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Learning Formal Definitions for Biomedical Concepts

George Tsatsaronis, Alina Petrova, Maria Kissa, Yue Ma, Felix Distel, Franz Baader, Michael Schroeder
2013 W3C Web Ontology Language (OWL) Experiences and Directions Workshop (OWLED)  
In this direction we propose a novel methodology to learn formal definitions for biomedical concepts from unstructured text.  ...  We evaluate experimentally the suggested methodology in learning formal definitions of SNOMED CT concepts, using their text definitions from MeSH.  ...  biomedical concept definitions. 3 The Problem of Learning Formal Definitions 3.1 Formal Definitions in SNOMED CT SNOMED CT ( [22] ) is a medical ontology describing concepts such as anatomical structures  ... 
dblp:conf/owled/TsatsaronisPKMDBS13 fatcat:cmyydre2jvgqnlx6uzddiu5k2i

Formalizing biomedical concepts from textual definitions

Alina Petrova, Yue Ma, George Tsatsaronis, Maria Kissa, Felix Distel, Franz Baader, Michael Schroeder
2015 Journal of Biomedical Semantics  
, (3) How do different machine learning algorithms compare to each other for the task of formal definition generation?, and, (4) What is the influence of the learning data size to the task?  ...  We develop a method that uses machine learning in combination with several types of lexical and semantic features and outputs formal definitions that follow the structure of SNOMED CT concept definitions  ...  Acknowledgements The current research work is funded by the Hybrid Reasoning for Intelligent Systems Unit (HYBRIS B1) established by the Deutsche Forschungsgemeinschaft (DFG).  ... 
doi:10.1186/s13326-015-0015-3 pmid:25949785 pmcid:PMC4422531 fatcat:ic2fn6etybcztk336nhoj6rbmq

A Pipeline for Supervised Formal Definition Generation

Alina Petrova
2014 Young Scientists' International Workshop on Trends in Information Processing  
The aim of this paper is to develop a method of creating formal definitions for biomedical concepts using textual information from scientific literature (PubMed abstracts), encyclopedias (Wikipedia), controlled  ...  Obtaining formalized knowledge from unstructured data is especially relevant for biomedical domain, since the amount of textual biomedical data has been growing exponentially.  ...  Formal definition generation pipeline can be used as a standalone tool for concept formalization, and it can also be integrated into ontology learning tools as a semi-automatic tool for the assistance  ... 
dblp:conf/ysip/Petrova14 fatcat:bkscwuiy7zc73kazqbjrzvjk3q

A Hybrid Approach to Learn Description Logic Ontology from Texts

Yue Ma, Alifah Syamsiyah
2014 International Semantic Web Conference  
Based on this approach, the present work aims to develop a system that takes biomedical texts as input and outputs the corresponding EL++ concept definitions.  ...  Moreover, the system allows users to trace textual causes for a generated definition, and also give feedback (i.e. correction of the definition) to the system to retrain its inner model, a mechanism for  ...  It extends the formal definition candidates learned by the Snomed-supervised relation extraction process [4, 3] with linguistic patterns to give a finer-grained translation of the learned candidates.  ... 
dblp:conf/semweb/MaS14 fatcat:hwaoqiirmfg4nen5mred4w5q2y

OMIT: Dynamic, Semi-Automated Ontology Development for the microRNA Domain

Jingshan Huang, Jiangbo Dang, Glen M. Borchert, Karen Eilbeck, He Zhang, Min Xiong, Weijian Jiang, Hao Wu, Judith A. Blake, Darren A. Natale, Ming Tan, Franca Fraternali
2014 PLoS ONE  
Our previous research has investigated the construction of a miR ontology, named Ontology for MIcroRNA Target Prediction (OMIT), the very first of its kind that formally encodes miR domain knowledge.  ...  Although it is unavoidable to have a manual component contributed by domain experts when building ontologies, many challenges have been identified for a completely manual development process.  ...  Leyue Wang for their help in the project.  ... 
doi:10.1371/journal.pone.0100855 pmid:25025130 pmcid:PMC4099014 fatcat:hsy3rgtu7bb7vkrtu3nv6k3fee

Building a Biomedical Tokenizer Using the Token Lattice Design Pattern and the Adapted Viterbi Algorithm

Neil Barrett, Jens Weber-Jahnke
2010 2010 Ninth International Conference on Machine Learning and Applications  
Tokenization is an important component of language processing yet there is no widely accepted tokenization method for English texts, including biomedical texts.  ...  Our machine learning approach differs from the previous split-join classification approaches. We evaluate our approach against three other tokenizers on the task of tokenizing biomedical text.  ...  This article has been published as part of BMC Bioinformatics Volume 12 Supplement 3, 2011: Machine Learning for Biomedical Literature Analysis and Text Retrieval.  ... 
doi:10.1109/icmla.2010.76 dblp:conf/icmla/BarrettW10 fatcat:cy5b5lvy5ncrpdo5t4fekn2rxi

Building a biomedical tokenizer using the token lattice design pattern and the adapted Viterbi algorithm

Neil Barrett, Jens Weber-Jahnke
2011 BMC Bioinformatics  
Tokenization is an important component of language processing yet there is no widely accepted tokenization method for English texts, including biomedical texts.  ...  Our machine learning approach differs from the previous split-join classification approaches. We evaluate our approach against three other tokenizers on the task of tokenizing biomedical text.  ...  This article has been published as part of BMC Bioinformatics Volume 12 Supplement 3, 2011: Machine Learning for Biomedical Literature Analysis and Text Retrieval.  ... 
doi:10.1186/1471-2105-12-s3-s1 pmid:21658288 pmcid:PMC3111587 fatcat:7urlmurecrbzbovlcvrj7egjqq

How might mathematics education be used to improve diagnostic reasoning?

