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A Framework for Ontology-Based Knowledge Management System Jiangning Wu Dalian University of Technology, China. Introduction Background Problems Solution Focus Contributions. Research Center of Knowledge Science & Technology, DUT. Introduction.
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A Framework for Ontology-Based Knowledge Management System Jiangning Wu Dalian University of Technology, China
Introduction Background Problems Solution Focus Contributions Research Center of Knowledge Science & Technology, DUT Introduction
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. Research Center of Knowledge Science & Technology, DUT Background
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. Research Center of Knowledge Science & Technology, DUT Problems
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. Research Center of Knowledge Science & Technology, DUT Solution
Research Center of Knowledge Science & Technology, DUT Focus • 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.
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. Research Center of Knowledge Science & Technology, DUT Contributions
To calculate the similarity between documents, we propose an integrated method based on node-based method and edge-based method to solve this problem. Research Center of Knowledge Science & Technology, DUT Contributions
Ontology in Knowledge Representation Ontology in General T.R. Gruber Why Ontology Our Ontology Research Center of Knowledge Science & Technology, DUT Ontology in KR
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. Research Center of Knowledge Science & Technology, DUT Ontology
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. Research Center of Knowledge Science & Technology, DUT Ontology
T.R. Gruber claimed: An ontologyis 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. Research Center of Knowledge Science & Technology, DUT T.R Gruber
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 Research Center of Knowledge Science & Technology, DUT Ontology
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 Research Center of Knowledge Science & Technology, DUT Why Ontology
Research Center of Knowledge Science & Technology, DUT Why Ontology • 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)
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. Research Center of Knowledge Science & Technology, DUT Our Ontology
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. Research Center of Knowledge Science & Technology, DUT Our Ontology
Research Center of Knowledge Science & Technology, DUT Interface
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. Research Center of Knowledge Science & Technology, DUT Our Ontology
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. Research Center of Knowledge Science & Technology, DUT Relationships
Procedures in the Development of the Chinese Ontology Our Ontology
Matching Method Node-based Method Edge-based Method Shortcomings Integrated Method Research Center of Knowledge Science & Technology, DUT Matching Method
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. Research Center of Knowledge Science & Technology, DUT Considerations
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. Research Center of Knowledge Science & Technology, DUT Node-based Method
The similarity of two concepts c1 and c2 is Research Center of Knowledge Science & Technology, DUT Node-based Method Considering many inherited concepts may have more than one senses, the above formula is modified as
Leacock and Chodorow summed up the shortest path length and converted this statistical distance to the similarity measure. Research Center of Knowledge Science & Technology, DUT Edge-based Method
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. Research Center of Knowledge Science & Technology, DUT Shortcomings
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. Research Center of Knowledge Science & Technology, DUT Shortcomings
The node-based method does not concern the distance between concepts. From the four-hierarchy concept tree, we can see thatif 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) Research Center of Knowledge Science & Technology, DUT Shortcomings
However, it is obvious to see that concepts C21 and C31 are more similar since C31 is the direct inheritor of C21. Research Center of Knowledge Science & Technology, DUT Shortcomings
Research Center of Knowledge Science & Technology, DUT Shortcomings
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. Research Center of Knowledge Science & Technology, DUT Shortcomings
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. Research Center of Knowledge Science & Technology, DUT Shortcomings
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. Research Center of Knowledge Science & Technology, DUT Integrated Method
The similarity between cis and cjt Research Center of Knowledge Science & Technology, DUT Integrated Method • The modified similarity
The similarity between two documents Research Center of Knowledge Science & Technology, DUT Integrated Method
Ontologies Building Documents Formalization Similarity Calculation User Interface. Research Center of Knowledge Science & Technology, DUT Framework
Research Center of Knowledge Science & Technology, DUT Framework
Two measures to verify our ontology-based KMS Research Center of Knowledge Science & Technology, DUT Evaluation
Precision Research Center of Knowledge Science & Technology, DUT Evaluation
Recall Research Center of Knowledge Science & Technology, DUT Evaluation
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. Research Center of Knowledge Science & Technology, DUT Conclusions
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. Research Center of Knowledge Science & Technology, DUT Conclusions
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. Research Center of Knowledge Science & Technology, DUT Future Works
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. Research Center of Knowledge Science & Technology, DUT Future Works
THANKS Research Center of Knowledge Science & Technology, DUT