Slide 7. Jashapara, Knowledge Management: An Integrated Approach, 2 nd Edition, Pearson Education Limited Nisan 14 Pazartesi

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1

2 WELCOME!

3 WELCOME! Chapter 7

4 WELCOME! Chapter 7

5 WELCOME! Chapter 7 KNOWLEDGE MANAGEMENT TOOLS:

6 WELCOME! Chapter 7 KNOWLEDGE MANAGEMENT TOOLS: Component Technologies

7 LEARNING OBJECTIVES

8 LEARNING OBJECTIVES To explain the differences between ontology and taxonomy tools for organising knowledge

9 LEARNING OBJECTIVES To explain the differences between ontology and taxonomy tools for organising knowledge To describe cognitive mapping and information retrieval tools for capturing knowledge

10 LEARNING OBJECTIVES To explain the differences between ontology and taxonomy tools for organising knowledge To describe cognitive mapping and information retrieval tools for capturing knowledge To distinguish between different tools for evaluating knowledge

11 LEARNING OBJECTIVES To explain the differences between ontology and taxonomy tools for organising knowledge To describe cognitive mapping and information retrieval tools for capturing knowledge To distinguish between different tools for evaluating knowledge To assess the different tools for sharing knowledge

12 LEARNING OBJECTIVES To explain the differences between ontology and taxonomy tools for organising knowledge To describe cognitive mapping and information retrieval tools for capturing knowledge To distinguish between different tools for evaluating knowledge To assess the different tools for sharing knowledge To explain technologies for storing and presenting knowledge

13

14 BUZZ GROUP

15 BUZZ GROUP What types of knowledge or information do I use in my everyday life?

16 BUZZ GROUP What types of knowledge or information do I use in my everyday life? What types of technology do I use to manage this knowledge? You may wish to consider the types of technologies you use to capture, organise, evaluate, store and share knowledge

17 Figure 7.1 A typology of knowledge tools and component technologies

18 COMPONENT TOOLS TYPOLOGY Figure 7.1 A typology of knowledge tools and component technologies

19 Figure 7.2 Different forms of knowledge

20 DIFFERENT FORMS OF KNOWLEDGE Figure 7.2 Different forms of knowledge

21 Figure 7.3 Ontology and taxonomies

22 ORGANISING KNOWLEDGE: ONTOLOGY & TAXONOMY Figure 7.3 Ontology and taxonomies

23

24 ONTOLOGY GENERATION TECHNOLOGIES

25 Manually ONTOLOGY GENERATION TECHNOLOGIES

26 ONTOLOGY GENERATION TECHNOLOGIES Manually Part of speech tagging

27 ONTOLOGY GENERATION TECHNOLOGIES Manually Part of speech tagging Word sense disambiguation

28 ONTOLOGY GENERATION TECHNOLOGIES Manually Part of speech tagging Word sense disambiguation Tokeniser

29 ONTOLOGY GENERATION TECHNOLOGIES Manually Part of speech tagging Word sense disambiguation Tokeniser Pattern matching

30 ONTOLOGY GENERATION TECHNOLOGIES Manually Part of speech tagging Word sense disambiguation Tokeniser Pattern matching Semi-automatic generation with machine learning

31 BUZZ GROUPS

32 BUZZ GROUPS Describe the different options available to you for integrating ontologies from two distinct knowledge bases in the organisation such as sales and finance. This could equally apply to integrating knowledge bases between two organisations in a takeover or merger situation

33 Figure 7.4 Ontology integration techniques

34 INTEGRATING ONTOLOGIES (see Ding & Foo, 2002) Figure 7.4 Ontology integration techniques

35 CAPTURING KNOWLEDGE Cognitive Mapping Tools

36 CAPTURING KNOWLEDGE Cognitive Mapping Tools Used principally in mapping strategic knowledge

37 CAPTURING KNOWLEDGE Cognitive Mapping Tools Used principally in mapping strategic knowledge Use oval mapping technique in groups

38 CAPTURING KNOWLEDGE Cognitive Mapping Tools Used principally in mapping strategic knowledge Use oval mapping technique in groups Develop concepts, links and clusters

39 CAPTURING KNOWLEDGE Cognitive Mapping Tools Used principally in mapping strategic knowledge Use oval mapping technique in groups Develop concepts, links and clusters Decision Explorer can develop complex levels of analysis

40 Figure 7.8 Indexing a text database

41 CAPTURING KNOWLEDGE Indexing a Text Database Figure 7.8 Indexing a text database

42 CAPTURING KNOWLEDGE Information Retrieval Tools

43 CAPTURING KNOWLEDGE Information Retrieval Tools Desire for precision and recall

44 CAPTURING KNOWLEDGE Information Retrieval Tools Desire for precision and recall Differences between an author s and user s vocabulary

45 CAPTURING KNOWLEDGE Information Retrieval Tools Desire for precision and recall Differences between an author s and user s vocabulary Use of inverted files for indexing text to speed up search assumes text as sequence of words easy to compress

