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A Framework for Ontology-Based Knowledge Management System Jiangning Wu

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

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

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

  3. 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

  4. 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

  5. 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

  6. 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.

  7. 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

  8. 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

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

  10. 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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)

  16. 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

  17. 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

  18. Research Center of Knowledge Science & Technology, DUT Interface

  19. 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

  20. 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

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

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

  23. 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

  24. 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

  25. 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

  26. 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

  27. 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

  28. 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

  29. 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

  30. 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

  31. Research Center of Knowledge Science & Technology, DUT Shortcomings

  32. 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

  33. 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

  34. 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

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

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

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

  38. Research Center of Knowledge Science & Technology, DUT Framework

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

  40. Precision Research Center of Knowledge Science & Technology, DUT Evaluation

  41. Recall Research Center of Knowledge Science & Technology, DUT Evaluation

  42. 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

  43. 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

  44. 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

  45. 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

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

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