One of the most important contributions that any decision
	support system can make to achieve wide acceptance among any community
	is to be able to justify its own suggestions. When dealing with highly
	technical and scientifically advanced practitioners like medical
	doctors or any other related clinical workers, the ability to justify
	itself using the domain specialist usual terminology and technicalities
	is imperative. In this article we demonstrate the use of an ontological
	framework as inferencing basis for automatic sound clinical suggestions
	providing. Our work has two main contributions, consolidating the
	use of \{OGCP\} (Ontology for General Clinical Practice) as foundation
	and providing controlled English justifications of the extracted
	suggestions. We found that clinical practitioners feel as acceptable
	the Attempto Controlled English justifications generated from the
	knowledge base

Baeta, Carlos

Mendes, David

Rodrigues, Irene Pimenta

AbstractOne of the most important contributions that any decision support system can make to achieve wide acceptance among any community is to be able to justify its own suggestions. When dealing with highly technical and scientifically advanced practitioners like medical doctors or any other related clinical workers, the ability to justify itself using the domain specialist usual terminology and technicalities is imperative. In this article we demonstrate the use of an ontological framework as inferencing basis for automatic sound clinical suggestions providing. Our work has two main contributions, consolidating the use of OGCP (Ontology for General Clinical Practice) as foundation and providing controlled English justifications of the extracted suggestions. We found that clinical practitioners feel as acceptable the Attempto Controlled English justifications generated from the knowledge base

Elsevier - Publisher Connector 

 Procedia Technology  9 ( 2013 )  1288 – 1293 
2212-0173 © 2013 The Authors Published by Elsevier Ltd. Open access under CC BY-NC-ND license.
Selection and/or peer-review under responsibility of SCIKA – Association for Promotion and Dissemination of Scientific Knowledge
doi: 10.1016/j.protcy.2013.12.144 
CENTERIS 2013 - Conference on ENTERprise Information Systems / PRojMAN 2013 - 
International Conference on Project MANagement / HCIST 2013 - International Conference on 
Health and Social Care Information Systems and Technologies 
Ontology based clinical practice justification in natural language 
David Mendesa,*, Irene Pimenta Rodriguesa, Carlos Baetab 
aUniversidade de Évora, 7004-516 Évora, Portugal 
bUnidade Local de Saúde do Norte Alentejano, 7300-853 Portalegre, Portugal 
Abstract 
One of the most important contributions that any decision support system can make to achieve wide acceptance among any 
community is to be able to justify its own suggestions. When dealing with highly technical and scientifically advanced 
practitioners like medical doctors or any other related clinical workers, the ability to justify itself using the domain 
specialist usual terminology and technicalities is imperative. In this article we demonstrate the use of an ontological 
framework as inferencing basis for automatic sound clinical suggestions providing. Our work has two main contributions, 
consolidating the use of OGCP (Ontology for General Clinical Practice) as foundation and providing controlled English 
justifications of the extracted suggestions. We found that clinical practitioners feel as acceptable the Attempto Controlled 
English justifications generated from the knowledge base.  
© 2013 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of 
CENTERIS/HCIST. 
Keywords: OGCP; OGMS; CPR; NLP; Clinical Concepts Extraction; Ontological Realism; Natural Language Justification; ACE 
* Corresponding author. Tel.: +351 96 3564161. 
E-mail address: dmendes@uevora.pt. 
Available online at www.sciencedirect.com
© 2013 The Authors Published by Elsevier Ltd. Open access under CC BY-NC-ND license.
Selection and/or peer-review under responsibility of SCIKA – Association for Promotion and Dissemination of 
Scientific Knowledge
ScienceDirect
1289 David Mendes et al. /  Procedia Technology  9 ( 2013 )  1288 – 1293 
1. Introduction 
The development of AI† tools like CDSS‡ have to rely upon strong KR§ techniques that are currently pro-
eminent in the research community. In the Biomedical domain at large and specifically in the healthcare sub-
domain major contributions have surfaced recently in the area of ontological representation of healthcare 
providing [1] and medicine in general [2].  
We are now using this ground for automatic reasoning and provide adequate justifications in 
natural/technical language to logically inferred conclusions. These justifications aim at getting good acceptance 
by the clinicians.  
We started our research trying to figure out how to create a reasoning framework that could provide a 
picture of the usual practice of any MD. This was an attempt to deliver CDSS systems based in Semantic Web 
technologies and state of the art automatic reasoning tools. We went to explore HL7 Messaging as information 
source for our KR efforts [3] where we present a detailed state of the art. After thorough evaluation of 
ontologies state-of-the-art in the healthcare domain we started our exploration of CPR** [4] and its enrichment 
from the widest available semi-structured corpora that are text reports [5]. We felt, however, that the structure 
was feeble to support some theoretical foundations of clinical thinking and we thought of adopting the, yet 
novel, but deep rooted in the philosophical and medical community OGMS†† [6] using DO‡‡ [7] for the concept 
linkage. With a generated knowledge base available we try to figure out some inferred conclusions and present 
them in ACE§§ technical/natural jargon that was found acceptable as justification by clinical peers. 
 
