1. A Framework for Ontology-Based Knowledge Management System
Jiangning Wu
Dalian University of Technology, China

2. Research Center of Knowledge Science & Technology, DUT
Introduction
Research Center of Knowledge Science & Technology, DUT
Introduction
Background
Problems
Solution
Focus
Contributions

3. Research Center of Knowledge Science & Technology, DUT
Background
Research Center of Knowledge Science & Technology, DUT
The goal of a general KMS is to provide the right knowledge to the right people at the right time and in the right format.
Through KMSs, users can access and utilize the rich sources of data, information and knowledge stored in different forms.

4. Research Center of Knowledge Science & Technology, DUT
Problems
Research Center of Knowledge Science & Technology, DUT
Traditional KMSs are based on the existing data repositories and users’ needs.
For knowledge discovering, users submit queries to the system and receive knowledge by keyword match.
But keyword-based systems cannot understand the meaning of data. They are inflexible and stifle for knowledge creation.

5. Research Center of Knowledge Science & Technology, DUT
Solution
Research Center of Knowledge Science & Technology, DUT
The emerging ontology-based KMSs can find the content-oriented knowledge that people really want.
The domain ontology is powerful in knowledge representation and associated inference.

6. Research Center of Knowledge Science & Technology, DUT
Focus
Research Center of Knowledge Science & Technology, DUT
We mainly focus on performing the activity for projects and domain experts matching.
In project management, it is not easy to choose an appropriate domain expert for a certain project if experts’ research areas and the contents of the projects are not understood very well.

7. Research Center of Knowledge Science & Technology, DUT
Contributions
Research Center of Knowledge Science & Technology, DUT
Our contributions are describing experts’ research areas and the contents of the projects by separated ontologies based on the same standard subject category of China.
So the matching problem is transformed into calculating the semantic similarities between ontologies.

8. Research Center of Knowledge Science & Technology, DUT
Contributions
Research Center of Knowledge Science & Technology, DUT
To calculate the similarity between documents, we propose an integrated method based on node-based method and edge-based method to solve this problem.

9. Research Center of Knowledge Science & Technology, DUT
Ontology in KR
Research Center of Knowledge Science & Technology, DUT
Ontology in Knowledge Representation
Ontology in General
T.R. Gruber
Why Ontology
Our Ontology

10. Research Center of Knowledge Science & Technology, DUT
Ontology
Research Center of Knowledge Science & Technology, DUT
Research on knowledge representation has been a focus of AI and IS disciplines for a number of years.
Much of contemporary research extends the seminal work within AI discipline, of which research in ontology has been one of the beneficiaries.

11. Research Center of Knowledge Science & Technology, DUT
Ontology
Research Center of Knowledge Science & Technology, DUT
Research in computational ontology has traditionally sought to develop structure for the purpose of knowledge subsumption.
The goal of such research aims to develop generic, reusable representations of domain ontology.

12. Research Center of Knowledge Science & Technology, DUT
T.R Gruber
Research Center of Knowledge Science & Technology, DUT
T.R. Gruber claimed: An ontology is an explicit specification of a conceptualization. The term is borrowed from philosophy, where an ontology is a systematic account of existence.
For knowledge-based systems, what “exists” is exactly that which can be represented.

13. Research Center of Knowledge Science & Technology, DUT
Ontology
Research Center of Knowledge Science & Technology, DUT
An ontology in short is an explicit description of a domain:
concepts
properties and attributes of concepts
constraints on properties and attributes
Individuals (often, but not always)
An ontology defines
a common vocabulary
a shared understanding

14. Research Center of Knowledge Science & Technology, DUT
Why Ontology
Research Center of Knowledge Science & Technology, DUT
To share common understanding of the structure of information
among people
among software agents
To enable reuse of domain knowledge
to avoid “re-inventing the wheel”
to introduce standards to allow interoperability

15. Research Center of Knowledge Science & Technology, DUT
Why Ontology
Research Center of Knowledge Science & Technology, DUT
To make domain assumptions explicit
easier to change domain assumptions (consider a genetics knowledge base)
easier to understand and update legacy data
To separate domain knowledge from the operational knowledge
re-use domain and operational knowledge separately (e.g., configuration based on constraints)

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Our Ontology
Research Center of Knowledge Science & Technology, DUT
The ontology is a collection of concepts and their relationships, and serves as a conceptualized vocabulary to describe an application domain.
In our study, it is created by means of Protege, which is developed by Stanford University.

17. Research Center of Knowledge Science & Technology, DUT
Our Ontology
Research Center of Knowledge Science & Technology, DUT
The initial concepts in our ontology are broadly extracted from the standard subject category of China.
To make the selected concepts more suitable for our concerned projects and domain experts, a tool called Concept Filler is developed, which is simply an interface to help domain experts assign proper concepts and weights manually.

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Interface
Research Center of Knowledge Science & Technology, DUT

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Our Ontology
Research Center of Knowledge Science & Technology, DUT
When specifying the concept, the corresponding weight value ranging from 0 to 1 is also assigned to itself aiming to distinguish its importance.
The relationships in an ontology are explicitly named which can reflect the context of the domain knowledge.

20. Research Center of Knowledge Science & Technology, DUT
Relationships
Research Center of Knowledge Science & Technology, DUT
Many types of relationships can be found in ontology construction as we have known, such as IS-A relation, Kind-of relation, Part-of relation, Substance-of relation, and so on.
Since IS-A (hyponym / hypernym) relation is the most common concern in ontology presentation, only this kind of relation is therefore introduced in our research for simplification.

