A. L. Rector, S. Bechhofer, C. A. Goble, I. Horrocks, W. A. Nowlan, and W. D. Solomon. The GRAIL concept modelling language for medical terminology. Artifical Intelligence in Medicine, 9:139--171, 1997.  @ NUS  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... problem (ExpTime) The performance improvement is often so great that it is impossible to measure precisely as unoptimised systems are virtually non terminating with ontologies that FaCT is easily able to deal with [Horrocks, 2000] Taking a large medical terminology ontology as an example [Rector et al. 1997] , FaCT is able to check the consistency of all 2,740 classes and determine the complete class hierarchy in about 45 seconds of (700MHz Pentium III) CPU time; unoptimised systems have been run for several weeks without their completing even a single class consistency test. In the current version ....

....their structure, function, cellular location and the processes in which they act. It is an ontology intended for retrieval purposes rather than hypothesis generation, so it is broad and shallow rather than deep and narrow [Baker et al. 1999] The TaO was originally modelled in the GRAIL DL [Rector et al. 1997] . It was subsequently migrated to OIL in order to (a) exploit OIL s high expressivity so as to maintain a better fidelity with biological knowledge as it is currently perceived; b) use reasoning support when building and evolving complex ontologies where the knowledge is dynamic and shifting; ....

A. Rector et al. The GRAIL concept modelling language for medical terminology. Artificial Intelligence in Medicine, 9:139--171, 1997.

....but also include some new heuristics designed to interact better with DLP s other optimisations. Performance DLP has not been used in any actual applications, and as an experimental system, it is unlikely to receive any such use. DLP has been used to classify the Galen medical knowledge base [ Rector et al. 1997 ] with the portions it cannot represent removed. DLP performed capably on this knowledge base, creating the subsumption partial order in 210 seconds on a Sparc Ultra 1 class machine. FaCT also takes about 200 seconds for this task on a comparable machine. DLP has also been tested on two sets of ....

A. Rector, S. Bechhofer, C. A. Goble, I. Horrocks, W. A. Nowlan, and W. D. Solomon. The Grail concept modelling language for medical terminology. Artificial Intelligence in Medicine, 9:139--171, 1997.

....Nazarenko et al. 13] successfully applied this technique to SNOMED. Although used in a different objective and limited in its scope, our method shares many of the techniques and corpora used by these authors. Finally, other approaches to analyzing terminologies include description logics [14]. These techniques may help to detect and fix semantic inconsistencies by automatically classifying the concepts (e.g. by comparing the expected classification to that proposed by the system) However, a significant amount of manual work is usually required for entering the terms into a ....

A.L. Rector, S. Bechhofer, C.A. Goble, I. Horrocks, W.A. Nowlan, W.D. Solomon, The GRAIL concept modelling language for medical terminology, Artif. Intell. Med. 9 (2) (1997) 139#/171.

....to represent ontologies. The most cited language is CLASSIC [Borgida et al. 1989] which is used by different systems including OBSERVER [Mena et al. 1996] SIMS [Arens et al. 1996] and the work of Kashyap and Sheth [Kashyap and Sheth, 1996b] Other terminological languages used are GRAIL [Rector et al. 1997] (the Tambis Approach [Stevens et al. 2000] LOOM [MacGregor, 1991] and OIL [Fensel et al. 2000] which is used for terminology integration in the BUSTER approach [Stuckenschmidt and Wache, 2000] Beside the purely terminological languages mentioned above there are also approaches using ....

A.L. Rector, S. Bechofer, C.A. Goble, I. Horrocks, W.A. Nowlan, and W.D. Solomon. The grail concept modelling language for medical terminology. Artificial Intelligence in Medicine, 9:139 -- 171, 1997.

....represent ontologies. The most often cited language is CLASSIC [Borgida et al. 1989] which is used by different systems including OBSERVER [Mena et al. 1996] SIMS [Arens et al. 1996] and the work of Kashyap and Sheth [Kashyap and Sheth, 1996b] Other terminological languages used are GRAIL [Rector et al. 1997] (the Tambis Approach [Stevens et al. 2000] LOOM [MacGregor, 1991] and OIL [Fensel et al. 2000] which is used for terminology integration in the BUSTER approach [Stuckenschmidt and Wache, 2000] In order to get an impression of the expressiveness these languages provide, we compared them ....