2. Proposed system 
We follow the philosophic approach of Ontological Realism [8, 9] to extend the OGMS with CPR and DO 
and its foundational ontologies as shown in Fig. 1 into the OGCP [10].  
With OGCP in place we populate into a Clinical Practice KB*** as introduced in our previous work [3] thus 
rendering the framework for QA††† in the represented domain. 
 
 
 
 
 
 
† Artificial Intelligence 
‡ Clinical Decision Support System 
§ Knowledge Representation 
** Computer based Patient Record Ontology 
†† Ontology for General Medical Science 
‡‡ Disease Ontology 
§§ Attempto Controlled English 
*** Knowledge Base 
††† Question Answering 
1290   David Mendes et al. /  Procedia Technology  9 ( 2013 )  1288 – 1293 
 
Fig. 1 - Ontologies alignment in CPR. 
 
Figure 1 shows the underlying structure of CPR that almost in its entirety abides to the OBO Foundry 
initiative. Ghazvinian et al. [11] analyzed the six ontologies announced as OBO Foundry members on March 5, 
2010, and identified that the level of overlap was extremely low, but, notably, so was the level of term reuse. 
From their analysis, they conclude that while the OBO Foundry has made significant progress toward 
orthogonality during the period of the study through increased adoption of explicit term reuse, a large amount 
of overlap remains among these ontologies. In our work we do our best to overcome the different issues 
identified by the several experts in [2]. The theory behind BFO was developed initially by Barry Smith and 
Pierre Grenon and presented in a series of publications. Since then, important contributions to BFO have been 
made by many people including members of the BFO Discussion Group [12]. 
We made an effort of trimming and pruning of the OGMS and CPR complementing in accordance to our 
team of cardiologists to better accommodate their needs expressed in the reports we sampled. That included 
some "gardening" to include: SO the Symptom Ontology, VSO the Vital Signs Ontology and others all of them 
accord to OBO Foundry principles. 
In order to align the clinical concepts in the various ontologies present, an effort was needed to amalgamate 
them according to a sound theory of disease and that's why we incorporate the DO that was expressly built with 
this purpose in mind [13]. 
The Disease Ontology is a community driven, open source ontology that is designed to link disparate 
datasets through disease concepts. It's provided a computable structure of inheritable, environmental and 
infectious origins of human disease to facilitate the connection of genetic data, clinical data, and symptoms 
through the lens of human disease [14]. The DO semantically integrates disease and medical vocabularies 
through extensive cross mapping and integration of MeSH, ICD, NCI’s thesaurus, SNOMED CT and OMIM 
[15] disease-specific terms and identifiers. It represents a comprehensive knowledge base of 8043 inherited, 
developmental and acquired human diseases.  
1291 David Mendes et al. /  Procedia Technology  9 ( 2013 )  1288 – 1293 
3. Ontological Framework 
The rendered ontology framework reveals it's soundness for supporting the previously named concept of 
"clinical thinking" as pictured in [16]. 
 We developed a simple pragmatic approach to the representation of disease and diagnostic as illustrated in 
the referred article by Scheuermann. 
 
 
Fig. 2 - SOAP Points Insertion. 
 
The text for any particular encounter (actually for any Clinical Episode) may be collected in the form 
suitable for processing into the Ontology framework using some NLP pragmatics. Populating the OGCP the 
``Clinical Picture'' is completed and thus our KB is available for validation and further logical inferencing. The 
semantic representation is done using pragmatic interpretation as defined in our fellow researcher at 
CENTRIA‡‡‡ Dora Melo's article [17]. 
 
