The paper describes the development of a legal decision support guide for owners corporation cases in the state of Victoria, Australia that uses an OWL ontology and Bayesian Network to perform legal reasoning. The rate of growth of owners corporations (also known as body corporate or strata title properties) has increased significantly in the last two decades. Because of this growth, and the need to manage a rapidly expanding population, the governance and management of these entities has become an important concern for government. Conflict and its management within them is an essential element of this concern. Cases that can't be settled through negotiation are often referred to the Victorian Civil and Administrative Tribunal (VCAT). Using an OWL ontology we have systematically modeled legal arguments and outcomes of past cases heard by VCAT to facilitate both stand alone and Web based information retrieval, extraction and case based reasoning. A Bayesian Belief network is also used to deal with assumptions that tend to be prevalent in commonsense reasoning. Through our system we aim to provide negotiation decision support to help guide owners corporation disputants through the grievance process.
The rate of growth of owners corporations (OC) in Australia, according to the
Australian Bureau of Statistics National Census 2006 is about twice that of detached
housing since 1981.1 In the big population centers of Sydney and Melbourne they
now comprise approximately a third of all dwellings. Because of this growth and the
need to manage a rapidly expanding population, the governance and management of
these entities has become an important concern for government. Conflict and its
management within them is an essential element of this concern (see [
Our research aims to promote better management of these conflicts by providing a
negotiation decision support guide for property owner disputes that mirrors judicial
reasoning practices so that disputants can negotiate more deliberatively before
proceeding to litigation. The system uses an OWL ontology to formalize legal
arguments, and a Bayesian Belief Network [
The paper commences with a discussion of cased based legal reasoning systems followed by a brief overview of recent initiatives involving the semantic Web and ontologies in the legal domain. Limitations of using ontologies for case based legal reasoning are examined and we describe how Bayesian Belief networks can help improve the inference capabilities. Specific aspects of the Victorian Owners Corporation Act (2006) are then described including the current legislative process for resolving disputes and the role of the Victorian Civil and Administrative Appeals Tribunal (VCAT). We identify factors considered by VCAT members in their discrete areas of decision making and show how these factors have been used to develop an OWL ontology and a Bayesian Belief network for the OC domain. Example queries are then used to demonstrate legal reasoning. The paper concludes with a brief discussion of our industry partner’s involvement in the project and our future research plan.
The ways in which past cases are used in arguments has long been of major research
interest to practitioners and academics in the field of artificial intelligence (AI) and
law. The current best known approach to Case Based Legal Reasoning is to represent
cases as collections of factors favoring plaintiff and defendant, e.g. Cato [
The Semantic Web is a Collective effort led by the W3C in which an evolved Web
describes data in a shared and formal format to be useful for people and machines
alike, allowing data to be shared and reused across applications, enterprises, and
community boundaries [
The OWL language became a W3C recommendation for building ontologies in
February 2004. The latest version is OWL 2 which provides more modeling
primitives, greater cardinality and extended data type and annotation support than the
original language specification. There are three sub-species of OWL called OWL Lite,
OWL DL and OWL Full; each with increasing expressive power. OWL DL is
designed to be classified using a Description Logic reasoner to automatically check for
inconsistencies and compute an inferred hierarchy. While OWL DL is a natural
framework for representing facts and reasoning about facts, like other forms of
deductive logic [
Modelling judicial reasoning with a Bayesian network addresses the monotonicity
problem by allowing facts to be assertible and retractrible based on what is known
about a problem. Bayesian belief networks are graphical tools for specifying
probability distributions. They rely on the basic insight that independence forms a
significant aspect of beliefs that can be elicited relatively easily using the language of
directed acyclic graphs (DAGs). Nodes in a DAG represent propositional variables
and edges of the nodes represent direct causal influences among these variables [
Owners Corporation disputes that can’t be settled through negotiation are often
referred to the Victorian Civil and Administrative Tribunal (VCAT). Under Section
162 of the Victorian Owners Corporation Act (2006), VCAT may hear and determine
a dispute or other matter arising under this act or the regulations or the rules of
an owners corporation that affects an owners corporation including a dispute or matter
relating to:
a) the operation of an owners corporation ; or
b) an alleged breach by a lot owner or an occupier of a lot of an obligation
imposed on that person by this Act or the regulations or the rules of
the owners corporation ; or
c) the exercise of a function by a manager in respect of the owners corporation.
