The Lexical Grid project is an on-going community driven initiative that provides a common terminology model to represent multiple vocabulary and ontology sources as well as a scalable and robust API for accessing such information. In order to add more powerful functionalities to the existing infrastructure and align LexGrid more closely with various Semantic Web technologies, we introduce the LexOWL project for representing the ontologies modeled within the LexGrid environment in OWL (Web Ontology Language). The crux of this effort is to create a &#x201c;bridge&#x201d; that functionally connects the LexBIG (a LexGrid API) and the OWL API (an interface that implements OWL) seamlessly. In this paper, we discuss the key aspects of designing and implementing the LexOWL bridge. We compared LexOWL with other OWL converting tools and conclude that LexOWL provides an OWL mapping and converting tool with well-defined interoperability for information in the biomedical domain

Christopher G. Chute

Cui Tao

Harold R. Solbrig

Md Drph

Pathak Phd

Phd Jyotishman

Jyotishman Pathak

Harold Solbrig

Christopher Chute

Crossref

LexOWL: A Bridge from LexGrid to OWL

Christopher  Chute

Nature Precedings

  
LexOWL: A Bridge from LexGrid to OWL 
Cui Tao PhD Jyotishman Pathak PhD Harold R. Solbrig Christopher G. Chute MD DrPH 
Mayo Clinic College of Medicine, Rochester, MN 
Abstract 
The Lexical Grid project is an on-going community 
driven initiative that provides a common terminology 
model to represent multiple vocabulary and ontology 
sources as well as a scalable and robust API for 
accessing such information. In order to add more 
powerful functionalities to the existing infrastructure 
and align LexGrid more closely with various Semantic 
Web technologies, we introduce the LexOWL project for 
representing the ontologies modeled within the LexGrid 
environment in OWL (Web Ontology Language). The 
crux of this effort is to create a “bridge” that 
functionally connects the LexBIG (a LexGrid API) and 
the OWL API (an interface that implements OWL) 
seamlessly. In this paper, we discuss the key aspects of 
designing and implementing the LexOWL bridge. We 
compared LexOWL with other OWL converting tools 
and conclude that LexOWL provides an OWL mapping 
and converting tool with well-defined interoperability 
for information in the biomedical domain. 
Introduction 
The Lexical Grid project (LexGrid) [2, 12] coordinated 
by the Mayo Clinic Division of Biomedical Statistics and 
Informatics provides support for a distributed network of 
lexical resources such as terminologies and ontologies 
via standards-based tools, storage formats, and access 
mechanisms. The LexGrid system supports formats such 
as HL7 RIM, OBO, OWL/Protégé frame, UMLS RRF, 
and LexGrid XML. It models ontology information 
including versioning, provenances, entities, associations, 
and instances. LexGrid loads ontologies and 
terminologies from different sources, maps the 
information into the LexGrid model, and stores them in a 
backend database. Information modeled by LexGrid can 
be accessed through LexBIG, an interface that 
implements the LexGrid model, on top of which standard 
tools and services can be built. 
A valuable augmentation to LexGrid is the adoption of 
Semantic Web technologies. The recent emergence of 
the Semantic Web and the Web Ontology Language 
(OWL) [4] is fostering a new level of interoperability. 
The biomedical informatics community greatly benefit 
by applying OWL’s combination of formal semantics, 
rich expressiveness and shared software base to 
biomedical and clinical terminologies. The LexOWL 
project provides a round trip between LexGrid and 
OWL. In this paper, we focus on the direction from 
LexBIG to OWL. Through LexOWL, information 
modeled in LexGrid can be represented in OWL. Hence, 
tools and services that have been developed by the 
Semantic Web community can be directly applied to the 
biomedical and clinical domain. To name a few, we can 
use Protégé, which is a widely-used OWL ontology 
authoring tool, to browse and edit the information 
modeled in LexGrid. We can apply different reasoning 
tools to medical and clinical terminologies, to check 
consistency or to infer new knowledge. We can use 
OWL ontology modularity tools to integrate or extract 
ontology modules as well as use OWL ontology mapping 
tools to map ontologies. The biomedical terminology 
community has been actively seeking connections to 
OWL. OBO2OWL [1], OBOInOWL [9], Protégé OBO 
to OWL Tab [10], and Protégé 4 OBO loader provide 
mappings and conversions from OBO to OWL. The 
conversion from UMLS Semantic Network to OWL has 
been studied in [6, 8]. The NCI Thesaurus to OWL DL 
conversion is discussed in [11]. The International 
Healthcare Terminology Standards Development 
Organization also released a Perl converter for 
converting from SNOMED CT to OWL in recent 
SNOMED CT releases. LexOWL augments all these 
efforts by providing LexGrid a converter to OWL. 
Compared to the other tools, LexOWL has an inherit 
advantage in that, it can convert all the ontologies and 
terminologies from different sources modeled by 
LexGrid without individual mappers and converters. As 
an immediate benefit, LexOWL provides a well-defined 
interoperability across these sources since all the 
different resources are modeled by LexGrid. 
We make the following contributions in this paper. 
 LexOWL functionally converts LexGrid to OWL 
through an API bridge and represents the 
information modeled in LexGrid in the OWL API 
representation. By doing so, we can leverage the 
services and tools developed for OWL and the 
Semantic Web directly. 
 LexOWL provides an OWL converter with 
relatively well-defined interoperability for different 
biomedical terminologies and ontologies. 
 LexOWL provides a dynamic interface between 
LexGrid and Protégé so that Protégé can use 
LexGrid as its backend database, which could be a 
valuable addition to Protégé 4. 
The rest of the paper is structured as fellow. We begin 
with an overview of the LexOWL system in Section 2. In 
Section 3, we discuss how LexOWL maps LexGrid 
components to OWL. In Section 4, we compare the 
OWL ontologies exported by LexOWL to those 
converted by the existing tools. Finally, in Section 5 we 
summarize and consider future work. 
  
