**2. Related work**

The term "Semantic Web" was coined by Tim Berners-Lee in 1998 and defined as not a separate Web but an extension of the current one, in which information is given welldefined meaning, better enabling computers and people to work in cooperation [2]. The layer cake framework of the semantic web implicates that the development of Semantic Web technologies proceeds in steps and each step building a layer on the top of another. It

The Semantic Web-Based Collaborative Knowledge Management 89

The machine readability of knowledge atom is a necessary condition for that of web knowledge and not the sufficient condition of it. There is another necessary condition which is machine readability of relations between knowledge atoms. Semantic web makes it possible to represent the knowledge relations in machine readable syntax. For example, a knowledge atom 20101881 is the teacher of another knowledge atom 20100808, the machine readable relation between them can be shown as follows in XML-based RDF(S) syntax.

Knowledge Life Cycle (KLC) is one of the most important concepts in New Generation Knowledge Management (NGKM). The NGKM for the first time allows for the production of new knowledge in knowledge management, while the First Generation Knowledge Management concerns itself mainly with the distribution, sharing and use of existing knowledge. Existing knowledge can be categorized into knowledge in human brain and knowledge stored on the Web. In this paper, the topic is just limited to the existing

The production of new knowledge form existing knowledge mainly relies on the technologies of Artificial Intelligence (AI) to date. However, the AI hasn't made much

<?xml version="1.0" encoding="UTF-8"?>

<rdf:Description rdf:about="20100808"> <rdf:type rdf:resource= "&my;Stuedent"/>

<?xml version="1.0" encoding="UTF-8"?>

<rdf:Description rdf:about="20101881"> <rdf:type rdf:resource= "&my;Teacher"/>

<my:isSupervisorOf rdf:resource="20100808"/>

**3. Knowledge Life Cycle on the semantic web** 

knowledge, namely knowledge stored on the Web.

progress with knowledge production yet.

<my:name>Wang</my:name>

</rdf:Description> </rdf:RDF>

<my:name>Zhang</my:name>

</rdf:Description> </rdf:RDF>

<rdf:RDF xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:my="http://www.chaolemen.com/University#" >

**2.3 Machine readability of relation between knowledge atoms** 

<rdf:RDF xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:my="http://www.chaolemen.com/University#" >

mainly includes seven different layers, namely Unicode and namespace, XML, RDF(S), ontology, logic, proof and trust. Two principles, those are downward compatibility and upward partial understanding, were usually recommended to build adjacent layers [3].

There has been noticeable improvement in the studies of semantic web technologies over the past ten years. Some of them, especially the technologies at lower levels of the layered cake, such as XML, RDF(S), OWL and SPARQL, have been standardized by W3C knowledge representation, knowledge searching, knowledge mining, semantic web services, semantic grid, application integration and social network analysis are becoming research hotspots in knowledge processing. Project10X examined over more than 270 companies providing semantic products and services and published a semantic web wave report[4].The ReadWriteWeb picked out top ten semantic web products of 2009[5], including Google Search Options , Rich Snippets, Open Calais 4.0,BBC's Semantic Music Project, Freebase, and Data.gov.

It is one of revolutionary innovations in the semantic web that human-centric knowledge representation, which has been widely used in traditional web, is substituted for machine-centric knowledge representation. Therefore, knowledge on the semantic web is machine-readable. Machine readability of semantic web knowledge representation is implemented by:

#### **2.1 Knowledge atom**

The semantic web makes it possible to process knowledge by a smallest atomic unit of it. There are two knowledge representation technologies are commonly used on the semantic web, one is XML-based RDF(S) and the other is OWL Language. However, the core ideas behind them are similar: describing a knowledge atom as an object-attribute-vale triple and converting non atomic knowledge representation units into knowledge atoms by Reification Mechanism. Take a knowledge atom that represents 20100808's name is Zhang as example, its XML based RDF syntax knowledge representation is the following:

```
<?xml version="1.0" encoding="UTF-8"?> 
<rdf:RDF xmlns:owl=http://www.w3.org/2002/07/owl# 
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" 
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" 
xmlns:my="http://www.chaolemen.com/ University#" > 
<rdf:Description rdf:about="20100808"> 
 <my:name>Zhang</my:name> 
</rdf:Description> 
</rdf:RDF>
```
#### **2.2 Machine readability of knowledge atom**

