The Foundational Model of Anatomy Ontology

Rosse, Cornelius; Mejino, José L. V.

doi:10.1007/978-1-84628-885-2_4

Cited by 157 publications

(130 citation statements)

References 25 publications

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“…Since the OBO Foundry was established, ontologies such as the GO and the Foundational Model of Anatomy (FMA) 13 have been reformed and new ontologies created on the basis of its principles14 -16. Perhaps most importantly, ontologies have been laid to rest.…”

Section: Progress Thus Farmentioning

confidence: 99%

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The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration

et al. 2007

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The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or 'ontologies'. Unfortunately, the very success of this approach has led to a proliferation of ontologies, which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium is pursuing a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing coordinated reform, and new ontologies are being created on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable and logically well formed and to incorporate accurate representations of biological reality. We describe this OBO Foundry initiative and provide guidelines for those who might wish to become involved.In the search for what is biologically and clinically significant in the swarms of data being generated by today's high-throughput technologies, a common strategy involves the creation and analysis of 'annotations' linking primary data to expressions in controlled, structured vocabularies, thereby making the data available to search and to algorithmic processing 1 . The most successful such endeavor, measured both by numbers of users and by reach across species and granularities, is the Gene Ontology (GO) 2 . There exist over 11 million annotations relating gene products described in the UniProt, Ensembl and other databases to terms in the GO3, of which half a million have been manually verified by specialist curators in different modelorganism communities on the basis of the analysis of experimental results reported in 52,000 scientific journal articles (http://www.ebi.ac.uk/GOA/). Data related to some 180,000 genes have been manually annotated in this way, an endeavor now being refined and systematized within the Reference Genome Project (US National Institutes of Health National Human Genome Research Institute grant 2P41HG002273-07), which will provide comprehensive GO annotations for both the human genome and a representative set of model-organism genomes in support of research on the primary molecular systems affecting human health. From retrospective mapping to prospective standardizationThe domain of molecular biology is marked by the availability of large amounts of well defined data that can be used without restriction as inputs to algorithmic processing. In the clinical domain, by contrast, only limited amounts of data are available for research purposes, and these still consist overwhelmingly of natural language text. Even where more systematic clinical data are available, the use of local coding schemes means that these data do not cumulate in ways useful to research 4 . One approach to solving this problem is the Unified Medical Language System (UMLS) 5 , a compendium of some 100 source vocabularies combined through a process of...

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Section: Progress Thus Farmentioning

confidence: 99%

“…The model of good practice in the formulation of definitions is the FMA 13 , a representation of types of anatomical entities built around two backbone hierarchies of 'is_a' and 'part_of' relations. The FMA imposes a rule whereby all definitions take the genus-species form:…”

Section: Models Of Good Practicementioning

confidence: 99%

The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration

et al. 2007

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“…GENIA is a corpus of biomedical literature compiled and annotated within the GENIA project (Kim et al, 2003). FMA stands for the Foundational Model of Anatomy Ontology, a knowledge source for biomedical informatics concerned with symbolic representation of the phenotypic structure of the human body (Rosse and Mejino Jr., 2008). GREC (Gene Regulation Event Corpus) is a semantically annotated English corpus of abstracts of biomedical papers (Thompson et al, 2009).…”

Section: Monolingual Training Datamentioning

confidence: 99%

Findings of the 2014 Workshop on Statistical Machine Translation

Bojar

Buck

Federmann

et al. 2014

Proceedings of the Ninth Workshop on Statistical Machine Translation

515

542

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This paper presents the results of the WMT14 shared tasks, which included a standard news translation task, a separate medical translation task, a task for run-time estimation of machine translation quality, and a metrics task. This year, 143 machine translation systems from 23 institutions were submitted to the ten translation directions in the standard translation task. An additional 6 anonymized systems were included, and were then evaluated both automatically and manually. The quality estimation task had four subtasks, with a total of 10 teams, submitting 57 entries.

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“…The in-domain monolingual corpora include (cf. Table 2): the Cochrane database of reviews of primary research in human health care and health policy [63], DrugBank -a bioinformatics and cheminformatics resource describing drugs [64], Gene Regulation Event Corpus (GREC) -a semantically annotated English corpus of abstracts of biomedical texts [65], the GENIA corpus of biomedical literature compiled and annotated within the GENIA project [66], the Foundational Model of Anatomy Ontology (FMA) -a knowledge source for biomedical informatics concerned with symbolic representation of the phenotypic structure of the human body [67], English texts extracted from the UMLS Metathesaurus [40], the Patient Information Leaflet Corpus (PIL) -a collection of documents giving instructions to patients about their medication [69], and finally, a large set of texts extracted from HONcode-certified sites (HON) that have been identified by language-detection libraries [70,71] to be English-language [72].…”

Section: In-domain Monolingual Corporamentioning

confidence: 99%

Adaptation of machine translation for multilingual information retrieval in the medical domain

Pecina¹,

Dušek²,

Goeuriot³

et al. 2014

Artificial Intelligence in Medicine

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Objective. We investigate machine translation (MT) of user search queries in the context of cross-lingual information retrieval (IR) in the medical domain. The main focus is on techniques to adapt MT to increase translation quality; however, we also explore MT adaptation to improve effectiveness of cross-lingual IR.Methods and Data. Our MT system is Moses, a state-of-the-art phrase-based statistical machine translation system. The IR system is based on the BM25 retrieval model implemented in the Lucene search engine. The MT techniques employed in this work include in-domain training and tuning, intelligent training data selection, optimization of phrase table configuration, compound splitting, and exploiting synonyms as translation variants. The IR methods include morphological normalization and using multiple translation variants for query expansion. The experiments are performed and thoroughly evaluated on three language pairs: Czech-English, German-English, and French-English. MT quality is evaluated on data sets created within the Khresmoi project and IR effectiveness is tested on the CLEF eHealth 2013 data sets.Results. The search query translation results achieved in our experiments are outstanding -our systems outperform not only our strong baselines, but also Google Translate and Microsoft Bing Translator in direct comparison carried out on all the language pairs. The baseline BLEU scores increased from 26.59 to 41.45 for Czech-English, from 23.03 to 40.82 for German-English, and from 32.67 to 40.82 for French-English. This is a 55% improvement on average. In terms of the IR performance on this particular test collection, a significant improvement over the baseline is achieved only for French-English. For Czech-English and German-English, the increased MT quality does not lead to better IR results.Conclusions. Most of the MT techniques employed in our experiments improve MT of medical search queries. Especially the intelligent training data selection proves to be very successful for domain adaptation of MT. Certain improvements are also obtained from German compound splitting on the source language side. Translation quality, however, does not appear to correlate with the IR performance -better translation does not necessarily yield better retrieval. We discuss in detail the contribution of the individual techniques and state-of-the-art features and provide future research directions.

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The Foundational Model of Anatomy Ontology

Cited by 157 publications

References 25 publications

The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration

The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration

Findings of the 2014 Workshop on Statistical Machine Translation

Adaptation of machine translation for multilingual information retrieval in the medical domain

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