtering a significant number of correctly identified entities. This means that the method can effectively discriminate the correctly identified chemical entities, while discarding a significant number of identification errors. For example, selecting a validation set with 75 % of all identified entities, we were able to increase the precision by 28 % for one of the chemical entity identification tools (Whatizit), maintaining in that subset 97 % the correctly identified entities. Our method can be directly used as an add-on by any state-of-the-art entity identification tool that provides mappings to a database, in order to improve their results. The proposed
Using data mining to improve student retention in higher education: a case study
- In International Conerence on Enterprise Information Systems
, 2010
"... Abstract: Data mining combines machine learning, statistics and visualization techniques to discover and extract knowledge. One of the biggest challenges that higher education faces is to improve student retention (National Audition Office, 2007).
Student retention has become an indication of academ ..."
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Abstract: Data mining combines machine learning, statistics and visualization techniques to discover and extract knowledge. One of the biggest challenges that higher education faces is to improve student retention (National Audition Office, 2007).
Student retention has become an indication of academic performance and enrolment management. Our project uses data mining and natural language processing technologies to monitor student, analyze student academic behaviour and provide a basis for efficient intervention strategies. Our aim is to identify potential problems as early as possible and to follow up with intervention options to enhance student retention. In this paper we discuss how data mining can help spot students ‘at risk’, evaluate the course or module suitability, and tailor the interventions to increase student retention. 1
Help will be provided for this task: Ontology-Based Annotator Web Service
"... Abstract. Semantic annotation is part of the vision for the semantic web. Ontologies are required for this task, and although they are in common use, there is a lack of annotation tools for users that are convenient, simple to use and easily integrated into their processes. This paper presents an on ..."
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Abstract. Semantic annotation is part of the vision for the semantic web. Ontologies are required for this task, and although they are in common use, there is a lack of annotation tools for users that are convenient, simple to use and easily integrated into their processes. This paper presents an ontology-based annotator web service methodology that can annotate a piece of text with ontology concepts and return annotations in OWL. Currently, the annotation workflow is based on syntactic concept recognition (using concept names and synonyms) and on a set of semantic expansion algorithms that leverage the semantics in ontologies (e.g., is_a relations). The paper also describes an implementation of this service for life sciences and biomedicine. Our biomedical annotator service uses one of the largest available set of publicly available terminologies and ontologies. We used it to create an index of open biomedical resources. Both the deployed web service and a user interface can be accessed at
Measuring Semantic Similarity Between Biomedical Concepts Within Multiple Ontologies
- IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS
, 2009
"... Most of the intelligent knowledge-based applications contain components for measuring semantic similarity between terms. Many of the existing semantic similarity measures that use ontology structure as their primary source cannot measure semantic similarity between terms and concepts using multiple ..."
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Most of the intelligent knowledge-based applications contain components for measuring semantic similarity between terms. Many of the existing semantic similarity measures that use ontology structure as their primary source cannot measure semantic similarity between terms and concepts using multiple ontologies. This research explores a new way to measure semantic similarity between biomedical concepts using multiple ontologies. We propose a new ontology-structure-based technique for measuring semantic similarity in single ontology and across multiple ontologies in the biomedical domain within the framework of Unified Medical Language System (UMLS). The proposed measure is based on three features: 1) cross-modified path length between two concepts; 2) a new feature of common specificity of concepts in the ontology; and 3) local granularity of ontology clusters. The proposed technique was evaluated relative to human similarity scores and compared with other existing measures using two terminologies within UMLS
Using Ontologies for Medical Image Retrieval- An Experiment
"... Medical research and clinical workflows often involve collaboration between various institutions. Therefore, ontologies such as SNOMED CT 1 or the Foundational Model of Anatomy (FMA) 2 have gained acceptance as an important tool for a common standard of communication. Medical images are often stored ..."
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Medical research and clinical workflows often involve collaboration between various institutions. Therefore, ontologies such as SNOMED CT 1 or the Foundational Model of Anatomy (FMA) 2 have gained acceptance as an important tool for a common standard of communication. Medical images are often stored in
The Lexicon Builder Web service: building custom lexicons from two hundred biomedical ontologies
- AMIA Annu Symp Proc 2010;2010:587e91
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