The meaning of knowledge atom is declared in domain ontology which is written by XMLbased RDF schema or OWL language. Therefore, the semantic web allocates a unique specific meaning to each of knowledge atoms in a semantic web document and can avoid the two wellknown semantic problems: homonymy and polysemy. Take knowledge atom 20100808 as example, the meaning that 20100808 represents a student could be described as follows:

mainly includes seven different layers, namely Unicode and namespace, XML, RDF(S), ontology, logic, proof and trust. Two principles, those are downward compatibility and upward partial understanding, were usually recommended to build adjacent layers [3]. There has been noticeable improvement in the studies of semantic web technologies over the past ten years. Some of them, especially the technologies at lower levels of the layered cake, such as XML, RDF(S), OWL and SPARQL, have been standardized by W3C knowledge representation, knowledge searching, knowledge mining, semantic web services, semantic grid, application integration and social network analysis are becoming research hotspots in knowledge processing. Project10X examined over more than 270 companies providing semantic products and services and published a semantic web wave report[4].The ReadWriteWeb picked out top ten semantic web products of 2009[5], including Google Search Options , Rich Snippets, Open Calais 4.0,BBC's Semantic Music

It is one of revolutionary innovations in the semantic web that human-centric knowledge representation, which has been widely used in traditional web, is substituted for machine-centric knowledge representation. Therefore, knowledge on the semantic web is machine-readable. Machine readability of semantic web knowledge representation is

The semantic web makes it possible to process knowledge by a smallest atomic unit of it. There are two knowledge representation technologies are commonly used on the semantic web, one is XML-based RDF(S) and the other is OWL Language. However, the core ideas behind them are similar: describing a knowledge atom as an object-attribute-vale triple and converting non atomic knowledge representation units into knowledge atoms by Reification Mechanism. Take a knowledge atom that represents 20100808's name is Zhang as example,

The meaning of knowledge atom is declared in domain ontology which is written by XMLbased RDF schema or OWL language. Therefore, the semantic web allocates a unique specific meaning to each of knowledge atoms in a semantic web document and can avoid the two wellknown semantic problems: homonymy and polysemy. Take knowledge atom 20100808 as example, the meaning that 20100808 represents a student could be described as follows:

its XML based RDF syntax knowledge representation is the following:

<rdf:RDF xmlns:owl=http://www.w3.org/2002/07/owl# xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:my="http://www.chaolemen.com/ University#" >

Project, Freebase, and Data.gov.

<?xml version="1.0" encoding="UTF-8"?>

<rdf:Description rdf:about="20100808"> <my:name>Zhang</my:name>

**2.2 Machine readability of knowledge atom** 

implemented by:

**2.1 Knowledge atom** 

</rdf:Description> </rdf:RDF>

<?xml version="1.0" encoding="UTF-8"?> <rdf:RDF xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns:my="http://www.chaolemen.com/University#" > <rdf:Description rdf:about="20100808"> <rdf:type rdf:resource= "&my;Stuedent"/> <my:name>Zhang</my:name> </rdf:Description> </rdf:RDF>

#### **2.3 Machine readability of relation between knowledge atoms**

The machine readability of knowledge atom is a necessary condition for that of web knowledge and not the sufficient condition of it. There is another necessary condition which is machine readability of relations between knowledge atoms. Semantic web makes it possible to represent the knowledge relations in machine readable syntax. For example, a knowledge atom 20101881 is the teacher of another knowledge atom 20100808, the machine readable relation between them can be shown as follows in XML-based RDF(S) syntax.

```
<?xml version="1.0" encoding="UTF-8"?> 
<rdf:RDF xmlns:owl="http://www.w3.org/2002/07/owl#" 
 xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" 
 xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" 
 xmlns:my="http://www.chaolemen.com/University#" > 
<rdf:Description rdf:about="20101881"> 
 <rdf:type rdf:resource= "&my;Teacher"/> 
 <my:name>Wang</my:name> 
 <my:isSupervisorOf rdf:resource="20100808"/> 
</rdf:Description> 
 </rdf:RDF>
```
#### **3. Knowledge Life Cycle on the semantic web**

