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Automating generation of textual class definitions from OWL to English.

ABSTRACT: BACKGROUND: Text definitions for entities within bio-ontologies are a cornerstone of the effort to gain a consensus in understanding and usage of those ontologies. Writing these definitions is, however, a considerable effort and there is often a lag between specification of the main part of an ontology (logical descriptions and definitions of entities) and the development of the text-based definitions. The goal of natural language generation (NLG) from ontologies is to take the logical description of entities and generate fluent natural language. The application described here uses NLG to automatically provide text-based definitions from an ontology that has logical descriptions of its entities, so avoiding the bottleneck of authoring these definitions by hand. RESULTS: To produce the descriptions, the program collects all the axioms relating to a given entity, groups them according to common structure, realises each group through an English sentence, and assembles the resulting sentences into a paragraph, to form as 'coherent' a text as possible without human intervention. Sentence generation is accomplished using a generic grammar based on logical patterns in OWL, together with a lexicon for realising atomic entities. We have tested our output for the Experimental Factor Ontology (EFO) using a simple survey strategy to explore the fluency of the generated text and how well it conveys the underlying axiomatisation. Two rounds of survey and improvement show that overall the generated English definitions are found to convey the intended meaning of the axiomatisation in a satisfactory manner. The surveys also suggested that one form of generated English will not be universally liked; that intrusion of too much 'formal ontology' was not liked; and that too much explicit exposure of OWL semantics was also not liked. CONCLUSIONS: Our prototype tools can generate reasonable paragraphs of English text that can act as definitions. The definitions were found acceptable by our survey and, as a result, the developers of EFO are sufficiently satisfied with the output that the generated definitions have been incorporated into EFO. Whilst not a substitute for hand-written textual definitions, our generated definitions are a useful starting point. AVAILABILITY: An on-line version of the NLG text definition tool can be found at http://swat.open.ac.uk/tools/. The questionaire and sample generated text definitions may be found at http://mcs.open.ac.uk/nlg/SWAT/bio-ontologies.html.

SUBMITTER: Stevens R

PROVIDER: S-EPMC3102894 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Automating generation of textual class definitions from OWL to English.

Stevens Robert R Malone James J Williams Sandra S Power Richard R Third Allan A

Journal of biomedical semantics 20110517

<h4>Background</h4>Text definitions for entities within bio-ontologies are a cornerstone of the effort to gain a consensus in understanding and usage of those ontologies. Writing these definitions is, however, a considerable effort and there is often a lag between specification of the main part of an ontology (logical descriptions and definitions of entities) and the development of the text-based definitions. The goal of natural language generation (NLG) from ontologies is to take the logical de ...[more]

PMID: 21624160

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Project description:IntroductionThe objective of present paper is to outline the methodology of the International Study of Definitions of English-Language Terms for Suicidal Behaviours (ISDELTSB). The aim of the study is to survey existing English language terms and definitions used around the world for suicidal behaviour.Methods and analysisThe ISDELTSB is a worldwide survey based on one 'designated expert' per each WHO-registered country. 'Experts' were contacted through the International Association for Suicide Prevention (IASP), the World Psychiatric Association and the World Organization of Family Doctors. Each individual was sent an invitation to participate and a link to an online questionnaire. A comparison sample was created by inviting all IASP members to respond to the questionnaire. The questionnaire was designed to assess respondents' preferences about a particular set of terms and definitions by using the four major criteria of the definition of suicide identified in the literature (outcome, intent, knowledge and agency). The questionnaire used a multiple-choice question format. Participants were asked to choose one term in the list for each of the proposed definitions. Statements and definitions in the questionnaire were elaborated using the four main features of the definition of suicide, starting by the definitions and terms for which there is already a certain degree of consensus and then progressing to definitions and terms less agreed on.Ethics and disseminationThe study protocol obtained approval of Griffith University's Ethics Committee (ethics reference number 2017/601) and in accordance with the Australian National Statement on Ethical Conduct in Human Research. Respondents are asked if they accept to be personally acknowledged in any output originating from this study, and if so to provide their full name, title and affiliations. If respondents do not accept, they are informed that the conduct of this research respects Griffith University's Privacy Plan and that identified personal information is confidential and that anonymity will at all times be safeguarded. As detailed in the questionnaire cover letter, by answering the online or paper version of the questionnaire, respondents express their consent to participate. Dissemination of results will be done through a peer-reviewed journal article publication. This study aims to map the international use of definitions and terms for suicidal behaviour and ideation and favour the future use of an internationally shared set of terms and definitions. This will hopefully avoid undue duplication of efforts and reliably permit meta-analysis of data produced in different countries.

Project description:BACKGROUND:The Gene Ontology (GO) consists of over 40,000 terms for biological processes, cell components and gene product activities linked into a graph structure by over 90,000 relationships. It has been used to annotate the functions and cellular locations of several million gene products. The graph structure is used by a variety of tools to group annotated genes into sets whose products share function or location. These gene sets are widely used to interpret the results of genomics experiments by assessing which sets are significantly over- or under-represented in results lists. F Hoffmann-La Roche Ltd. has developed a bespoke, manually maintained controlled vocabulary (RCV) for use in over-representation analysis. Many terms in this vocabulary group GO terms in novel ways that cannot easily be derived using the graph structure of the GO. For example, some RCV terms group GO terms by the cell, chemical or tissue type they refer to. Recent improvements in the content and formal structure of the GO make it possible to use logical queries in Web Ontology Language (OWL) to automatically map these cross-cutting classifications to sets of GO terms. We used this approach to automate mapping between RCV and GO, largely replacing the increasingly unsustainable manual mapping process. We then tested the utility of the resulting groupings for over-representation analysis. RESULTS:We successfully mapped 85% of RCV terms to logical OWL definitions and showed that these could be used to recapitulate and extend manual mappings between RCV terms and the sets of GO terms subsumed by them. We also show that gene sets derived from the resulting GO terms sets can be used to detect the signatures of cell and tissue types in whole genome expression data. CONCLUSIONS:The rich formal structure of the GO makes it possible to use reasoning to dynamically generate novel, biologically relevant groupings of GO terms. GO term groupings generated with this approach can be used in. over-representation analysis to detect cell and tissue type signatures in whole genome expression data.

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Automating generation of textual class definitions from OWL to English.

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Automating generation of textual class definitions from OWL to English.

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