=Paper=
{{Paper
|id=Vol-549/paper-1
|storemode=property
|title=A Rule Management and Elicitation Tool for SWRL Rule Bases
|pdfUrl=https://ceur-ws.org/Vol-549/paper1.pdf
|volume=Vol-549
|authors=Saeed Hassanpour,Martin J. O'Connor and Amar K. Das
|dblpUrl=https://dblp.org/rec/conf/ruleml/HassanpourOD09a
}}
==A Rule Management and Elicitation Tool for SWRL Rule Bases==
https://ceur-ws.org/Vol-549/paper1.pdf
A Rule Management and Elicitation Tool for SWRL
Rule Bases
Saeed Hassanpour, Martin J. O’Connor and Amar K. Das
Stanford Center for Biomedical Informatics Research
MSOB X215, 251 Campus Drive, Stanford, California, USA 94305
{saeedhp, martin.oconnor, amar.das}@stanford.edu
Abstract. Rules are increasingly being used to represent knowledge in
ontology-based systems on the Semantic Web. As the size of such rule bases
increases, users face the age old problem in understanding and managing the
complexity of the knowledge represented by the rules. To support rapid
exploration of rule bases and meet the scalability goals of the Semantic Web,
techniques are needed to provide simplified interpretations of rules as well as
high-level abstractions of their computational structures. To address this
problem, we have implemented a rule management tool called Axiomé. Axiomé
was developed as a Protégé-OWL plug-in and supports visual rule base
exploration, automated rule categorization, rule paraphrasing, and rule
elicitation functionality with the goal of facilitating the management of large
SWRL rule bases.
Keywords: Rule Management, Rule Elicitation, Rule Visualization, Rule
Paraphrasing, Rule Categorization, OWL, SWRL.
1 Introduction
SWRL rules are increasingly being used for knowledge representation in OWL
ontologies. As the size of these rule bases increases, users face the problem of
understanding and managing the increasing amounts of knowlege represented by the
rules. To support the management of the resulting complexity, rule management
techniques are needed. These techniques include the ability to categorize rules, to
provide simplified explaination of rules, and to provide high-level abstractions of rule
base structure. In particular, rule paraphrasing and rule visualization can help non-
specialists to understand the meaning of complex rules. Abstraction of common
patterns in rule bases can also enable automatic categorization of rules into related
groups. These categories can also be used as a basis of rule elicitation tools that guide
users when entering new rules.
To tackle this problem, we have developed rule management and elicitation
software called Axiomé [3]. Axiomé was developed as a plug-in for the popular
Protégé-OWL ontology development environment and provides an array of tools for
managing SWRL rule bases in OWL ontologies. These tools supports visual rule base
exploration, automated rule categorization, rule paraphrasing, and rule elicitation
functionality with the goal of facilitating the management of large SWRL rule bases.
2 Axiomé: Core Features
Axiomé was developed as a Protégé-OWL plug-in and has five main functional areas.
These functionalities are available as sub-tabs within the plug-in: (1) a Rule Graph tab
that provides a graph structure to browse and explore SWRL rule base and rule
relationships; (2) a Rule Visualization tab to visualize individual rules; (3) a Rule
Paraphrasing tab that displays an English-like text explanation for each rule; (4) a
Rule Categorization tab to automatically categorize rules in a rule base; and (5) a Rule
Elicitation tab that provides a graphical templates to acquire new rules based on
analysis of existing rules in a rule base. A Rule Browser component is permanently
displayed to show a tree-table representation of the SWRL rules in an ontology. This
tree-table enables users to explore the rule base and launch any of five sub-tabs for the
rule or group being explored.
2.1 Rule Graph
The Rule Graph tab provides a graphical representation of a SWRL rule base (Figure
1). Each rule in the rule base is presented as a node, and directed edges between two
rules indicate the SWRL atoms shared by the rules and the dependency direction. A
number of graphical layouts are supported, using the JUNG visualization framework
[2] to produce optimal graph layouts. Search functionality is also provided and rules
partially matching the search term can be highlighted in the graph. Rule groups and
the types of dependencies between rules which are based on the common SWRL
atoms between them, can also be indicated visually. Cyclical dependencies between
rules can be found and visually highlighted in the graph.
Figure 1. Example of a rule base visualization in Rule Graph tab using an autism-
related ontology. The figure also shows the process of searching a rule base for a
particular term, with the matching terms highlighted.
2.2 Rule Visualization
The Rule Visualization tab (Figure 2) allows individual rules to be visualized as a tree
structure. Trees are generated by depth-first search for each variable chain in the rule,
with more prominent atoms being placed near the root of each tree. A number of
heuristics are employed to ensure that the most important clauses in a rule are given
more prominence [1].
Figure 2. Example of the visualization of a simple rule from the family history
ontology in the Rule Visualization tab.
2.3 Rule Paraphrasing
The Rule Paraphrasing tab (Figure 3) uses a similar approach to build a tree structure
for each rule and then uses additional heuristics to generate understandable English
paraphrases of that rule [1].
Figure 3. Example of rule paraphrasing of a rule in the Rule Paraphrasing tab.
2.4 Rule Categorization
The Rule Categorization tab uses the data structure that is generated for the rule
visualization and paraphrasing tabs to automatically group the rules with a similar
syntactic structure [1]. It then graphically displays the results of this grouping. These
groupings can then be used in the Rule Graph tab when exploring the rule base.
2.5 Rule Elicitation
The Rule Elicitation tab (Figure 4) provides graphical rule templates to facilitate
acquisition of rules. It generates these templates using the syntactic structures
generated for rule groupings. Users can select an appropriate rule group and then
generate a graphical acquisition template to enter rules with the structure of other
rules in that group.
Figure 4. Example of rule elicitation in Rule Elicitation tab.
3 Summary
We have described Axiomé, a free, open-source Protégé-OWL plug-in for SWRL rule
management. It is compatible with the latest Protégé-OWL 3.4 release and is
available for download from the Axiomé web site [3].
References
1. Hassanpour, S., O’Connor, M.J., Das, A.K.: Exploration of SWRL Rule Bases through
Visualization, Paraphrasing, and Categorization of Rules. The International RuleML
Symposium on Rule Interchange and Applications, in press, 2009.
2. Java Universal Network/Graph Framework: http://jung.sourceforge.net
3. Axiomé: http://protegewiki.stanford.edu/index.php/Axiomé
4. SWRL Submission: http://www.w3.org/Submission/SWRL