Knowledge Life Cycle (KLC) is one of the most important concepts in New Generation Knowledge Management (NGKM). The NGKM for the first time allows for the production of new knowledge in knowledge management, while the First Generation Knowledge Management concerns itself mainly with the distribution, sharing and use of existing knowledge. Existing knowledge can be categorized into knowledge in human brain and knowledge stored on the Web. In this paper, the topic is just limited to the existing knowledge, namely knowledge stored on the Web.

The production of new knowledge form existing knowledge mainly relies on the technologies of Artificial Intelligence (AI) to date. However, the AI hasn't made much progress with knowledge production yet.

The Semantic Web-Based Collaborative Knowledge Management 91

the importance of demand side knowledge process (including the production, validation and integration of knowledge).The introduction of KLC changes the conventional ways of thinking on knowledge management into a more effective way - Next Generation

Semantic Web makes it possible for AI to manage the knowledge on the web effectively. Semantic Web is an evolving extension of the World Wide Web in which the semantics of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content [2]. Therefore, this new technology will definitely introduce a new research domain into current

Contrary to knowledge in human organizations, the knowledge on semantic web is created, processed, stored, and transferred by machine agents, not directly by human brain. Semantic knowledge management turns much attention to sharing and reusing the knowledge while organizational knowledge management places more emphasis on the continuous production of new knowledge through enhancing the conditions in which innovation and creativity naturally occur and organizational learning happen. In other words, Knowledge management on semantic web belongs to supply side knowledge management while organizational knowledge management belongs to demand side knowledge management. Currently, KLC is limited to life cycle of knowledge in human

The different stages in semantic KLC are representation, interconnection, reasoning,

organizations, not considering the knowledge management on semantic web.

Knowledge Management (NGKM).

**3.2 KLC on the semantic web** 

knowledge management theory.

Fig. 2. KLC on the Semantic Web

retrieving, validation and integration (Figure 2).

Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation[6]. Semantic web having well structured data provides a new solution for producing new knowledge based on existing knowledge.

#### **3.1 KLC in next generation knowledge management**

KLC shown as Figure 1 was developed by members at the Knowledge Management Consortium International (KMCI), especially by Joseph M. Firestone and Mark W. McElroy[7]. In that Figure, the life cycle of knowledge is a continuum regime of knowledge process and it can be divided into three fundamental phases: knowledge production, knowledge validation and knowledge integration.

Fig. 1. KLC in NGKM (Source: Mark W. McElroy, The new knowledge management: Complexity, learning, and sustainable innovation, KMCI Press, USA, 2002.)

The theoretical foundation of KLC is Complex Adaptive System Theory (CAS theory). This theory views a system as a fluidly changing collection of distributed interacting components that react to both their environments and to one another. Therefore, CAS theory makes KLC possible to study organizational knowledge management from a new perspective, in which the complexity of managing knowledge comes from not only the external environment, but also the internal adaptive components.

The most significant contribution of KLC to knowledge management is for the first time arguing that the knowledge process is one kind of natural process of living systems. The first generation knowledge management theory places too much emphasis on supply side of knowledge process (such as the sharing and use of existing knowledge), while neglecting the importance of demand side knowledge process (including the production, validation and integration of knowledge).The introduction of KLC changes the conventional ways of thinking on knowledge management into a more effective way - Next Generation Knowledge Management (NGKM).