Richard Cohen, Kevin W. Eva
2014 Diagnosis  
to a concepts-before-procedures sequence for students learning concepts related to decimals and associated arithmetic procedures [9] .  ...  Recent calls for pedagogical innovations supporting integrated learning in medicine are a move in this direction, but they await formal evaluation of educational effectiveness [14] .  ... 
doi:10.1515/dx-2013-0022 pmid:29539963 fatcat:dqflujm25rh3tdpfivjgm4mw6m

A semantic-based method for extracting concept definitions from scientific publications: evaluation in the autism phenotype domain

Saeed Hassanpour, Martin J O'Connor, Amar K Das
2013 Journal of Biomedical Semantics  
We examined three separate scenarios: (1) the snippet of text contained a definition already in the knowledge base; (2) the snippet contained an alternative definition for a concept in the knowledge base  ...  A variety of informatics approaches have been developed that use information retrieval, NLP and text-mining techniques to identify biomedical concepts and relations within scientific publications or their  ...  Also, they would like to acknowledge support from the National Database for Autism Research.  ... 
doi:10.1186/2041-1480-4-14 pmid:23937724 pmcid:PMC3765483 fatcat:3n3so5s6zjaohhhqhb3ogjpi4m

What is biomedical informatics?

Elmer V. Bernstam, Jack W. Smith, Todd R. Johnson
2010 Journal of Biomedical Informatics  
The emphasis on data plus meaning also suggests that biomedical informatics problems tend to be difficult when they deal with concepts that are hard to capture using formal, computational definitions.  ...  Biomedical informatics lacks a clear and theoretically-grounded definition.  ...  Sittig for valuable discussions regarding the ideas expressed in this manuscript. Supported in part by the Center for Clinical and Translational Sciences at UT-Houston (1UL1RR024148).  ... 
doi:10.1016/j.jbi.2009.08.006 pmid:19683067 pmcid:PMC2814957 fatcat:mbua4m25b5h4pozjaopaqsdppy

IMIA LaMB WG event: 'Biomedical Semantics in the Big Data Era', Workshop at MEDINFO 2015 – São Paulo,Brazil

Roland Cornet, Stephane Meystre, Stefan Schulz, Patrick Ruch, Tomasz Adamusiak, Laszlo Balkanyi, Jianying Hu
2019 Zenodo  
knowledge and information, by Stefan Schulz - Deep question-answering for biomedical decision support, by Patrick Ruch - Feature extraction for predictive modeling, by Jianying Hu - Connecting structured  ...  This document set document has (1) all the presentation materials: - Introduction, by Ronald Cornet - From free text to ontology, by Stephane Meystre - Bridging natural and formal languages for representing  ...  These scenarios show various usability and representation problems: high number of relationships for refinement and qualification, improper options for refinement, incorrect formal definitions, and lack  ... 
doi:10.5281/zenodo.3398964 fatcat:ksn3rajzs5earbjd3kczdvrwdi

OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction [article]

Fatima Zohra Smaili, Xin Gao, Robert Hoehndorf
2018 arXiv   pre-print
OPA2Vec can be used to produce vector representations of any biomedical entity given any type of biomedical ontology.  ...  Motivation: Ontologies are widely used in biology for data annotation, integration, and analysis.  ...  Therefore, we use transfer learning in OPA2Vec to assign a semantics to natural language words based on their use in a large corpus of biomedical text.  ... 
arXiv:1804.10922v1 fatcat:u6vwrilmnndyvirpghvmazqxom

Desiderata for ontologies to be used in semantic annotation of biomedical documents

Michael Bada, Lawrence Hunter
2011 Journal of Biomedical Informatics  
corpora of annotated documents for training and testing.  ...  Sophisticated natural-language-processing systems are needed to translate text into unambiguous formal representations grounded in high-quality consensus ontologies, and these systems in turn rely on gold-standard  ...  Acknowledgments The authors are grateful for helpful comments provided by Barry Smith of the OBO Foundry, Judith Blake of the Mouse Genome Informatics Group and the Gene Ontology Consortium, Alex Diehl  ... 
doi:10.1016/j.jbi.2010.10.002 pmid:20971216 fatcat:26fxn5mhbrfevhifzmwculjsda

Linked open data-based framework for automatic biomedical ontology generation

Mazen Alobaidi, Khalid Mahmood Malik, Susan Sabra
2018 BMC Bioinformatics  
For concept extraction, evaluation shows an average F-measure of 58.12% for CDR corpus and 81.68% for SemMedDB; F-measure of 65.26% and 77.44% for biomedical taxonomic relation extraction using datasets  ...  of CDR and SemMedDB, respectively; and F-measure of 52.78% and 58.12% for biomedical non-taxonomic relation extraction using CDR corpus and SemMedDB, respectively.  ...  Basically, we use the definition of the concept to measure the overlap with other discovered concepts definitions within the text, then we select the concepts that meet the threshold and have high overlap  ... 
doi:10.1186/s12859-018-2339-3 pmid:30200874 pmcid:PMC6131949 fatcat:drrkhq63zbeung3sssq2htltq4

Methods in biomedical ontology

Alexander C. Yu
2006 Journal of Biomedical Informatics  
Research on ontologies is becoming widespread in the biomedical informatics community.  ...  Discovering general, feasible methods has thus become a central activity for many of those hoping to reap the benefits of ontologies.  ...  Acknowledgments The author thanks James Cimino and an anonymous reviewer for their valuable feedback.  ... 
doi:10.1016/j.jbi.2005.11.006 pmid:16387553 fatcat:lejoszoldnhupo7irv6f6c2q5i
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