 
‡‡‡  Centre for Artificial Intelligence (CENTRIA) is a research centre from the Faculty of Science and Technology of the New 
University of Lisbon (FCT/UNL) 
1292   David Mendes et al. /  Procedia Technology  9 ( 2013 )  1288 – 1293 
The enrichment process must always maintain the entailments provided by the base (gold-standard) 
ontologies and so can never lead to inconsistency. We use a round-trip, debug and repair, building method to 
populate/enhance the OGCP then. For any new instance the validation is performed and new possible inferred 
facts generated if consistency is yet valid. These new facts are candidates for NLP justifications generation. 
The main objective of the system is to provide accurate answers to questions posed by users and, in our 
proposal these answers are clinically valid because the generation method guarantees that.  
QA is, however, only one of the interesting features of our work that is enhanced by the adequate 
justification to be evidently useful for practitioners. To develop justifications from DL§§§ arguments inferred 
from consequence based reasoners [18,19] we based our work in [20] to study and compare the justificatory 
structure to those present in the NCBO BioPortal addressed in the mentioned article.  
The results so far are in the realm of 'ontology verbalization', the generated explanations are still in a 
controlled natural language (CNL) fashion. The obtained results seem to be adequate enough for the users to 
find them believable and thus the justifications stand in our controlled clinical setting. We use the verbalization 
tooling [21] to present the justifications in an acceptable manner. The foundational techniques were introduced 
in [22]. For the verbalization to function properly all the restrictions of content are guaranteed in the process of 
ontology (Knowledge base) enrichment from SOAP reports. For instance, all names are English words and 
individuals are singular proper names (preferably capitalized) named classes are denoted by singular 
countable nouns and (object) properties by transitive verbs  in their lemma form (i.e. infinitive form) [22]. The 
decision of what inferred knowledge is then presented with its justifications to the user is a task handled by the 
DC**** using the developed pragmatics introduced in the above referred article [17]. 
 
4. Conclusion 
We are developing a knowledge representation infrastructure enabling the usage of highly optimized 
distributed consequence based reasoners that are referred in literature only in 2011. With these very recent 
developments it's finally possible to validate the enormous knowledge bases that are created by automatically 
populating a proposed ontology OGCP that relies on extensive, and very solid, foundations like SNOMED-CT 
and FMA among others. Logical inferencing and clinical facts entailment that is possible through this 
capability is an interesting contribution to the application of Artificial Intelligence to healthcare. We introduce 
clinical decision support systems (CDSS) that are based on such a breakthrough technique. We further argue 
that it is imperative, for the broad acceptance of these tooling by the medical community, that their inferences 
are justified using controlled natural language and adequate terminology. 
 
Acknowledgements 
We acknowledge IIFA - Instituto de Investigação e Formação Avançada of Universidade de Évora for the 
Bento de Jesus Caraça scholarship that is awarded to the corresponding author and CENTRIA – Center for 
Research in Artificial Intelligence of Faculdade de Ciências e Tecnologia of Universidade Nova de Lisboa for 
its continuous financial support of our work. 
 