#### **3.2 KLC on the semantic web**

90 New Research on Knowledge Management Technology

Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation[6]. Semantic web having well structured data provides a new solution for

KLC shown as Figure 1 was developed by members at the Knowledge Management Consortium International (KMCI), especially by Joseph M. Firestone and Mark W. McElroy[7]. In that Figure, the life cycle of knowledge is a continuum regime of knowledge process and it can be divided into three fundamental phases: knowledge production,

Fig. 1. KLC in NGKM (Source: Mark W. McElroy, The new knowledge management:

The theoretical foundation of KLC is Complex Adaptive System Theory (CAS theory). This theory views a system as a fluidly changing collection of distributed interacting components that react to both their environments and to one another. Therefore, CAS theory makes KLC possible to study organizational knowledge management from a new perspective, in which the complexity of managing knowledge comes from not only the external environment, but

The most significant contribution of KLC to knowledge management is for the first time arguing that the knowledge process is one kind of natural process of living systems. The first generation knowledge management theory places too much emphasis on supply side of knowledge process (such as the sharing and use of existing knowledge), while neglecting

Complexity, learning, and sustainable innovation, KMCI Press, USA, 2002.)

also the internal adaptive components.

producing new knowledge based on existing knowledge.

**3.1 KLC in next generation knowledge management** 

knowledge validation and knowledge integration.

Semantic Web makes it possible for AI to manage the knowledge on the web effectively. Semantic Web is an evolving extension of the World Wide Web in which the semantics of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content [2]. Therefore, this new technology will definitely introduce a new research domain into current knowledge management theory.

Contrary to knowledge in human organizations, the knowledge on semantic web is created, processed, stored, and transferred by machine agents, not directly by human brain. Semantic knowledge management turns much attention to sharing and reusing the knowledge while organizational knowledge management places more emphasis on the continuous production of new knowledge through enhancing the conditions in which innovation and creativity naturally occur and organizational learning happen. In other words, Knowledge management on semantic web belongs to supply side knowledge management while organizational knowledge management belongs to demand side knowledge management. Currently, KLC is limited to life cycle of knowledge in human organizations, not considering the knowledge management on semantic web.

The different stages in semantic KLC are representation, interconnection, reasoning, retrieving, validation and integration (Figure 2).

Fig. 2. KLC on the Semantic Web

The Semantic Web-Based Collaborative Knowledge Management 93

Application programming interface (API) to integrate knowledge with applications,

Technologies to integrate knowledge with business process including Individual and

Though knowledge management on semantic web is only in the initial phase, it looks quite promising. That knowledge life cycle on semantic has three features: 1) the life cycles of knowledge on semantic web is a continuums regime of knowledge process, 2) six distinct stages in semantic knowledge management are representation, interconnection, reasoning, retrieving , validation and integration, and 3) semantic knowledge management requires

**4. Modelling collabrative knowledge management on the semantic web** 

The concept of "Web 2.0" began with a conference brainstorming session between O'Reilly and MediaLive International [12]. Web 2.0 is not a new technology, but a new shift in the application model of the World Wide Web. Design principles behind web 2.0 include: The web as platform, harnessing collective intelligence, data is the next Intel inside, end of the software release cycle, lightweight programming models, software above the level of a single device and rich user experience [13].There are some typical applications of web 2.0 such as Blog,RSS,Wiki,Tag,SNS,P2P which has been widely used on existing web. The reasons why web2.0 has been successfully accepted are as follows: users create value, networks multiply effects, people build connections, companies capitalize competences, new recombines with old, and businesses incorporate

*1 The web as platform Leveraging customer-self service and algorithmic data* 

*inside Database management is a core competency of Web 2.0 companies* 

;

*are less valuable than those that are connected* 

*for "hackability" and remixability* 

*the richness of the shared data.* 

*dominance in the Web 2.0 era.* 

*treated as co-developers* 

*coupled systems*

*management to reach out to the long tail and not just the head.* 

*Network effects from user contributions are the key to market* 

*Operations must become a core competency and users must be* 

*Support lightweight programming models that allow for loosely* 

*The PC is no longer the only access device for internet applications, and applications that are limited to a single device* 

*Companies that succeed will create applications that learn from their users, using architecture of participation to build a commanding advantage not just in the software interface, but in* 

*Think syndication, not coordination*

;*Design* 

such as ADO.NET, ODBC, and JDBC.

different methodologies or technologies for different stages.[11]

Group Learning.

