[Use case] from medicine

Hi, below is a couple of use interesting use cases
which Christine Golbreich came up with.

Cheers, Uli

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Semantic Web Rules
Use Cases and Requirements for Health Care and Life Sciences

Christine Golbreich
Christine.Golbreich@univ-rennes1.fr
December 2005

This paper suggests some possible Use Cases and Requirements in the  
domain of Health Care and Life Sciences. Most of them are based on  
our experience with real applications in the biomedical domain. The  
list below gives a short description of the use cases and their  
implications in terms of requirements for a Web rule language for the  
W3C RIF WG. The first section (§1) concerns the basic requirement of  
compatibility with OWL. Section 2 lists different types of rule uses  
(§2). The use cases description follows in sections (§3- 4 -5). While  
Section 3 mainly focuses on the requirement of close and safe  
integration, the following cases illustrate other uses of rules in  
the medical domain. The title of each use case indicates its main  
implications in terms of a Web rule language.

1.      Compatibility

1.1.   [Use case] CG-1: A Web rule extension compatible with OWL DL
·        Outline: library of huge biomedical ontologies in OWL e.g.  
the FMA [17].
Life Sciences have a long tradition of controlled vocabularies. Large- 
scale terminologies, classifications and ontologies have been  
developed for many years in various biomedical domains. These  
resources have the potential to contribute to the Semantic Web for  
the Life Sciences. Several actual biomedical ontologies, e.g. The  
Foundational Model of Anatomy (FMA), the Medical Subject Headings  
(MeSH), the Gene Ontology™, the National Cancer Institute Thesaurus  
are converted to OWL. The conversion of other ontologies to OWL is  
also been investigated, including the UMLS® Metathesaurus® and  
Semantic Network and other ones e.g., SNOMED-CT, GALEN are  
represented in other DLs. New biomedical ontologies are now directly  
developed in OWL e.g.; BioPax an ontology for biological pathway  
information. For example, the FMA is the most complete ontology of  
human canonical anatomy. It contains more than 72,000 concepts and  
more than two million instantiations of 150 relations. 2/3 of the FMA  
(i.e. a subset of 40000 concepts) including about 40000 subclass  
axioms, with existential and union in rhs and other OWL constructors  
has been represented in OWL DL [17]. The FMA is used in several  
actual applications e.g. the Virtual Soldier project Virtual Soldier  
project (http://www.virtualsoldier.net/) [16]. This use case is  
connected to [CG7] (5.4) and [CG3] (3.2)
Implications:
-         DLs more suited for structural knowledge (ontology) than LP  
language
-         Impossible to use LP rules for representing large-scale  
biomedical ontologies
-         Interoperability between ontologies through OWL standard is  
necessary to allow reasoning across connected domains e.g. pathology,  
genes, anatomy.
-         As most of the ontologies use union or existential in rhs,  
OWL-DLP expressiveness is not enough
-         OWL DL expressiveness is required.
-         OWL DL reasoining services (consistency checking,  
classification) are crucial for the quality assurance of such large- 
scale biomedical ontologies.
In conclusion, many biomedical ontologies are represented in OWL DL,  
a Web rule language should be compatible with OWL DL.
2.      Different uses of rules

Rules are required for different tasks [1]:
(1)    “deductive rules” are needed for inferences based on  
dependencies between some ontology properties, such as the transfer  
of properties from parts to wholes (e.g. a disease located in an  
organ part, is located in the organ), or dependencies between  
topological and mereological properties in the brain-cortex [4]  
http://idm.univ-rennes1.fr/~obierlai/anatomy/annexes/annexes.pdf  
([CG-3] 3.2) For a long time, rule-based expert systems have shown  
the usefulness of deductive rules in health care e.g. for diagnosis  
([CG-6]- 5.2), guidelines (CG-7] 5.3) etc.
(2)    “meta-reasoning rules” are needed for facilitating meta- 
reasoning on ontology in  control or knowledge engineering tasks  
e.g., acquisition, validation, maintenance of an ontology [1] [10]  
([CG-8] 5.4)
(3)   “connecting rules” between ontologies are required for  
reasoning across several domains such as Genomics, Proteonomics,  
Pathology, for example when searching for correlations between  
diseases and the abnormality of a function of a protein coded by a  
human gene.
(4)    “mapping rules” for mapping ontologies in data integration,  
and querying heterogeneous sources e.g., patient data scattered in  
many Hospital Information Systems [11 -13-14-15] ([CG-5] 5.1)
(5)   “querying rules” expressing complex queries upon the Web or  
sources ([CG-5] 5.1) etc.