21. Our Ontology
Procedures
in the Development
of the Chinese Ontology

22. Research Center of Knowledge Science & Technology, DUT
Matching Method
Research Center of Knowledge Science & Technology, DUT
Matching Method
Node-based Method
Edge-based Method
Shortcomings
Integrated Method

23. Research Center of Knowledge Science & Technology, DUT
Considerations
Research Center of Knowledge Science & Technology, DUT
Calculating the similarity between concepts based on the complex relationships is a challenging work.
Unfortunately no method can deal with the above problem effectively up to now.
Considering some similarity calculation methods have been developed based on the simplest relation - IS-A relation, only this kind of relation is retained in our study.

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Node-based Method
Research Center of Knowledge Science & Technology, DUT
Resnik used information content to measure the similarity.
His point is that the more information content two concepts share, the more similarity two concepts have.

25. Research Center of Knowledge Science & Technology, DUT
Node-based Method
Research Center of Knowledge Science & Technology, DUT
The similarity of two concepts c1 and c2 is
Considering many inherited concepts may have more than one senses, the above formula is modified as

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Edge-based Method
Research Center of Knowledge Science & Technology, DUT
Leacock and Chodorow summed up the shortest path length and converted this statistical distance to the similarity measure.

27. Research Center of Knowledge Science & Technology, DUT
Shortcomings
Research Center of Knowledge Science & Technology, DUT
Both node-based and edge-based methods only simply consider two concepts in the same concept tree without expanding to two lists of concepts in different concept trees.
However the fact is when we describe different documents in the same domain using ontology structures, homogeneous but heteromorphic concept trees are often formed.

28. Research Center of Knowledge Science & Technology, DUT
Shortcomings
Research Center of Knowledge Science & Technology, DUT
The matching problem to be solved here is calculating the similarity between two different concept trees, not between two concepts in the same tree.
So we have to develop a new method that can calculate the similarities between two lists of concepts in different trees, by which the quantified similarity value can show how similar the documents are.

29. Research Center of Knowledge Science & Technology, DUT
Shortcomings
Research Center of Knowledge Science & Technology, DUT
The node-based method does not concern the distance between concepts.
From the four-hierarchy concept tree, we can see that if concepts C21, C31 and C36 have the same sense and the equal frequency, we may get the following result according to the node-based method
sim(C21, C31) = sim(C21, C36)

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Shortcomings
Research Center of Knowledge Science & Technology, DUT
However, it is obvious to see that concepts C21 and C31 are more similar since C31 is the direct inheritor of C21.

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Shortcomings
Research Center of Knowledge Science & Technology, DUT

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Shortcomings
Research Center of Knowledge Science & Technology, DUT
In contrast to the node-based method, the edge-based method only considers the relationships between concepts and ignores the weights of concepts.
Both concepts C31 and C32 respectively have only one edge with C21. According to the edge-base method, the same similarity value can be obtained.

33. Research Center of Knowledge Science & Technology, DUT
Shortcomings
Research Center of Knowledge Science & Technology, DUT
But, if C31 has bigger weight than C32, C31 is considered to be more important and the corresponding similarity value between C31 and C21 should be greater.

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Integrated Method
Research Center of Knowledge Science & Technology, DUT
Before conducting the proposed method, the documents related to projects and domain experts should be formalized first that results in two vectors containing the concepts with their frequencies.

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Integrated Method
Research Center of Knowledge Science & Technology, DUT
The similarity between cis and cjt
The modified similarity

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Integrated Method
Research Center of Knowledge Science & Technology, DUT
The similarity between two documents

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Framework
Research Center of Knowledge Science & Technology, DUT
Ontologies Building
Documents Formalization
Similarity Calculation
User Interface.

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Framework
Research Center of Knowledge Science & Technology, DUT

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Evaluation
Research Center of Knowledge Science & Technology, DUT
Two measures to verify our ontology-based KMS

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Evaluation
Research Center of Knowledge Science & Technology, DUT
Precision

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Evaluation
Research Center of Knowledge Science & Technology, DUT
Recall

42. Research Center of Knowledge Science & Technology, DUT
Conclusions
Research Center of Knowledge Science & Technology, DUT
An ontology-based method to match projects and domain experts is presented.
The prototype system we developed contains four modules: Ontology building, Document formalization, Similarity calculation and User interface.

43. Research Center of Knowledge Science & Technology, DUT
Conclusions
Research Center of Knowledge Science & Technology, DUT
We discuss node-based and edge-based approaches to computing the semantic similarity, and propose an integrated approach to calculating the semantic similarity between two documents.
The experimental results show that our ontology-based KMS performing the activity for projects and domain experts matching can reach better recall and precision.

44. Research Center of Knowledge Science & Technology, DUT
Future Works
Research Center of Knowledge Science & Technology, DUT
As mentioned previously, only the simplest relation “IS-A relation” is considered in our study.
When dealing with the more complex ontology whose concepts are restricted by logic or axiom, our method is not powerful enough to describe the real semantic meaning by merely considering the hierarchical structure.

45. Research Center of Knowledge Science & Technology, DUT
Future Works
Research Center of Knowledge Science & Technology, DUT
So the future work will be focused on the other kinds of relations that are used in ontology construction.
In other words, it will be an exciting and challenging work for us to compute the semantic similarity upon various relations in the future.

46. Research Center of Knowledge Science & Technology, DUT
THANKS