46 CAPTURING KNOWLEDGE Information Retrieval Tools Desire for precision and recall Differences between an author s and user s vocabulary Use of inverted files for indexing text to speed up search assumes text as sequence of words easy to compress Develop inverted index including vocabulary search, list of occurrences and processing of occurrences to solve phrases, proximity and Boolean operations

47 CAPTURING KNOWLEDGE Information Retrieval Tools Desire for precision and recall Differences between an author s and user s vocabulary Use of inverted files for indexing text to speed up search assumes text as sequence of words easy to compress Develop inverted index including vocabulary search, list of occurrences and processing of occurrences to solve phrases, proximity and Boolean operations Suffix Trees & Indicies allows more complex queries. Sees text as long string with each position as a suffix

48 CAPTURING KNOWLEDGE Information Retrieval Tools Desire for precision and recall Differences between an author s and user s vocabulary Use of inverted files for indexing text to speed up search assumes text as sequence of words easy to compress Develop inverted index including vocabulary search, list of occurrences and processing of occurrences to solve phrases, proximity and Boolean operations Suffix Trees & Indicies allows more complex queries. Sees text as long string with each position as a suffix Signature files cuts text into blocks. Not as good as inverted index

49 CAPTURING KNOWLEDGE Information Retrieval Tools Desire for precision and recall Differences between an author s and user s vocabulary Use of inverted files for indexing text to speed up search assumes text as sequence of words easy to compress Develop inverted index including vocabulary search, list of occurrences and processing of occurrences to solve phrases, proximity and Boolean operations Suffix Trees & Indicies allows more complex queries. Sees text as long string with each position as a suffix Signature files cuts text into blocks. Not as good as inverted index Manipulation algorithms such as BNDM and BMS for Boolean queries

50 Figure 7.9 Information retrieval process

51 CAPTURING KNOWLEDGE Retrieval Process Figure 7.9 Information retrieval process

52

53 CAPTURING KNOWLEDGE Text Processing

54 CAPTURING KNOWLEDGE Text Processing Lexical analysis to identify words from characters

55 CAPTURING KNOWLEDGE Text Processing Lexical analysis to identify words from characters Eliminating stopwords occurring frequently

56 CAPTURING KNOWLEDGE Text Processing Lexical analysis to identify words from characters Eliminating stopwords occurring frequently Stemming e.g. Connect is stem for connected, connecting, and connections

57 CAPTURING KNOWLEDGE Text Processing Lexical analysis to identify words from characters Eliminating stopwords occurring frequently Stemming e.g. Connect is stem for connected, connecting, and connections Full text indexing

58 CAPTURING KNOWLEDGE Text Processing Lexical analysis to identify words from characters Eliminating stopwords occurring frequently Stemming e.g. Connect is stem for connected, connecting, and connections Full text indexing Thesaurus index terms synonyms and near synonyms

59 CAPTURING KNOWLEDGE Text Processing Lexical analysis to identify words from characters Eliminating stopwords occurring frequently Stemming e.g. Connect is stem for connected, connecting, and connections Full text indexing Thesaurus index terms synonyms and near synonyms Text compression to cope with information overload

60 Figure 7.10 Search engine: crawler-indexer architecture

61 CAPTURING KNOWLEDGE Search Engines: Crawler Indexer Figure 7.10 Search engine: crawler-indexer architecture

62 CAPTURING KNOWLEDGE Search Engines: IR and the Web

63 CAPTURING KNOWLEDGE Search Engines: IR and the Web Centralised crawler-indexer architecture. Crawlers (software agents) traverse web sending back pages for indexing. Indexer deals with query from user and new info. from crawler

64 CAPTURING KNOWLEDGE Search Engines: IR and the Web Centralised crawler-indexer architecture. Crawlers (software agents) traverse web sending back pages for indexing. Indexer deals with query from user and new info. from crawler Decentralised gatherers-brokers architecture. Gatherers collect and extract indexing info. from lots of servers. Brokers provide indexing and query interface

65 CAPTURING KNOWLEDGE Search Engines: IR and the Web Centralised crawler-indexer architecture. Crawlers (software agents) traverse web sending back pages for indexing. Indexer deals with query from user and new info. from crawler Decentralised gatherers-brokers architecture. Gatherers collect and extract indexing info. from lots of servers. Brokers provide indexing and query interface Metasearchers are Web servers that send query to several search engines

66 CAPTURING KNOWLEDGE Search Engines: IR and the Web Centralised crawler-indexer architecture. Crawlers (software agents) traverse web sending back pages for indexing. Indexer deals with query from user and new info. from crawler Decentralised gatherers-brokers architecture. Gatherers collect and extract indexing info. from lots of servers. Brokers provide indexing and query interface Metasearchers are Web servers that send query to several search engines Most common query on the Web is 2.3 words

67

68 CAPTURING KNOWLEDGE Personalisation

69 CAPTURING KNOWLEDGE Personalisation Device provides needs and wants of consumer

70 CAPTURING KNOWLEDGE Personalisation Device provides needs and wants of consumer Solution lies in data mining in terms of analysing user s clickstream and making recommendations