A.L. Rector, S. Bechofer, C.A. Goble, I. Horrocks, W.A. Nowlan, and W.D. Solomon. The grail concept modelling language for medical terminology. Artificial Intelligence in Medicine, 9:139 -- 171, 1997. 2

.... languages are Krypton [17] BACK [18] CLASSIC [19] Loom [20] and K Rep [21, 22] Cimino, in 1994 [6] argued for following a knowledge based approach to managing medical terminology, Rector and colleagues populated an implementation of the description logic GRAIL with medical terms [23], and Campbell and colleagues populated K Rep with SNOMED terms [15] Gradually, the medical informatics community has recognized the desirability of approaches more formal than those used in MeSH, SNOMED, ICD 9 CM, and the UMLS, but our goal to create systems that hold hundreds of thousands of ....

....with its parent and additional properties of its own. Such properties give the concept a structured definition, and terminology maintainers can use them to promote consistency of manual classification, as in the Read Codes [39] or to permit automatic classification, as in GALEN and SNOMED RT [23, 40]. A structured definition in the Read Codes is set of object attribute value triples [39] a structured definition in SNOMED RT is a superconcept and a set of roles and their role values [40, 41] and a structured definition in GALEN is a base concept and a set of criteria, composed of ....

[Article contains additional citation context not shown here]

Rector A, Bechhofer S, Goble C, et al. The GRAIL concept modelling language for medical terminology. Artificial Intelligence in Medicine 1997; 9(2): 139-171.

....in their recent works on natural language indexing, could play a major role in that situation, similarly to the one played for Medical Literature [8] To get highly specific results requires a strong model of the semantic. Such a model is currently being developed within the GALEN European project [9]. Furthermore, the GALEN project includes natural language analysis and natural language generation in a multilingual approach that is essential in the context of sharable Electronic Patient Record (EPR) 10, 11, 12] There are currently no available solution that addresses these issues. ....

A.L. Rector, S. Bechhofer, C.A. Goble, I. Horrocks, W.A. Nowlan, W.D. Solomon. The GRAIL concept modelling language for medical terminology. Artif Intell Med 9(2):139-171 (1997)

....but are vital for performing bioinformatics tasks. Other conceptualisations contradict domain knowledge, but are also needed for bioinformatics tasks. The translation of DNA to protein is an example of this kind of conceptualisation. The TaO is encoded in the Description logic (DL) GRAIL [13] and is delivered to TAMBIS through a terminology server (TeS) 3] that offers reasoning services to TAMBIS. The reasoning services allow new concepts to be formed dynamically by users and automatically classified within the TaO. So, the concept Protein can be joined to the function Receptor via ....

....queries. In addition, some of the TaO s concepts currently map directly to terms used within some of the resources. An increased number of resources in TAMBIS makes this untenable. Increased generality in the TaO avoids any bias in the conceptualisation. The TaO is currently encoded in GRAIL [13], a relatively inexpressive DL. Whilst this made the query processing easier, the conceptualisation, at some points,was awkward and contrived. The new TaO is encoded in the Ontology Inference Layer (OIL) 6] supported by the reasoning supplied by the DL FaCT [9] GRAIL only had conjunctive ....

A. Rector et al. The GRAIL concept modelling language for medical terminology. Artificial Intelligence in Medicine, 9:139--171, 1997.

....our discussion in a real world application, we will focus on the MED as our test bed vocabulary. The methodology developed herein will be applied to a complex subnetwork of the MED. Our approach is closely related to the principle of orthogonal taxonomies as implemented in the GALEN project [30,31]. There, a taxonomy is organized from the start by requiring that all primitive entities have only one primitive parent. In our methodology, an existing vocabulary is partitioned to achieve a similar effect. The rest of this paper is organized as follows. In Section 2, we describe the notions of ....

Rector A, Bechhofer S, Goble C, Horrocks I, Nowlan W, Solomon W. The GRAIL concept modelling language for medical terminology. Artif Intell Med 1997;9:139 -- 71.