 
§§§ Description Logic 
**** Discourse Controller 
1293 David Mendes et al. /  Procedia Technology  9 ( 2013 )  1288 – 1293 
References 
[1] Ogbuji, C. (2011). A framework ontology for computer-based patient record systems. In Proceedings of the ICBO: International 
Conference on Biomedical Ontology, pages 217-223, Buffalo, NY, USA. 
[2] Brochhausen, M., Burgun, A., Ceusters, W., Hasman, A., Leong, T., Musen, M., Oliveira, J., Peleg, M., Rector, A., Schulz, S., et al. 
(2011). Discussion of "biomedical ontologies: Toward scientific debate". Methods of information in medicine, 50(3), 217. 
[3] Mendes, D. and Rodrigues, I. P. (2011). A semantic web pragmatic approach to develop clinical ontologies, and thus semantic 
interoperability, based in hl7 v2.xml messaging. In HCist 2011 - Proceedings of the International Workshop on Health and Social 
Care Information Systems and Technologies. Springer-Verlag - book of the CCIS series (Communications in Computer and 
Information Science). 
[4] W3C (2009). Computer-based patient record ontology. Available Online: http://code.google.com/p/cprontology. 
[5] Mendes, D. and Rodrigues, I. P. (2012). Advances to semantic interoperability through cpr ontology extracting from soap framework 
reports. electronic Journal of Health Informatics. 
[6] OGMS (2010). Ontology for general medical science. Available Online: http://code.google.com/p/ogms. 
[7] DO (2010). Disease ontology. Available Online: http://diseaseontology.sourceforge.net/. 
[8] Smith, B., Ceusters, W., Klagges, B., Khler, J., Kumar, A., Lomax, J., Mungall, C., Neuhaus, F., Rector, A. L., and Rosse, C. (2005). 
Relations in biomedical ontologies. Genome Biol, 6(5), R46. 
[9] Smith, B. and Ceusters, W. (2010). Ontological realism: A methodology for coordinated evolution of scientific ontologies. Applied 
ontology, 5(3-4), 139-188. 
[10] David Mendes and Irene Pimenta Rodrigues. (2013). Ontology for general clinical practice. Available Online: 
http://www.semanticweb.org/david/ontologies/2013/0/OGCP. 
[11] Ghazvinian, A., Noy, N., Musen, M., et al. (2011). How orthogonal are the obo foundry ontologies. J Biomed Semantics, 2(Suppl. 2). 
[12] BFO (2012). Basic formal ontology 2.0. Available Online: http://code.google.com/p/bfo. 
[13] Schriml, L., Arze, C., Nadendla, S., Chang, Y., Mazaitis, M., Felix, V., Feng, G., and Kibbe, W. (2012). Disease ontology: a 
backbone for disease semantic integration. Nucleic Acids Research, 40(D1), D940{D946. 
[14] WIKI-DO (2012). Disease ontology wiki page. Available Online: http://dowiki.nubic.northwestern.edu/do-wiki/index.php/Main 
Page. 
[15] OMIM (2012). Omim, online mendelian inheritance in man. Available Online: http://www.ncbi.nlm.nih.gov/omim. 
[16] Scheuermann, R. H., Ceusters, W., and Smith, B. (2009). Toward an ontological treatment of disease and diagnosis. In 2009 AMIA 
Summit on Translational Bioinformatics, pages 116{120, San Francisco, CA. 
[17] Melo, D., Rodrigues, I., and Nogueira, V. (2012). Work out the semantic web search: The cooperative way. Advances in Artificial 
Intelligence, 2012. 
[18] Kazakov, Y. (2009). Consequence-driven reasoning for horn SHIQ ontologies. In C. Boutilier, editor, IJCAI, pages 2040-2045. IJCAI 
Distinguished Paper Award Winner. 
[19] Kazakov, Y., Krötzsch, M., and Simancancik, F. (2011). Concurrent classification of EL ontologies. In Proceedings of the 10th 
international conference on The semantic web - Volume Part I, ISWC'11, pages 305-320, Berlin, Heidelberg. Springer-Verlag. 
[20] Bail, S., Horridge, M., Parsia, B., and Sattler, U. (2011). The justificatory structure of the NCBO Bioportal ontologies. In L. Aroyo, 
C. Welty, H. Alani, J. Taylor, A. Bernstein, L. Kagal, N. F. Noy, and E. Blömqvist, editors, International Semantic Web Conference 
(1), volume 7031 of Lecture Notes in Computer Science, page 6782. Springer. 
[21] Kaljurand, K. (2010). Owl verbalizer: making machine-readable knowledge also human-readable. Available Online: 
https://code.google.com/p/owlverbalizer/. 
[22] Kaljurand, K. and Fuchs, N. E. (2007). Verbalizing owl in Attempto controlled English. Proceedings of OWLED07. 


