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project teaming, his study refers to the discipline of
knowledge engineering (KE) proposed by Guarino
(1995). Typical KE includes expertise gathering,
knowledge model building, and knowledge
representation. Detailed steps are described as the
followings:
Expertise Gathering: Expertise gathering is the
focus of identifying critical elements of project
teaming. Observation of project teaming events
identifies three primary subjects including Project,
Worker and Teaming factors. Further expertise
gathering are also implemented, including
elicitation, analysis, and transformation are
implemented.
Property elicitation. Over 100 properties are
gathered during this stage. For example, the
collected properties of projects are basic
features such as project’s budget and skill
needs.
Analysis. A total of 35 properties are identified.
New or subordinate subjects are generated by
either separating existing subjects or
assembling relevant subjects. For example, the
subjects Project_Type, Title, Skills and
Personality are subordinate to the
Teaming_Factors subject.
Transformation. This step transforms subjects
and their dependent properties into an
ontological representation. The representation
is formed by a formatted expression written as
{Concept: Property list}. Examples are
presented below.
{Project: Project_Description, PM, Budget,
Number_of_HR, Skill_Need, PM_Skill_Need,
Length_of_Time, has_Type, Qualified_Basic_Worker,
Watch_List, … }
{Worker: Age, Gender, Seniority, Salary, has_Title,
has_Skill, member_of, Hostile_to,
has_Experience, …}
Building Knowledge Model: A knowledge model
is based on an abstract view of the task domain, and
can be used as an intermediary between the real
world and information systems. Two type of
relations including “is-a” and “has-a” are developed.
The “is-a” denotes an inheritance relationship
between two concepts. For example, the
Teaming_factors concept has sub-concepts such as
Title, Skills and Personality. The “has-a” denotes
the “part-of” relation between two concepts. These
properties stipulate a schema for describing the
concepts. Users can employ these properties to
contribute their factual knowledge to the knowledge
base or to obtain implicit knowledge via inference
mechanisms.
Furthermore, two types of property’s content
including “Asserted property” and “Inferred
property” need to be defined during model building.
The asserted properties provide the basis of
inference engine to deduce new knowledge. On the
other hand, the content of inferred property is
implicit, but can be obtained by inferring factual
knowledge via a reasoned. The inferred property
plays a critical role for rule-based reasoning.
Knowledge Representation: This study employs
Web Ontology Language (OWL) as the notation and
formalism for representing the knowledge to be
stored in ontology. After constructing the team
composition model, OWL is utilized for knowledge
representation. OWL is highly appropriate for
representing structured knowledge using classes and
properties organized in taxonomies.
3 CREATING RELATIONSHIPS
USING SEMANTIC RULES
Horrocks and Patel-Schneider (2004) have reported
several limitations and issues of OWL in syntax and
computation, particularly in relationships between
roles chains using rules, causing indeductibility,
logical undecidability, by embedding the word
problem in inferences. The rules apply the syntax
“Antecedent ➔ Consequent”. Both antecedent and
consequent are conjunctions of atoms of the form
atom
1
⋀ ..⋀ atom
n
, where a variable is indicated by
a question mark (e.g., ?x). The semantic rules are
used to extend the power of the ontological approach
to identify semantic relationships between instances.
This study utilizes the Project_Type concept to
manage characteristics of typical historical projects
as best practices. Several properties used in rules
development are detailed below. The PT_Skill_Need
and PT_PM_Skill_Need properties are used to
indicate the skills needed by workers and project
managers, respectively. Furthermore, the
PT_Personality_Need property describes preferred
personality types for performing the project. These
properties can be used to develop rules to connect
other concepts such as Worker to obtain candidate
members for a project team. The following five rules
are examples developed in this study.
Rule-1 is used to identify qualified team
members with reference to best practice. Rule-2
helps identify candidate project manager(s) based on
the qualified workers with reference to best
practices. Rule-3 is applied to group senior workers
as candidate team members. Rule-4 adds the
PT_Personality_Need property to deduce whether
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