....necessary. For example, parts of the TaO are simple asserted trees of concepts, whereas other parts are very elaborate and exploit the full expressive power of the OIL language. This ability forms the core of the methodology described in this paper. The TaO was originally modelled in the GRAIL DL [9]. It has been migrated to OIL in order to (a) exploit OIL s high expressivity, maintaining a better fidelity with biological knowledge as it is currently perceived; b) use reasoning support when building and evolving complex ontologies where the knowledge is dynamic and shifting; and (c) be able ....

A. Rector et al. The GRAIL concept modelling language for medical terminology. Artificial Intelligence in Medicine, 9:139--171, 1997.

....the task of finding or confirming the existence of such characteristics difficult to perform. The research was a case study on conceptual models of Medical and Bioinformatics terminologies, represented in the knowledge representation formalism GRAIL (GALEN Representation and Integration Language) [79, 125]. The models are respectively the core component of two projects. The GALEN Project (Generalised Architecture for Languages, Encyclopaedias and Nomenclatures in Medicine) 126, 69, 124] was a long term research project to provide the foundations for future clinical information and electronic 16 ....

....the complexity of Description Logics, and further understand the scope of this research, the following sub sections introduce Description Logics and GRAIL in an informal manner. 1.2. 1 Fundamentals of Description Logics Description Logics (DL) such as CLASSIC [19] LOOM [101, 102] and GRAIL [125], are a class of powerful declarative languages in the KL ONE family [174] The family is based on knowledge representation such as semantic networks frames with subsumption and multiple inheritance. Description Logic models have been used in a wide variety of data oriented applications including ....

RECTOR, A. L., BECHHOFER, S. K., GOBLE, C. A., HORROCKS,I.,NOWLAN, W. A., AND SOLOMON, W. D. The GRAIL Concept Modelling Language for Medical Terminology. Artificial Intelligence in Medicine 9 (1997), 139--171.

....of those systems, see for example [11] But this is not the case for transplantation, so we have to relay in the re use of solutions approaches to try to solve our problem. For the Knowledge Representation and Ontology construction for the medical domain we follow the work developed in GRAIL [29]. A comprehensive set of Internet resources for medical terminology is compiled in [18] The work of Decker [7] on the design of coordination mechanisms for groups of agents applied to Hospital scheduling is clear antecedent for our work. For planning in the medical domain we follow the work of ....

....clinical systems which are intuitive to use and adequately expressive to satisfy a number of broad requirements: clinical generality, medical expressiveness, operational flexibility, soundness and safety, verifiability and support for reusability. There exist some efforts in this line as GRAIL [29] or PROforma [10] that we will follow. The Surgeon Agent, specific for each kind of piece, is responsible of communicating with the surgeons through the Surgeon Interface and it collects and formalizes the requests for pieces for transplant. Each request has to include the relevant information ....

[Article contains additional citation context not shown here]

A. Rector, S. Bechhofer, C.A. Goble, I. Horrocks, W.A. Nolan, and W.D. Solomon. The GRAIL concept modellinglanguage for medical terminology. Artificial Intelligence in Medicine, 9:139--171, 1997.

....such as MeSH, CPM93, CPT98, SNOMED, ICD9 CM, etc. that are integrated into the UMLS [6, 7, 8, 9, 10, 11] a complex collection of terms, concepts, and relationships retrieved and integrated from a variety of existing medical information sources) GALEN s Core Model [12] expressed in GRAIL [13]) and the Medical Entities Dictionary (MED) 5, 14] Acceptance of these CMTs and others has been slow, however, partly due to their wide scope and high complexity; CMTs typically comprise on the order of tens of thousands to hundreds of thousands of interconnected concepts. The size and ....

Rector A, Bechhofer S, Goble C, Horrocks I, Nowlan W, Solomon W. The GRAIL concept modelling language for medical terminology. Artificial Intelligence in Medicine 1997;9:139--171.

....our discussion in a real world application, we will focus on the MED as our test bed vocabulary. The methodology developed herein will be applied to a complex subnetwork of the MED. Our approach is closely related to the principle of orthogonal taxonomies as implemented in the GALEN project [30, 31]. There, a taxonomy is organized from the start by requiring that all 3 primitive entities have only one primitive parent. In our methodology, an existing vocabulary is partitioned to achieve a similar effect. The rest of this paper is organized as follows. In Section 2, we describe the notions ....