Ontology based Clinical Practice Justification in Natural Language 

Repositório Científico da Universidade de Évora

 Available online at www.sciencedirect.com  Procedia Technology 00 (2013) 000–000   CENTERIS 2013 - Conference on ENTERprise Information Systems / HCIST 2013 - International Conference on Health and Social Care Information Systems and Technologies  Ontology based clinical practice justification in natural language David Mendesa*,Irene Pimenta Rodriguesa, Carlos Baetab aFirst affiliation, Address, City and Postcode, Country bSecond affiliation, Address, City and Postcode, Country    Abstract One of the most important contributions that any decision support system can make to achieve wide acceptance among any community is to be able to justify its own suggestions. When dealing with highly technical and scientifically advanced practitioners like medical doctors or any other related clinical workers, the ability to justify itself using the domain specialist usual terminology and technicalities is imperative. In this article we demonstrate the use of an ontological framework as inferencing basis for automatic sound clinical suggestions providing. Our work has two main contributions, consolidating the use of OGCP (Ontology for General Clinical Practice) as foundation and providing controlled English justifications of the extracted suggestions. We found that clinical practitioners feel as acceptable the Attempto Controlled English justifications generated from the knowledge base.   © 2013 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of CENTERIS/HCIST. Keywords: OGCP; OGMS; CPR; NLP; Clinical Concepts Extraction; Ontological Realism; Natural Language Justification; ACE   * Corresponding author. Tel.: +351 96 3564161. E-mail address: dmendes@uevora.pt. 2 Author name / Procedia Technology 00 (2012) 000–000 1. Introduction The development of AI† tools like CDSS‡ have to rely upon strong KR§ techniques that are currently pro-eminent in the research community. In the Biomedical domain at large and specifically in the healthcare sub-domain major contributions have surfaced recently in the area of ontological representation of healthcare providing [1] and medicine in general [2].  We are now using this ground for automatic reasoning and provide adequate justifications in natural/technical language to logically inferred conclusions. These justifications aim at getting good acceptance by the clinicians.  We started our research trying to figure out how to create a reasoning framework that could provide a picture of the usual practice of any MD. This was an attempt to deliver CDSS systems based in Semantic Web technologies and state of the art automatic reasoning tools. We went to explore HL7 Messaging as information source for our KR efforts [3] where we present a detailed state of the art. After thorough evaluation of ontologies state-of-the-art in the healthcare domain we started our exploration of CPR** [4] and its enrichment from the widest available semi-structured corpora that are text reports [5]. We felt, however, that the structure was feeble to support some theoretical foundations of clinical thinking and we thought of adopting the, yet novel, but deep rooted in the philosophical and medical community OGMS†† [6] using DO‡‡ [7] for the concept linkage. With a generated knowledge base available we try to figure out some inferred conclusions and present them in ACE§§ technical/natural jargon that was found acceptable as justification by clinical peers.  2. Proposed system We follow the philosophic approach of Ontological Realism [8, 9] to extend the OGMS with CPR and DO and its foundational ontologies as shown in Fig. 1 into the OGCP [10].  With OGCP in place we populate into a Clinical Practice KB*** as introduced in our previous work [3] thus rendering the framework for QA††† in the represented domain.          † Artificial Intelligence ‡ Clinical Decision Support System § Knowledge Representation ** Computer based Patient Record Ontology †† Ontology for General Medical Science ‡‡ Disease Ontology §§ Attempto Controlled English *** Knowledge Base ††† Question Answering  Author name / Procedia Technology 00 (2013) 000–000 3                 Fig. 1 Ontologies alignment in CPR.  Figure 1 shows the underlying structure of CPR that almost in its entirety abides to the OBO Foundry initiative. Ghazvinian et al. [11] analyzed the six ontologies announced as OBO Foundry members on March 5, 2010, and identified that the level of overlap was extremely low, but, notably, so was the level of term reuse. From their analysis, they conclude that while the OBO Foundry has made significant progress toward orthogonality during the period of the study through increased adoption of explicit term reuse, a large amount of overlap remains among these ontologies. In our work we do our best to overcome the different issues identified by the several experts in [2]. The theory behind BFO was developed initially by Barry Smith and Pierre Grenon and presented in a series of publications. Since then, important