The Use Cases description and their requirements follow (more  
detailed description are given for each use case in the referred  
papers). Whatever the type of rules concerned, all the use cases need  
reasoning on ontology and rules. Most of them require a Web rule  
language allowing a close integration between ontology and rules.
3.      Integration

[Use Case] CG2 - CG3: Close integration between the ontology and rule  
language allowing safe reasoning
The first use-case illustrates a “mapping” approach between the SWRL  
extension and the LP language Jess on a simple example: the Family  
use-case, and its limitation. The second use-case illustrates the  
requirements of a close integration between the ontology and the rule  
language on a real application: Brain Anatomy digitally processed images

3.1.   [Use Case] CG-2 limitations of a “mapping” approach
·        Outline: The Family rules example and the SWRLJessTab  
Protégé plugin [2] [3]
The Family rules example is a simple example including an ontology  
representing the usual family relationships, and a rule base  
representing their dependencies.
The SWRLJessTab Protégé plugin presented at the 7th Protégé 2004  
Conference and the RuleML WS [2] [3] illustrates on the family  
example how some reasoning support might be provided to interoperate  
between SWRL and OWL, and why such loose interoperation between  
ontology and rules is not satisfying. SWRLJessTab, was intended to  
bridge between OWL, SWRL, Racer and Jess, for supporting reasoning  
with SWRL. The approach is illustrated on the ‘family’ SWRL rules  
(Rbox) and ontology (Tbox and Abox). Concepts individuals and role  
instances of the Abox are mapped to Jess facts. SWRL rules are mapped  
to Jess rules. After the Jess engine execution, the new Jess facts  
derived from Jess knowledge are converted into assertions on concepts  
and roles in Racer Abox by an inverse mapping process. Iterative  
calls of Racer and Jess are done until an inconsistency is detected  
or no new fact is inferred. The family use-case illustrates that, if  
reasoning is performed separately by an OWL reasoner and a rule  
engine, some inferences are obviously missed. Some results can even  
be false, when the OWL ontology and SWRL rules components are not  
closely integrated. The Protégé environment including a SWRL Editor  
and a plugin mechanism for integrating third party rule follows a  
similar “mapping” approach. [7] [6]. However, for the same reasons,  
this loose integration has similar limitations. First, the rule  
inferences are based only on the rule component, since the OWL  
ontology (Tbox) component is not integrated into the rule (Jess)  
knowledge base. As Jess knowledge base is incomplete, some inferences  
are inevitably missed. In fact, as DL and LP languages have basically  
different expressiveness and different semantics, a DL ontology e.g.,  
the family ontology, cannot be converted into an equivalent Jess  
knowledge base, unless serious restrictions of the ontology  
sublanguage e.g., to DLP fragment of OWL [8]. Second, even under DLP  
restrictions, the reasoning method consisting in deriving all  
consequences of a DL reasoner e.g., Racer for the (DLP) OWL  
component, all consequences of a rule engine e.g., Jess for the (DLP)  
SWRL component separately, and iterating until saturation or  
inconsistency, may still be not satisfying. One reason is that the  
ontology and rule components have incompatible semantics [9]: the OWL- 
DLP ontology being a fragment of OWL (the intersection of OWL with  
Horn clauses [8]) has open world first order semantics, while the DLP  
rule components has Datalog semantics (based on closed world  
assumption and Herbrand models). The soundness and completeness of  
the basic reasoning tasks (satisfiability and subsumption) should be  
carefully investigated. Although this simple approach is quite  
attractive, such loose integration have some limitations. The risks  
run in processing so have to be clearly identified and notified.
Implications :
-         A loose interoperation of SWRL rules with existing rules  
engine is not satisfying, interoperating between ontology and rules  
requires a close integration
-         A Web rule language should have a clear semantics that  
enables OWL DL and rules to safely interoperate.