LexOWL System Overview 
Figure 1 shows the LexOWL system overview. The core 
component of LexOWL is the LexOWLManager. It 
manages both the LexBIG service through which we can 
access the LexBIG API, and the OWL Ontology 
manager through which we can access the OWL API. On 
the left hand side of the system overview, the LexGrid 
system loads ontologies in different formats from 
different sources, translates them to LexGrid 
representation as well as saves the knowledge to a 
relational database. Through the LexBIG API, LexOWL 
can access the ontologies loaded in the database. On the 
right hand side of the system overview, through the 
OWL API, LexOWL re-represents the information in the 
LexGrid database virtually to the OWL API Ontology 
representation, which can the be used directly by Protégé 
4 and other Semantic Web tools. 
Thus, in essence, LexOWL maps LexGrid to OWL on 
the API level. It is not just a tool that maps and converts 
from one format to another. In addition to that, it 
generates a “bridge” between the two APIs. The “bridge” 
accesses information from the LexBIG API and 
translates it to the OWL API’s representations. The 
benefit of an API “bridge” is that even if the backend 
representations for ontologies change, the “bridge” still 
performs the same way and an update is not necessary. 
We also defined the LexGrid to OWL mapping and a 
lexgrid2owl meta-ontology [3], based on which  
LexOWL can re-represent a selected LexGrid ontology 
to the OWL API representation. In the next section, we 
discuss how LexOWL maps LexGrid to OWL. 
 
 
Figure 1 LexOWL System Overview. 
 