contributions to BFO have been made by many people including members of the BFO Discussion Group [12]. We made an effort of trimming and pruning of the OGMS and CPR complementing in accordance to our team of cardiologists to better accommodate their needs expressed in the reports we sampled. That included some "gardening" to include: SO the Symptom Ontology, VSO the Vital Signs Ontology and others all of them accord to OBO Foundry principles. In order to align the clinical concepts in the various ontologies present, an effort was needed to amalgamate them according to a sound theory of disease and that's why we incorporate the DO that was expressly built with this purpose in mind [13]. The Disease Ontology is a community driven, open source ontology that is designed to link disparate datasets through disease concepts. It's provided a computable structure of inheritable, environmental and infectious origins of human disease to facilitate the connection of genetic data, clinical data, and symptoms through the lens of human disease [14]. The DO semantically integrates disease and medical vocabularies through extensive cross mapping and integration of MeSH, ICD, NCI’s thesaurus, SNOMED CT and OMIM [15] disease-specific terms and identifiers. It represents a comprehensive knowledge base of 8043 inherited, developmental and acquired human diseases.  4 Author name / Procedia Technology 00 (2012) 000–000 3. Ontological Framework The rendered ontology framework reveals it's soundness for supporting the previously named concept of "clinical thinking" as pictured in [16].  We developed a simple pragmatic approach to the representation of disease and diagnostic as illustrated in the referred article by Scheuermann.       Fig. 2 SOAP Points Insertion  The text for any particular encounter (actually for any Clinical Episode) may be collected in the form suitable for processing into the Ontology framework using some NLP pragmatics. Populating the OGCP the  Author name / Procedia Technology 00 (2013) 000–000 5 ``Clinical Picture'' is completed and thus our KB is available for validation and further logical inferencing. The semantic representation is done using pragmatic interpretation as defined in our fellow researcher at CENTRIA‡‡‡ Dora Melo's article [17]. The enrichment process must always maintain the entailments provided by the base (gold-standard) ontologies and so can never lead to inconsistency. We use a round-trip, debug and repair, building method to populate/enhance the OGCP then. For any new instance the validation is performed and new possible inferred facts generated if consistency is yet valid. These new facts are candidates for NLP justifications generation. The main objective of the system is to provide accurate answers to questions posed by users and, in our proposal these answers are clinically valid because the generation method guarantees that.  QA is, however, only one of the interesting features of our work that is enhanced by the adequate justification to be evidently useful for practitioners. To develop justifications from DL§§§ arguments inferred from consequence based reasoners [18,19] we based our work in [20] to study and compare the justificatory structure to those present in the NCBO BioPortal addressed in the mentioned article.  The results so far are in the realm of 'ontology verbalization', the generated explanations are still in a controlled natural language (CNL) fashion. The obtained results seem to be adequate enough for the users to find them believable and thus the justifications stand in our controlled clinical setting. We use the verbalization tooling [21] to present the justifications in an acceptable manner. The foundational techniques were introduced in [22]. For the verbalization to function properly all the restrictions of content are guaranteed in the process of ontology (Knowledge base) enrichment from SOAP reports. For instance, all names are English words and individuals are singular proper names (preferably capitalized) named classes are denoted by singular countable nouns and (object) properties by transitive verbs  in their lemma form (i.e. infinitive form) [22]. The decision of what inferred knowledge is then presented with its justifications to the user is a task handled by the DC**** using the developed pragmatics introduced in the above referred article [17].  4. Conclusion We are developing a knowledge representation infrastructure enabling the usage of highly optimized distributed consequence based reasoners that are referred in literature only in 2011. With these very recent developments it's finally possible to validate the enormous knowledge bases that are created by automatically populating a proposed ontology OGCP that relies on extensive, and very solid, foundations like SNOMED-CT and FMA among others. Logical inferencing and clinical facts entailment that is possible through this capability is an interesting contribution to the application of Artificial Intelligence to healthcare. We introduce clinical decision support systems (CDSS) that are based on such a breakthrough technique. We further argue that it is imperative, for the broad acceptance of these tooling by the medical community, that their inferences are justified using controlled natural language and adequate terminology.    ‡‡‡ Centre for Artificial Intelligence (CENTRIA) is a research centre from the Faculty of Science and Technology of the New University of Lisbon (FCT/UNL) §§§ Description Logic **** Discourse Controller 6 Author name / Procedia Technology 00 (2012) 000–000 Acknowledgements We acknowledge IIFA - Instituto de Investigação e Formação Avançada of Universidade de Évora for the Bento de Jesus Caraça scholarship that is awarded to the corresponding author and CENTRIA – Center for Research in Artificial Intelligence of Faculdade de Ciências e Tecnologia of Universidade Nova de Lisboa for its continuous financial support of our work. References [1] Ogbuji, C. (2011). A framework ontology for computer-based patient record systems. In Proceedings of the ICBO: International Conference on Biomedical Ontology, pages 217-223, Buffalo, NY, USA. [2] Brochhausen, M., Burgun, A., Ceusters, W., Hasman, A., Leong, T., Musen, M., Oliveira, J., Peleg, M., Rector, A., Schulz, S., et al. (2011). Discussion of "biomedical ontologies: Toward scientific debate". Methods of information in medicine, 50(3), 217. [3] Mendes, D. and Rodrigues, I. P. (2011). A semantic web pragmatic approach to develop clinical ontologies, and thus semantic interoperability, based in hl7 v2.xml messaging. In HCist 2011 - Proceedings of the International Workshop on Health and Social Care Information Systems and Technologies. Springer-Verlag - book of the CCIS series (Communications in Computer and Information Science). [4] W3C (2009). Computer-based patient record ontology. Available Online: http://code.google.com/p/cprontology. [5] Mendes, D. and Rodrigues, I. P. (2012). Advances to semantic interoperability through cpr ontology extracting from soap framework reports. electronic Journal of Health Informatics. [6] OGMS (2010). Ontology for general medical science. Available Online: http://code.google.com/p/ogms. [7] DO (2010). Disease ontology. Available Online: http://diseaseontology.sourceforge.net/. [8] Smith, B., Ceusters, W., Klagges, B., Khler, J., Kumar, A., Lomax, J., Mungall, C., Neuhaus, F., Rector, A. L., and Rosse, C. (2005). Relations in biomedical ontologies. Genome Biol, 6(5), R46. [9] Smith, B. and Ceusters, W. (2010). Ontological realism: A methodology for coordinated evolution of scientific ontologies. Applied ontology, 5(3-4), 139-188. [10] David Mendes and Irene Pimenta Rodrigues. (2013). Ontology for general clinical practice. Available Online: http://www.semanticweb.org/david/ontologies/2013/0/OGCP. [11] Ghazvinian, A., Noy, N., Musen, M., et al. (2011). How orthogonal are the obo foundry ontologies. J Biomed Semantics, 2(Suppl. 2). [12] BFO (2012). Basic formal ontology 2.0. Available Online: http://code.google.com/p/bfo. [13] Schriml, L., Arze, C., Nadendla, S., Chang, Y., Mazaitis, M., Felix, V., Feng, G., and Kibbe, W. (2012). Disease ontology: a backbone for disease semantic integration. Nucleic Acids Research, 40(D1), D940{D946. [14] WIKI-DO (2012). Disease ontology wiki page. Available Online: http://dowiki.nubic.northwestern.edu/do-wiki/index.php/Main Page. [15] OMIM (2012). Omim, online mendelian inheritance in man. Available Online: http://www.ncbi.nlm.nih.gov/omim. [16] Scheuermann, R. H., Ceusters, W., and Smith, B. (2009). Toward an ontological treatment of disease and diagnosis. In 2009 AMIA Summit on Translational Bioinformatics, pages 116{120, San Francisco, CA. [17] Melo, D., Rodrigues, I., and Nogueira, V. (2012). Work out the semantic web search: The cooperative way. Advances in Artificial Intelligence, 2012. [18] Kazakov, Y. (2009). Consequence-driven reasoning for horn SHIQ ontologies. In C. Boutilier, editor, IJCAI, pages 2040-2045. IJCAI Distinguished Paper Award Winner. [19] Kazakov, Y., Krötzsch, M., and Simancancik, F. (2011). Concurrent classification of EL ontologies. In Proceedings of the 10th international conference on The semantic web - Volume Part I, ISWC'11, pages 305-320, Berlin, Heidelberg. Springer-Verlag. [20] Bail, S., Horridge, M., Parsia, B., and Sattler, U. (2011). The justificatory structure of the NCBO Bioportal ontologies. In L. Aroyo, C. Welty, H. Alani, J. Taylor, A. Bernstein, L. Kagal, N. F. Noy, and E. Blömqvist, editors, International Semantic Web Conference (1), volume 7031 of Lecture Notes in Computer Science, page 6782. Springer. [21] Kaljurand, K. (2010). Owl verbalizer: making machine-readable knowledge also human-readable. Available Online: https://code.google.com/p/owlverbalizer/. [22] Kaljurand, K. and Fuchs, N. E. (2007). Verbalizing owl in Attempto controlled English. Proceedings of OWLED07. 

Ontology based Clinical Practice Justification in Natural Language

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Ontology based Clinical Practice Justification in Natural Language

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