It is interesting to observe how the reported cases have managed this schema. The
cases available through the Australasian Legal Information Institute (AustLII)2
provides an overview of the most important and frequent matters coming before
VCAT and the Supreme Court3. An analysis of these cases indicates at least twelve
discrete areas of decision making or issues have emerged as follows:
1) Applications for Unpaid Fees
2) Conduct of Litigation
3) Vexatious and Frivolous Claims
4) Legal and Other Representation
5) Substituted Service of Proceedings
6) Costs
7) Joinder of Parties
8) Overturning Majority Decisions of an OC
9) Appointment and Termination of Managers
10) Issues with Common Property
11) Lot Liability
12) Licenses and Easements
For VCAT there is clearly a two step procedure. First is to determine that there is a
“dispute” within the meaning of section 162. If there is such a dispute then section
165 provides that the decision be guided by the principle of “fairness” under which a
number of further factors or considerations apply. A hierarchy of factors can thus be
discerned which could be defined as a “decision or argument tree” for the guidance of
the Tribunal. In this sense the plan of the Act provides a decision tree that could be
represented as follows in Table 1. A more detailed discussion of the Victorian Owners
Corporation Act (2006) and the role of VCAT in determining OC rulings can be
found in [
3 Available at http://www.austlii.edu.au/ (at this time approximately 85 in number). 6
For the purposes of this paper the issue of what circumstances VCAT would overturn a decision of the OC (number 8 in the list above) is examined. In particular, we are interested in how the decision outcomes are arrived at so as to guide potential disputants in decision making. This category of dispute provides a good background against which to examine how the Tribunal is interpreting and applying the provisions of the Act and in particular the factors outlined in the argument tree in Table 1. 6.1
An OWL Ontology for Legal Reasoning Our domain expert4 has modeled judicial reasoning for owners corporations cases heard by VCAT using an OWL ontology to capture the discrete areas of decision making and factors used in legal arguments identified in the previous section. The ontology which is shown in Figure 1 was created using the Protégé ontology editing and acquisition tool. We used the recently released version 4.1 Beta of Protégé which supports OWL 2. 4 Co-author Peter Condliffe is a Nationally Accredited Mediator and Advanced Mediator at the Victorian Bar and LEADR.
To create a case base, outcomes of all past cases were modeled as Defined Classes. Facts of past cases are represented as Necessary and Sufficient class restrictions. A Necessary data property restriction “hasOutcome” is used to instantiate instances of this class with the string value “Allow time to remedy”. Figure 2 shows the defined OWL class “Allow time to Remedy”. The axiom below which forms part of the Necessary and Sufficient conditions is called a closure axiom: (hasFactor only (IntentToRectifyBreach or IsBreachOfLaw or IsGoodFaith or NegativeImpact or NoDescrimination)) Facts of a query must precisely match the facts of the closure axiom for the query to return the outcome. The reason for using a closure axiom is to prevent an outcome being incorrectly returned when additional facts may have invalidated the result. Figure 3 shows the creation of a query class. A query is created as a Primitive class meaning facts are entered as Necessary class restrictions.
By running the reasoner and classifying the ontology to create an inferred hierarchy
Query 1 below is now reclassified under the outcome class “Allow time to Remedy”.
The Boswell V Forbes case describe in [
Asserted Facts There was discrimination against the complainant Overturning the decision would impact lot owners as a whole There was no breach of law.
Inferred Outcome In this example the DAG inferred that the resolution should not be overturned. Even though there was discrimination against the complainant, the fact that overturning the decision would impact on lot owner as whole combined with the fact that there was no breach of law tip the balance of probabilities in favor of not overturning the resolution. The inferred outcome can now be created as a Defined ontology sub-class of “Outcomes” in the OWL ontology by inserting the following code into the OWL file using string manipulation and a standard Java “println” command: <owl:Class rdf:ID="NotOverturn"> <rdfs:subClassOf rdf:resource="#OverturnOCRsolutionOutcome"/> <owl:equivalentClass> <owl:Class> <owl:intersectionOf rdf:parseType="Collection"> <owl:Restriction> <owl:allValuesFrom> <owl:Class> <owl:unionOf rdf:parseType="Collection"> <owl:Class rdf:about="#NoBreachOfLaw"/> <owl:Class rdf:about="#IsDescrimination"/> <owl:Class rdf:about="#NegativeImpact"/> </owl:unionOf> </owl:Class> Having inserted the above text into OWL ontology the outcome can now be processed as a Defined class in the same way as the “Allow time to Remedy” class in Figure 2.
With the rapid of growth of owners corporations in Victoria, Australia over the last
thirty years, conflict and its management has become an essential element of concern.
Current legal remedies, however, are widely seen as inadequate. Our research aims to
assist with better management of these conflicts by providing a negotiation decision
support guide for property owner disputes that mirrors judicial reasoning practices so
that disputants can negotiate more deliberatively before proceeding to litigation. 7 led
to the development of the OWL ontology and Bayesian Belief network to be used as a
decision support guide for OC cases. Preliminary evaluations have shown the OWL
ontology to be capable of precisely replicating the outcomes of past cases when the
exact same facts of the real case are entered. Testing with hypothetical cases has also
satisfied our domain expert that inferred outcomes obtained from the Bayesian
Network are consistent with logical judicial reasoning. The next phase of the research
will be to test the robustness of the conclusions drawn using a more formal technique
called sensitivity analysis [
.