The document outlines an ontology-based knowledge management system. It discusses how traditionally keyword systems cannot understand query meanings, while ontology systems provide a content-oriented approach. It then defines knowledge management and knowledge management systems, and how ontologies can classify knowledge and define vocabularies to facilitate searching, sharing, and integrating related resources. Finally, it discusses using ontologies and information extraction to extract concepts from natural language texts.

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2. Presentation Outlines
 Introduction
 Knowledge Management
 Knowledge Management System
 Ontology Based Knowledge Management System
 Concept Extraction Process From Natural Text Formats
 Application of Ontology Based Knowledge Management System
 Conclusion
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3. Introduction
 Now days in many organizations knowledge is considered as
the most important asset that enables sustainable competitive
advantage in very dynamic and competitive markets.
 The development of KM and KMS becomes an important issue.
 Traditionally, KMS uses keyword based system to discover,
retrieve and share useful knowledge from the system.
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4. Cont.
 Since keyword based system can not understand the meaning of
data users are stressed to find related terms to their query terms.
 Due to the fact that the emerging of new ontology based
knowledge management system is occurred.
 It is a content oriented system which helps users to get related
terms according their submitted query.
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5. Knowledge Management
 Knowledge Management (KM) is a process that deals with the
development, storage, retrieval, and dissemination of
information and expertise within an organization to support and
improve its business performance.
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6. Cont.
 Knowledge Management (KM) consists of a technique that uses
Technological tools for the management of information.
 its goal is:-
 To improve the efficiency of work teams;
 To studies methods for making knowledge explicit, and
 Sharing of informative knowledge resources.
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7. Knowledge Management System
 knowledge management system, supporting the creation and storage
of knowledge,
 The goal of KMS is to provide the right knowledge to the right
people at the right time and right format.
 KMS contain both explicit and tacit knowledge.
 Tacit knowledge is more difficult to manage than explicit
knowledge and can be expressed as "we know more than we can
tell”.
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8. Cont.
 KMS makes tacit knowledge better capitalize and disseminate
using different technologies.
 Those are:
 Document based technologies
 Ontology/Taxonomy based technologies
 Artificial Intelligence based technologies
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9. Ontology Based Knowledge Management System
 Ontology is an explicit and formal specification of a
conceptualization.
 That is a formal description of the relevant concepts and
relationships in an area of interest, simplifying and abstracting
the view of the world for some purpose.
 It gives an understandable meaning both to humans and to
software agents.
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10. Cont.
 In KMS, ontology can be regarded as the classification of
knowledge.
 Ontology defines shared vocabulary for facilitating knowledge
communication, storing, searching and sharing in knowledge
management systems.
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11. Cont.
 Ontology-based knowledge management system is better at
supporting:-
 The integration of related resources,
 Searching the accurate knowledge quickly, and
 Avoiding a large number of irrelevant knowledge.
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12. Ontology Development Tools
 Ontologies are built by highly trained knowledge engineers.
Some of the developmental tools for ontology are:
 Protégé 2000
 Onto Lingua
 Onto Edit
 Web Onto
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13. Cont.
Figure: General architecture of Ontology based knowledge management system
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14. Unique Features of Ontology based KMS
 Complete relation ship between the concepts, cases and facts is
stated well.
 Meaning of the facts is taking in to account to relate facts each
other
 Knowledge is represented in a formal language to make good
communication way and understanding among human and
machine.
 Logic-based inference rules - correct and provable results
achieved. 14

15. Concept Extraction From Natural Texts
 Concepts are sequences of words that represent real or
imaginary entities or ideas that users are interested in from the
large data source.
 Ontology based knowledge management system is used to
extract concepts and realizations from the natural language or
plain text using different techniques.
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16. Cont.
 Such as:-
•Information Retrieval system
•Information Extraction
•Machine Learning
•Data Mining
 Ontologies provide a shared and common understanding of a domain
that can be communicated across people and applications.
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17. Cont.
 Information Extraction is the process of automatically obtaining
knowledge from plain text.
 Because of the ambiguity of written natural language,
Information Extraction is a difficult task.
 Ontology-based Information Extraction (OBIE) reduces this
complexity by including contextual information in the form of a
domain ontology.
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18. Cont.
Figure: Information Extraction process From Natural Textual document Using
OBIE.
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19. APPLICATION
 OKMS is applicable In Education, Business, Agriculture,
Medicine and Library.
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20. Advantages
 Facilitates search, exchange and integration of knowledge.
 Satisfy the users need by recommend them related items.
 Improves human to computer interaction.
 Helps to extract concepts through large datasets
 It supports business centers for competitive advantage in very
dynamic and competitive markets.
 Allow machine able to understand semantics of data.
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21. Cont.
 Ontologies make domain knowledge reusable.
 Ontologies enable the interoperability among models or specific
domain vocabularies.
 Ontologies allow and simplify the communication among
humans, computational systems, and between humans and
systems.
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22. Cont.
 Ontologies have the expressive power for acquiring context
from diverse and heterogeneous source.
 It is possible to apply reasoning and inference mechanisms by
means of explicit representation of semantics.
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23. Disadvantages
 OBKMS systems have not been widely adopted because of the
 Its difficulties in deployment and maintenance
 Hard to implement and construct
 Space, time and money consuming.
 They have no discussion hub rather than they are suitable for
searching.
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24. Conclusion
 OBKMS is useful to satisfy customers need by developing
semantic webs, Recommender systems and IR systems in
different areas such as library and business centers.
 Since our world is covered in dynamic and variety of word
sense and Knowledge fusion, it is difficult to construct ontology
based knowledge system easily.
 Knowledge representation is not a simple way process.
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25. Recommendation
 While we are living in knowledge fusion world it is good to
implement multi-lingual ontology based knowledge
management system.
 According to our country it is possible to implement and
develop ontology based knowledge management system for
 Ethiopian Higher institution Library system
 Ethiopian Health System centers.
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