A. Rector, S. Bechhofer, C. Goble, I. Horrocks, W. Nowlan, and W. Solomon. The GRAIL concept modelling language for medical terminology. Artificial Intelligence in Medicine, 9:139-- 171, 1997.

.... querying of domain (e.g. medical) knowledge [7, 32] Examples of such systems from the healthcare domain include MeSH [23] CPT98 [1] SNOMED [8] ICD9 CM [33] all of which have been integrated into the Unified Medical Language System (UMLS) 16, 18] GALEN s Core Model [29] expressed in GRAIL [28]) and the Medical Entities Dictionary (MED) This research was (partially) done under a cooperative agreement between the National Institute of Standards and Technology Advanced Technology Program (under the HIIT contract #70NANB5H1011) and the Healthcare Open Systems and Trials, Inc. consortium. ....

A. L. Rector, S. Bechhofer, C. A. Goble, I. Horrocks, W. A. Nowlan, and W. D. Solomon. The GRAIL concept modelling language for medical terminology. Artifical Intelligence in Medicine, 9:139--171, 1997.

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A. Rector, S. Bechhofer, C. A. Goble, I. Horrocks, W. A. Nowlan, and W. D. Solomon. The GRAIL concept modelling language for medical terminology. Artificial Intelligence in Medicine, 9:139--171, 1997.

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Rector A.L., Bechhofer S., Goble C.A., Horrocks I., Nowlan W.A., and Solomon W.D. The GRAIL Concept Modelling Language for Medical Terminology. Journal of Artificial Intelligence in Medicine, Kluwer Publishing, Vol 9, pp: 139-171, (1997).

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A. Rector, S. Bechhofer, C. A. Goble, I. Horrocks, W. A. Nowlan and W. D. Solomon. The GRAIL concept modelling language for medical terminology. Artificial Intelligence in Medicine, 9, 139--171, 1997.

No context found.

A. Rector, S. Bechhofer, C. A. Goble, I. Horrocks, W. A. Nowlan, and W. D. Solomon. The GRAIL concept modelling language for medical terminology. Artificial Intelligence in Medicine, 9:139--171, 1997.

No context found.

A. Rector, S. Bechhofer, C. A. Goble, I. Horrocks, W. A. Nowlan, and W. D. Solomon. The GRAIL concept modelling language for medical terminology. Artificial Intelligence in Medicine, 9:139-- 171, 1997.

No context found.

A. Rector, S. Bechhofer, C. A. Goble, I. Horrocks, W. A. Nowlan, and W. D. Solomon. The GRAIL concept modelling language for medical terminology. Artificial Intelligence in Medicine, 9:139--171, 1997.

No context found.

A.L. Rector, S. Bechhofer, C.A. Goble, Nowlan W.A. Horrocks, I., and W.D. Solomon. The grail concept modelling language for medical terminology. Arti cial Intelligence in Medicine, 9:139-171, 1997.

....of the femur being inferred to be a kind of fracture of the femur, or an ulcer located in the gastric mucosa being inferred to be a kind of stomach ulcer. The importance of these kinds of inference, particularly in medical terminology applications, is illustrated by the fact that the Grail DL [Rector et al. 1997] , which was specifically designed for use with medical terminology, is able to represent these kinds of propagation (although it is quite weak in other respects) Moreover, in another medical terminology application using the comparatively inexpressive DL a rather complex work around is ....

A. Rector, S. Bechhofer, C. A. Goble, I. Horrocks, W. A. Nowlan, and W. D. Solomon. The GRAIL concept modelling language for medical terminology. Artificial Intelligence in Medicine, 9:139--171, 1997.

.... IR Quality Assurance Quality Assurance Quality Assurance Quality Assurance Delivery DeliveryDelivery Delivery GRAIL Source Editor Langauge Annotator Compiled Common Reference Model GRAIL Source Editor: The Common Reference Model is authored in a description logic dialect called GRAIL [10]. Primary authoring occurs in text files that are then compiled to an internal form. In common with many description logic tools, roll back or undoing of the internal form is technically difficult, so revisions must be recompiled from the source files. Furthermore, GRAIL syntax demands that ....