3.2.   [Use case] CG-3: Safe integration of OWL DL with rules with  
clear semantics
·        Outline: Brain Anatomy digitally processed images
This use case on Brain Anatomy digitally processed images[1] (MRI)  
reported at Washington 2005 W3C Workshop on Rule Languages for  
Interoperability [4] and at the OWL Experiences and Directions  
Workshop collocated with the International Conference on Rule Markup  
Languages for the Semantic Web [5] presents some requirements of a  
Web Rule language expected for the Semantic Web Health Care and Life  
Sciences[2].
The general framework is sharing anatomical knowledge (ontology and  
rules) and tools (services) needed in the context of neuroimaging,  
applied both to medical practice, i.e. decision support in neurology  
and neurosurgery, and research about neurological pathology such as  
epilepsy, dementia, etc. The application aims at developing new  
methods for assisting the labeling of the brain cortex structures in  
MRI images. The approach proposed relies on a brain ontology storing  
the a priori “canonical” knowledge about the most important brain  
cortex structures, combined  with rules describing the dependencies  
between their properties. A simplified example is provided to  
illustrate the need for supplementing OWL with rules, for reasoning  
over the ontology complemented with rules. This example illustrates  
that solutions are missed if the Web ontology language and the rule  
languages are not closely integrated. Although it should be more  
carefully investigated, the language required for the rules might be  
a FOL extension with function-free Horn rules (with negation as  
failure).
Implications
-         A language extending OWL DL expressiveness with rules is  
required for Health Care and Life Sciences applications
-         Some reasoning support is required to reason over a  
knowledge base composed of an ontology and rules
-         The rule language to be devised should have a clear  
semantics that enables OWL DL and rules to safely interoperate  
(decidable, sound and complete reasoning)
In conclusion of these two use-cases, a close integration between  
ontology and rules allowing safe reasoning is required.
4.      Fuzzy rules

4.1.   [Use case] CG-4: Fuzzy Brain Anatomy
The Brain Anatomy use case has been extended for fuzzy reasoning, see  
use-case “Fuzzy Reasoning with Brain Anatomical Structures”,  
presented by Giorgos Stamou  et al.
5.      Other use cases with ontologies and rules

Several other uses cases can be provided in Health Care and Life  
Sciences, illustrating different uses of rules in that field.

5.1.    [Use case] CG-5: a language combining ontology and rules for  
semantic integration of heterogeneous innformation
·        Outline: integration of dialysis and transplantation data  
for strategic decisions in Health care [11 -13-14-15]
Semantic integration is now crucial in many biomedical domains where  
better patient care, as well as better understanding of diseases and  
sound decision making in public health requires accessing large  
amounts of data from heterogeneous resources. For example, strategic  
decisions in the field of organ failure public health policies for  
end-stage renal disease, dialysis or transplantation requires  
accessing patient data scattered in many Hospital Information  
Systems. A simple scenario can be provided, based on a first case  
study that was achieved two years ago with the National French  
Biomedicine Agency (Agence de Biomedecine). The goal of the use-case  
might be to answer queries from 3 local databases where the dialysis  
and transplant data are stored and a (partial) dialysis and  
transplantation ontology in OWL. The ontology (still under  
construction) is issued from the Biomedicine Agency  terminological  
server which was originally built in integrating several existing  
terminologies, e.g. the French Thesaurus of Nephrology and the  
International Classification of Diseases (ICD). A first prototype  
achieving this simple scenario was achieved using PICSEL mediator,  
according to the LAV approach based on CARIN language [11].
Implications
-         OWL DL is required for the dialysis and transplantation  
ontology
-         A rule language for “mapping rules” and for expressing the  
“queries”
-         A combination of OWL DL and rules that allows representing  
the ontology in OWL DL, expressing mapping rules between the local  
and global ontologies, formulating queries, so as to answer the user  
queries is desirable:

5.2.    [Use case] CG-6 Interoperating between ontology and rules
·        Outline: Diagnosis rules in Health Care
This use-case is based on the famous Clancey’s Heuristic  
Classification method [18]. According to it, Diagnosis involves three  
main steps: data abstraction, heuristic matching, and refinement.
-         data abstraction transforms the data (e.g., finding such as  
temperature value of 39° C ) into data abstractions (e.g., high  
fever), usually using abstraction rules
-         heuristic matching associates the previous data to a  
generic hypothesis (e.g. disease), using heuristic matching rules
-         refinement allows to specialize the hypothesis into more  
refined  hypothesis, based on an ontology (e.g. ontology of diseases  
such as SNOMED-CT)
This use-case scenario may come with various scenarios, for example  
including fuzzy reasoning e.g., for breast cancer diagnosis based on  
hybrid reasoning combining fuzzy results issued from digital image  
analysis with reasoning based on the ACR classification (from the  
American College of Radiology) of mammography images, or with the TNM  
Breast Tumors classification.
Implications
-         Interoperating between ontology (Tbox) and rules (Rbox)
-         possibly fuzzy reasoning

5.3.    [Use case] CG-7 Combining ontologies and rules
·        Outline: Guidelines assisting decision making in Health Care
Another use-case and scenario might be provided in the domain of  
Recommendations for Chronic Diseases (guidelines), more specially for  
Type 2 Diabetes. Guidelines. Therapeutic recommendations in the  
guidelines can be considered as rules composed of body and head [19].  
For chronic diseases, body are usually expressed as combinations of  
clinical and therapeutic criteria. Therapeutic criteria include  
patient’s past or ongoing treatments, i.e. earlier treatment that has  
been prescribed and its outcome in terms of efficacy and tolerance.  
Body are sets of therapeutic options, generally expressed in terms of  
therapeutic classes, but sometimes expressed otherwise, as a  
particular type or group of therapeutic agents. Here some examples of  
rules:
If oral monotherapy with maximal doses of sulfamide or metformin  
associated with lifestyle changes is not effective, then the  
monotherapy should be replaced by oral biotherapy
If a drug may interact with patient’s medication or other conditions  
e.g., contraindications do not prescribe this treatment.

Suggesting therapeutic recommendations require to  combine the  
knowledge issued from several ontologies, e.g., an ontology of drugs,  
an ontology of diseases, an ontology of food with the rules.
Implications
-         Combining ontology (Tbox) and  rules (Rbox)

5.4.   [Use case] CG-8: Reasoning with rules for building and  
validating ontologies
·        Outline: meta-reasoning rules for ontologies
The goal is to use a set of rules for building or validating  
ontologies such as the FMA (1.1) or the Brain Anatomy (3.2). For  
example, the rule below generates 221 relations between the classes  
of the the Brain Ontology [1] [10] such as CentralSulcus separates  
FrontalLobe and ParietalLobe.

IF Y is part of X and Z is not part of Y and T separates X and Z THEN  
T separates Y and Z
AE(?x), AE(?y),AE(?z),AE(?t),part-of(?y,?x, not part-of(?z,?y),  
separates(?t,?x,?z) => separates(?t,?y,?z)

Similary, the rule below expressing that if an entity has laterality,  
then its parts have the same laterality, enables to verify whether  
laterality constraints are respected in the FMA or Brain ontologies.

If X  is part of Y and Y has side Z then X has the same side
isPartOf(?x, ?y),hasSide(?y, ?z) => hasSide(?x,?z)
Implications
-         Interoperating between ontology and rules
6.      References