LexGrid to OWL Mapping 
LexOWL first maps the general ontology information. 
This includes information about the ontology itself such 
as name, version, and copyright. For some information, 
we can find equivalent representations in OWL (e.g., 
codingScheme to owl:ontology, localName to rdfs:label, 
and representsVersion to owl:versionInfo). For some 
information, we can find equivalent representations in 
standard name spaces such as dublin core (e.g., 
formalName to dc:title and copyright to dc:rights). We 
used the lexgrid2owl meta-ontology to represent the rest 
information (e.g, we define ApproxNumConcepts and 
isNative as two annotation properties in the meta- 
ontology). 
LexOWL maps each LexGrid concept11 to an OWL 
class. A concept in the LexGrid model can have 
properties such as a concept code, descriptions, 
presentations, definitions, and sources. LexOWL uses the 
concept code as the OWL class name and assign concept 
descriptions to a set of rdfs:label. In the lexgrid2owl 
meta-ontology, we define three OWL classes, 
Presentation, Definition, and Source, to represent the 
presentations, definitions, and sources in the LexGrid 
concept properties. We also defined annotation 
properties: hasPresentation, hasDefinition, and 
hasSource in the meta-ontology, to represent the 
relationships between concepts and such properties. 
Figure 2(a) shows a sample OBO term and Figure 2(b) 
shows its LexGrid representation. Figure 2(c) shows how 
LexOWL represents this concept and its properties in 
OWL. LexOWL creates an OWL class for the Concept 
Code “TAIR:0000055” and assign the Entity Description 
“pollen development” as a rdfs:label. The class has three 
annotation properties, one hasDefinition and two 
hasPresentations, which link to “definition21” (an 
instance of the Definition Class), “presentation37”, and 
“presentation38” (two instances of the Presentation 
Class) respectively. In addition, “definition21” has an 
annotation property hasSource, which links to 
“source21”. Each of these instances also has annotation 
properties that represent contents such as synonyms and 
definitions from the source document. 
LexGrid also has a special kind of concepts—
anonymous concepts—which it uses to represent the 
anonymous classes in OWL. LexOWL parses each 
anonymous class and translates it back to OWL based on 
concept properties. Figure 3 shows an example. The 
upper part shows the LexGrid representation. The 
concept “A38” is the anonymous concept which is 
equivalent to the concept “Father”. LexOWL can 
translate it back to OWL as the lower part of Figure 3 
shows, which is identical to the original OWL 
representation. 
 
(a) A Sample OBO Term 
                                                           
1 A “concept” represents a “kind” or “universal” entity in the 
LexGrid 2008 model. Here we still use “concept” to be compatible 
with LexGrid 2008. We are upgrading both LexGrid and LexOWL 
to avoid using this confusing label. 
 
  
 
(b) LexGrid Representation for the Sample Term 
 
 
(c) LexOWL Representation for the Sample Term 
Figure 2: An Example for Entity Mapping 
 
An association in the LexGrid model establishes a 
relation between two LexGrid entities. LexOWL 
classifies the LexGrid associations into two types: pre-
defined associations and other associations. A pre-
defined association can be directly mapped to an OWL 
element. For example, the associations “subClassOf” 
(OWL), “CHD” (ICD 10), and “is a” (OBO) are all 
mapped to owl:subClassOf. The association 
“hasSubtype” (UMLS) is mapped as an inverse of OWL 
element subClassOf. The associations “equivalentClass” 
(OWL) and “same as” (UMLS) are mapped to 
owl:equivalentClass. For detailed information about the 
pre-defined-association mapping, please see [3].  
 
Figure 3: An Example of Anonymous Concept  
Evaluation and Discussion 
We tested LexOWL using different ontologies from 
various sources: OWL, OBO, UMLS Semantic Network, 
and WHO ICD10. We used Protégé Prompt [5] to 
compare the OWL ontologies generated by LexOWL 
and by other tools. We also sampled concepts and 
associations in each test ontology and compared them 
with the original source and checked whether all the 
related information are represent properly. The details of 
the results are listed below. 
We tested on 5 OWL files. We chose these 5 ontologies 
carefully so that they cover most of the OWL Lite syntax 
introduced in [4]. We compared the OWL ontologies 
generated by LexOWL with the original ontologies. Each 
pair of ontologies is semantically equivalent to each 
other. 
We also tested on 10 OBO files. For each OBO file, we 
compared the OWL ontology translated by LexOWL 
with those converted by OBO2OWL [1], Protégé 3.3.1 
OBO to OWL Tab [10], and Protégé 4.0 OBO loader. 
All the four tools mapped OBO terms to OWL classes, 
OBO “isa” to OWL subClassOf, and used OWL 
someValuesFrom to represent relationships two classes. 
Semantically, the corresponding ontologies from all the 4 
converters are identical. However, each converter 
defined its own annotation properties and used different 
annotation properties to represent the same OBO 
information. OBO2OWL and Protégé 4.0 OBO loader 
have relatively simple and straightforward conversions 
where they used the OBO labels directly as the OWL 
annotation property names. Protégé OBO to OWL Tab 
and LexOWL processed information in a lower 
granularity (e.g., the “def” in Figure 2(a) is parsed and 
the source information is annotated separately.) 
We used LexOWL to export UMLS Semantic Network 
loaded in LexGrid to an OWL file and compared it with 
the one converted by Jimenez-Ruiz [6]. LexOWL uses 
the UIs as the OWL class names versus Jimenez-Ruiz 
uses the actual names. Hierarchically, these two 
ontologies are identical. Jimenez-Ruiz introduced some 
annotation properties that are specific for the UMLS 
Semantic Network where LexOWL used lexgrid2owl 
  