Rector A, Bechhofer S, Goble C, Horrocks I, Nowlan W, Solomon W. The GRAIL concept modelling language for medical terminology. Artif Intell Med. 1997 Feb;9(2):139-71.

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Rector A, Bechhofer S, Goble C, Horrocks I, Nowlan W, Solomon WD. The GRAIL concept modelling language for medical terminology. Artificial Intelligence in Medicine 1997;9:139-171.

....and Part II High Level Schemata in Methods of Information in Medicine. 1. Introduction GALEN seeks to provide re usable terminology resources for clinical systems. The heart of GALEN is the use of an ontology, the Common Reference Model, formulated in a specialised description logic, GRAIL [1]. There are several other efforts to use description logics or closely related formalisms in medical terminology including SNOMED RT [2 4] the pioneering work of the Canon group [5 7] and more language oriented work, e.g. 8, 9] plus less formal methods of representing the semantics of ....

....must face and hopes to provide sufficient detail to open further debate on alternative approaches to these issues. This paper assumes a general familiarity with the ideas of formal classification and compositional concept representation. More detailed information and introductions can be found in [1, 23 25] and on the OpenGALEN web site. 1.1 Kinds of knowledge Modules of a terminology system A key feature of the GALEN approach is that it divides the problem of clinical knowledge representation and terminology into distinct parts each served by different software modules in the GALEN ....

[Article contains additional citation context not shown here]

Rector, A, Bechhofer, S, Goble, C, Horrocks, I, Nowlan, W, and Solomon, W, The GRAIL concept modelling language for medical terminology. Artificial Intelligence in Medicine, 1997. 9: pp. 139-171.

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Rector A, Bechhofer S, Goble C, Horrocks I, Nowlan W, Solomon W. The GRAIL concept modelling language for medical terminology. Artificial Intelligence in Medicine 1997;9:139-171.

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Rector, A., Bechhofer, S., Goble, C., Horrocks, I., Nowlan, W. and Solomon, W. (1997). The GRAIL concept modelling language for medical terminology. Artificial Intelligence in Medicine 9;: 139-171.

No context found.

Rector AL, Bechhofer SB, Goble CA, Horrocks I, Nowlan WA, Solomon WD. The GRAIL concept modelling language for medical terminology. Artif Intell Med, 1997;9:139-71.

.... have been several projects in the medical field applying either standard description logics or developing special purpose description logics with a restrictions and extensions specifically aimed at medical applications and different from those pursued initially in the broader theoretical community [3, 8, 9, 27, 33]. More broadly, both within and outside of medicine, description logics and their relations are being seen increasingly as the natural means of creating reusable terminological structure, or ontologies, for federating heterogeneous knowledge sources [12, 19, 25] and there is intense research on ....

....extremity, in the formalism of description logics, only concepts of the form C 1 R.C 2 where R is a relation (a role in description logic terms) and C 1 and C 2 are a concepts. This restriction does not reduce the worst case complexity but does allow improved optimisation of practical cases[27, 29]. Conclusion Does the conceptual knowledge need to be formal If we are to install software modules based on classification systems at the heart of our software for medical records, then the other modules must be able to depend on their behaving in predictable ways, and must be given explicit ....

A. Rector, S. Bechhofer, C. Goble, I. Horrocks, W. Nowlan, W. Solomon, The GRAIL concept modelling language for medical terminology, Artificial Intelligence in Medicine (1996) (in press).

....available to be reused in further compositional concepts. Most of the other biological ontologies are static the TAMBIS ontology is dynamic, built around a collection of concept descriptions and constraints on how they can be composed. The TAMBIS ontology is described using the DL called GRAIL [20]. In GRAIL, a new concept can be defined as follows: Base which r 1 f 1 : r n f n where each r i is a role name and each f i a filler concept. Each r i f i pair is also known as a criterion. A role is a property of a concept, and the filler of a role is the name or description of the concept ....

A.L. Rector, S.K. Bechhofer, C.A. Goble, I. Horrocks, W.A. Nowlan, and W.D. Solomon. The GRAIL Concept Modelling Language for Medical Terminology. Artificial Intelligence in Medicine, 9:139--171, 1997.