1.        Golbreich C, Dameron O, Gibaud B, Burgun A, Web ontology  
language requirements w.r.t expressiveness of taxononomy and axioms  
in medicine, 2nd International Semantic Web Conference, ISWC 2003,  
Sanibel Island, Florida, October 2003, LNCS 2870, Springer. 2003.
2.        Golbreich C., Combining Rule and Ontology Reasoners for the  
Semantic Web, Invited talk, Rules and Rule Markup Languages for the  
Semantic Web, Hiroshima, Japan, G. Antoniou, H. Boley Editors, LNCS  
3323, Springer, 2004.
3.       Golbreich, C., Imai, A. Combining SWRL rules and OWL  
ontologies with Protégé OWL Plugin, Jess, and Racer. 7th  
International Protégé Conference, Bethesda, MD, 2004.
4.       Golbreich, C. Bierlaire, O. Dameron, O. and B. Gibaud. Use  
case: Ontology with rules for identifying brain anatomical  
structures. W3C Workshop on Rule Languages for Interoperability, 2005.
5.        Golbreich, C. Bierlaire, O. Dameron, O. and B. Gibaud. What  
reasoning support for Ontology and Rules?  the brain anatomy case  
study, OWL Experiences and Directions Workshop, collocated with the  
International Conference on Rule Markup Languages for the Semantic  
Web, Galway, Ireland, 2005
6.        Martin O’Connor, Holger Knublauch, Samson Tu, Mark Musen,  
Writing Rules for the Semantic Web Using SWRL and Jess, Protégé With   
Rules workshop collocated with 8th International Protégé Conference,  
Bethesda, MD, Madrid, 2005.
7.        M. J. O’Connor, H. Knublauch, S. W. Tu, B. Grossof, M.  
Dean, W. E. Grosso, M. A. Musen. Supporting Rule System  
Interoperability on the Semantic Web with SWRL. Fourth International  
Semantic Web Conference (ISWC2005), Galway, Ireland, 2005
8.        Grosof B., Horrocks I., Volz R., and Decker S., Description  
Logic Programs: Combining Logic Programs with Description Logic In:  
Proc. 12th Intl. Conf. on the World Wide Web (WWW-2003), Budapest,  
Hungary, May 20-23, 2003.
9.        Ian Horrocks, Bijan Parsia, Peter Patel-Schneider, and  
James Hendler. Semantic web architecture: Stack or two towers? In  
Francois Fages and Sylvain Soliman, editors, Principles and Practice  
of Semantic Web Reasoning (PPSWR 2005), number 3703 in LNCS, pages  
37-41. SV, 2005.
10.     Dameron O., Gibaud B., Musen M. Using semantic dependencies  
for consistency management of an ontology of brain-cortex anatomy, KR- 
MED 2004.
11.     Golbreich C., Mercier S., Construction of the dialysis and  
transplantation ontology: advantages, limits, and questions about  
Protege OWL,
  Workshop on Medical Applications of Protégé, 7th International  
Protégé Conference, Bethesda, 2004 (Master report: data integration  
for Health Care decision support in dialysis and transplant, in French).
12.     Golbreich C., Burgun, Biomedical Information Integration, a  
hot issue, Poster at MEDINFO 2004, San Francisco, 2004.
13.     Golbreich C, Burgun A, Challenges for Biomedical Information  
Integration, Position Statement, in Proceedings of the Semantic  
Integration Workshop, Sanibel Island, Florida, Edited by AnHai Doan,  
University of Illinois at Urbana-Champaign, Alon Halevy, University  
of Washington, Natasha Noy, Stanford University, CEUR Vol 82, 2003.
14.     Maquet G., Golbreich C., Burgun A., >From an ontology-based  
search engine towards a more flexible integration for medical and  
biological information, In Proceedings of the Semantic Integration  
Workshop, Sanibel Island, Florida, Edited by AnHai Doan, University  
of Illinois at Urbana-Champaign, Alon Halevy, University of  
Washington, Natasha Noy, Stanford University, CEUR Vol 82, 2003
15.     Golbreich C., Burgun, Biomedical Information Integration, a  
hot issue, Poster at MEDINFO 2004, San Francisco, 2004
16.     Rubin DL, Dameron O, Bashir Y, Grossman D, Dev P and Musen  
MA, Using ontologies linked with geometric models to reason about  
penetrating injuries. American  Med Info J, 2005 (AMIA)
17.     C. Golbreich, S. Zhang, O. Bodenreider Foundational Model of  
Anatomy in OWL: experience and perspectives, OWL Experiences and  
Directions Workshop, collocated with the International Conference on  
Rule Markup Languages for the Semantic Web, Galway, Ireland, 2005  
(extended version submitted)
18.     W. Clancey. Heuristic classification. Artificial  
Intelligence, 27:289--350, 1985.
19.     Vahid Ebrahiminiaa, Catherine Duclosa, Regis Cohenb, Alain  
Venota, Representing the Patient’s Therapeutic History in Medical  
Records and in Guideline Recommendations for Chronic Diseases Using a  
Unique Model, MIE 2005





[1] Slides available at http://www.w3.org/2004/12/rules-ws/slides/ 
christinegolbreich.pdf
[2] Slides available http://www.med.univ-rennes1.fr/~cgolb/Slides/ 
OWLED-Rules-CG.pdf