meta-ontology to represent all the information. For 
example, Jimenez-Ruiz mapped SRDEF to 
rdfs:comment, whereas LexOWL mapped it to 
lexgrid2owl :Definition, which can bring better 
interoperability since definitions of terms from other 
sources are also mapped to lexgrid2owl:Definition. 
Jimenez-Ruiz used owl:allValuesFrom to represent 
relationships between two classes and LexOWL used 
owl:someValuesFrom since this is the default restriction 
LexOWL uses for representing relationships between 
classes2. 
We also used LexOWL to export ICD10 WHO second 
edition loaded in LexGrid to an OWL file and compared 
it with the OWL file converted by Cardillo, et al. [7]. 
Hierarchically, these two ontologies are identical. The 
ontology converted by [7] only covered hierarchical 
information, however. Information such as exclusions 
and inclusions are ignored whereas LexOWL considered 
them as OWL ObjectProperties, thereby preserving the 
semantics. 
In summary, the test results show that LexOWL can 
convert information modeled in LexGrid to OWL 
successfully. LexOWL uses a single meta-ontology for 
all different sources where other tools use different meta-
ontologies even for the same format. Hence, the 
ontologies converted by LexOWL has better 
Interoperability that will bring benefits in ontology 
mapping, integration and reasoning in the future. 
Concluding Remarks and Future Work 
We introduced LexOWL, a system that functionally 
connects LexGrid to OWL through a bridge over the 
LexBIG and the OWL APIs. LexOWL can represent 
information modeled in LexGrid in the OWL API 
representation, so that tools and services that are 
developed for OWL can be applied to the biomedical 
terminologies and ontologies. LexOWL also provides a 
LexGrid-to-OWL converter with a well-defined 
interoperability for information from different sources 
and in different formats. 
As for the future work, several directions remain to be 
pursued. First, we would like to investigate performance 
of LexOWL with large-sized ontologies such as 
SNOMED CT, the Gene Ontology, and ICD10. We 
would like to add the editing and saving function as 
Figure 1 shows, so that we not only can browse, but also 
edit information represent in LexGrid using Protégé. 
Finally, LexOWL serves as a foundational pillar for 
ontology reasoning and inference. Our next step is to 
explore toward that direction on biomedical and clinical 
information. 
                                                           
2 How to represent the semantic relationships between classes in a 
more precise way is a problem we are investigating when mapping 
information to LexGrid and is out of the scope of this paper. 
 
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English

LexGrid to owl Mapping documentations. https:// cabig-kc.nci.nih.gov/Vocab/KC/index.php/LexGrid_ Documentation,

LexGrid: A framework for representing, storing, and querying biomedical terminologies from simple to sublime.

LexGrid: The Lexical Grid.

Logical analysis of mappings between medical classification systems.

OBO and OWL: Leveraging semantic web technologies for the life sciences.

OBO to OWL: a protégé OWL tab to read/save OBO ontologies.

Ontology Language Reference.

Representing the NCI Thesaurus in OWL DL: Modeling tools help modeling languages.

Representing the UMLS semantic network using OWL.

The Protégé Prompt Tab. http://protege.stanford .edu/plugins/prompt/prompt.html,

The UMLS Semantic Network in OWL.

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.172.7444

LexOWL: A Bridge from LexGrid to OWL

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