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A. L. Rector, S. Bechhofer, C. A. Goble, I. Horrocks, W. A. Nowlan, and W. D. Solomon. The GRAIL concept modelling language for medical terminology. Artifical Intelligence in Medicine, 9:139--171, 1997.

No context found.

A. Rector, S. Bechhofer, C. A. Goble, I. Horrocks, W. A. Nowlan, and W. D. Solomon. The grail concept modelling language for medical terminology. Artificial Intelligence in Medicine, 9:139--171, 1997.

No context found.

Rector AL, Bechhofer S, Goble CA, Horrocks I, Nowlan WA, Solomon WD. The GRAIL concept modelling language for medical terminology. Artif Intell Med 1997;9(2):139-71

No context found.

Rector AL, Bechhofer S, Goble CA, Horrocks I, Nowlan WA, Solomon WD. The GRAIL concept modelling language for medical terminology. Artif Intell Med 1997;9(2):139-71.

No context found.

Rector AL, Bechhofer S, Goble CA, Horrocks I, Nowlan WA, Solomon WD. The GRAIL concept modelling language for medical terminology. Artif Intell Med 1997;9(2):139-71

No context found.

Rector AL, Bechhofer S, Goble CA, Horrocks I, Nowlan WA, Solomon WD. The GRAIL concept modelling language for medical terminology. Artif Intell Med 1997;9(2):139-71

No context found.

A. Rector, S. Bechhofer, C. A. Goble, I. Horrocks, W. A. Nowlan, and W. D. Solomon. The grail concept modelling language for medical terminology. Artificial Intelligence in Medicine, 9:139--171, 1997.

No context found.

A.L. Rector, S.K. Bechhofer, C.A. Goble, I. Horrocks, W.A. Nowlan, and W.D. Solomon. The GRAIL concept modelling language for medical terminology. Artificial Intelligence in Medicine, 9:139--171, 1997.

No context found.

Rector AL, Bechhofer S, Goble CA, Horrocks I, Nowlan WA, Solomon WD. The GRAIL concept modelling language for medical terminology. Artif Intell Med 1997;9(2):139-71.

No context found.

Rector AL, Bechhofer S, Goble CA, Horrocks I, Nowlan WA, Solomon WD. The GRAIL concept modelling language for medical terminology. Artif Intell Med 1997;9(2):139-71.

No context found.

Rector AL, Bechhofer S, Goble CA, Horrocks I, Nowlan WA, Solomon WD. The GRAIL concept modelling language for medical terminology. Artif Intell Med 1997;9(2):139-71.

No context found.

Rector AL, Bechhofer S, Goble CA, Horrocks I, Nowlan WA, Solomon WD. The GRAIL concept modelling language for medical terminology. Artif Intell Med 1997;9(2):139-71

No context found.

Rector A. et al. The GRAIL concept modelling language for medical terminology. Art Int Med,9:139-171, 1997.

No context found.

Rector AL, Bechhofer S, Goble CA, Horrocks I, Nowlan WA, Solomon WD. The GRAIL concept modelling language for medical terminology. Artif Intell Med 1997;9(2):139-71

No context found.

Rector AL, Bechhofer SK, Goble CA, Horrocks I, Nowlan WA, Solomon WD. The GRAIL Concept Modelling Language for Medical Terminology. Artificial Intelligence in Medicine, Volume 9, 1997.

No context found.

Rector AL, Bechhofer S, Goble CA, Horrocks I, Nowlan WA, Solomon WD. The GRAIL concept modelling language for medical terminology. Artif Intell Med 1997;9(2):139-71.

No context found.

A. Rector, S. Bechhofer, C.A. Goble, I. Horrocks, W.A. Nolan, and W.D. Solomon, "The GRAIL concept modelling language for medical terminology," Artificial Intelligence in Medicine, vol. 9, pp. 139--171, 1997.

No context found.

A. Rector, S. Bechhofer, C. Goble, I. Horrocks, W. Nowlan, and W. Solomon. The GRAIL concept modelling language for medical terminology. Artificial Intelligence in Medicine, 9:139--171, 1997.

No context found.

A.L. Rector, S. Bechhofer, C.A. Goble, I. Horrocks, WA Nowlan and WD Solomon, The GRAIL concept modelling language for medical terminology, Artificial Intelligence in Medicine 9 (1997b) 139--171.

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