**4.1 Web2.0 and its implications** 

*Principles Lessons* 

*<sup>2</sup>harnessing collective intelligence* 

*<sup>3</sup>data is the next Intel* 

*<sup>4</sup>end of the software release cycle* 

*<sup>6</sup>software above the level of a single device* 

*7 rich user experience* 

Table 1. Design principles behind web 2.0

*programming models* 

*<sup>5</sup>lightweight* 

strategies [14].

(1)Knowledge representation: The main purpose of knowledge representing is changed on semantic web. Traditionally, web content is formatted for human readers rather than computer applications. As a result, the machines hardly find, organize, integrate or validate knowledge on the traditional web without man's intervention. We have been tended to believe that Artificial Intelligence is the only way to manage the web data by machines or applications. AI hasn't been, however, made much progress with data management yet and therefore many scholars of knowledge management have to grant a higher value on humancentric knowledge management instead of machine- centric knowledge management. Semantic web, for the first time, make it easier for machines to manage web knowledge because data are represented to be machine readable. The main semantic technologies to represent data semantically are Unicode, XML (Extensive markup language), RDF/RDFs (Resources Description Framework / Resources Description Framework Schema), and OWL (Web Ontology Language) [8].

(2)Knowledge interconnection: Another main purpose of semantic web is to build a web between data. Today's web is not a web of data, but a web of computers or applications. Knowledge on the current web doesn't connect with or be related to each other. Uniform(URI) and Namespace (NS) are most common used technologies to build connection between semantic knowledge.

(3)Knowledge reasoning: Semantic web's strength lies in its ability to knowledge reasoning. It is very difficult for today's web to reason knowledge because of lacking metadata and rules. Semantic web makes knowledge reasoning possible by adding semantic metadata and a rule system to semantic data. By adding new rule system to the semantic web, new knowledge can be inferred and existing knowledge can be validated. Rule system may be Monotonic or Nonmonotonic. Monotonic rule system, which is a special care of predicate logic, can be combined with semantic web by Semantic Web Rules Language (SWRL) or Description Logic Programs (DLP). No monotonic rules are useful in situations where the available information is incomplete [9]. Through RuleML, Nonmontonic rules can be represented easily and priorities to resolve some conflicts between these rules can also be added.

(4)Knowledge retrieving: Knowledge can be retrieved with high precision on semantic web. The process of semantic knowledge searching can be divided into following steps: searching for semantic web document and searching for semantic knowledge in a web document found. Intelligent agent and search engines are the most frequently used tools to search for semantic web documents. After the document is located, addressing and querying languages, such as XQL, XQuery, X-Path, RQL and SPARQL, can be used to further search for semantic web parts.

(5)Knowledge validation: As knowledge on semantic web may be redundant, out-of-date, incorrect, or distorted, it is necessary for semantic web to validate the result set of knowledge retrieving. Knowledge validation in NGKM refers to the process by which new "knowledge claims" are subjected to peer review and a test of value in practice [10]. The process of validating semantic knowledge can be carried out based on its authenticity and integrity. Digital signatures, encryption, certificate authority technology are the most prevalent technologies for semantic web to validate its knowledge.

(6)Knowledge integration: Diverse knowledge which has been validated sometimes needs to be integrated with each other. There are three kinds of knowledge integration technologies:

 Technologies to integrate knowledge with each other, such as Message-oriented Middleware (MOM), Message Broker or Adapters.


Though knowledge management on semantic web is only in the initial phase, it looks quite promising. That knowledge life cycle on semantic has three features: 1) the life cycles of knowledge on semantic web is a continuums regime of knowledge process, 2) six distinct stages in semantic knowledge management are representation, interconnection, reasoning, retrieving , validation and integration, and 3) semantic knowledge management requires different methodologies or technologies for different stages.[11]