71 CAPTURING KNOWLEDGE Personalisation Device provides needs and wants of consumer Solution lies in data mining in terms of analysing user s clickstream and making recommendations Use of agents and machine learning

72

73 EVALUATING KNOWLEDGE Case-based reasoning

74 EVALUATING KNOWLEDGE Case-based reasoning Capture and store past experiences as organisational knowledge

75 EVALUATING KNOWLEDGE Case-based reasoning Capture and store past experiences as organisational knowledge System searches for stored cases with similar profile to new problem

76 EVALUATING KNOWLEDGE Case-based reasoning Capture and store past experiences as organisational knowledge System searches for stored cases with similar profile to new problem Adds unsuccessful cases to aid learning

77 EVALUATING KNOWLEDGE Case-based reasoning Capture and store past experiences as organisational knowledge System searches for stored cases with similar profile to new problem Adds unsuccessful cases to aid learning Built on artificial intelligence technology

78 BUZZ GROUPS

79 BUZZ GROUPS Discuss the limitations of case-based reasoning tools

80

81 EVALUATING KNOWLEDGE OLAP: On-line analytical processing

82 EVALUATING KNOWLEDGE OLAP: On-line analytical processing Provides multidimensional analysis of data to allow user to see data in different ways using multiple dimensions

83 EVALUATING KNOWLEDGE OLAP: On-line analytical processing Provides multidimensional analysis of data to allow user to see data in different ways using multiple dimensions Main technique is to rotate a data cube

84 EVALUATING KNOWLEDGE OLAP: On-line analytical processing Provides multidimensional analysis of data to allow user to see data in different ways using multiple dimensions Main technique is to rotate a data cube Also called slice and dice

85

86 EVALUATING KNOWLEDGE Data mining

87 EVALUATING KNOWLEDGE Data mining Uses variety of neural network, decision trees and genetic modeling algorithms

88 EVALUATING KNOWLEDGE Data mining Uses variety of neural network, decision trees and genetic modeling algorithms Use sophisticated data search capabilities using algorithms to discover patterns and correlations in vast amounts of data

89 SHARING KNOWLEDGE Internet/Intranet

90 SHARING KNOWLEDGE Internet/Intranet Share knowledge with knowledge providers across the world some free

91 SHARING KNOWLEDGE Internet/Intranet Share knowledge with knowledge providers across the world some free Intranet provides same but restricted access from outside

92 SHARING KNOWLEDGE Internet/Intranet Share knowledge with knowledge providers across the world some free Intranet provides same but restricted access from outside Uses HTML and XML a metalanguage that allows definition of tags and allows distribution of knowledge to call phones, pagers and PDAs

93 BUZZ GROUPS Intranets can be large data warehouses that nobody visits. Critically discuss the barriers that prevent knowledge sharing in organisations

94

95 SHARING KNOWLEDGE Groupware tools

96 SHARING KNOWLEDGE Groupware tools Allows to work on same document by multiple users

97 SHARING KNOWLEDGE Groupware tools Allows to work on same document by multiple users Maintain and update identical data on numerous PCs

98 SHARING KNOWLEDGE Groupware tools Allows to work on same document by multiple users Maintain and update identical data on numerous PCs Organising discussions

99 SHARING KNOWLEDGE Groupware tools Allows to work on same document by multiple users Maintain and update identical data on numerous PCs Organising discussions Storing information

100 SHARING KNOWLEDGE Groupware tools Allows to work on same document by multiple users Maintain and update identical data on numerous PCs Organising discussions Storing information Moving and tracking documents of groups

101 SHARING KNOWLEDGE Groupware tools Allows to work on same document by multiple users Maintain and update identical data on numerous PCs Organising discussions Storing information Moving and tracking documents of groups Preventing unauthorised access of data

102 SHARING KNOWLEDGE Groupware tools Allows to work on same document by multiple users Maintain and update identical data on numerous PCs Organising discussions Storing information Moving and tracking documents of groups Preventing unauthorised access of data Mobile use to access corporate network

103

104 SHARING KNOWLEDGE

105 SHARING KNOWLEDGE

106 SHARING KNOWLEDGE Text-based conferencing

107 SHARING KNOWLEDGE Text-based conferencing Yellow Pages

108 SHARING KNOWLEDGE Text-based conferencing Yellow Pages Computer-based training/e-learning

109 SHARING KNOWLEDGE Text-based conferencing Yellow Pages Computer-based training/e-learning Security

110 WEB 2.0 PLATFORM

111 WEB 2.0 PLATFORM Shift to dynamic social web applications

112 WEB 2.0 PLATFORM Shift to dynamic social web applications Network effects critical to their success

113 WEB 2.0 PLATFORM Shift to dynamic social web applications Network effects critical to their success Provide customer services free Google ($200bn), YouTube ($1.6bn), Facebook ($50bn)

114 WEB 2.0 PLATFORM Shift to dynamic social web applications Network effects critical to their success Provide customer services free Google ($200bn), YouTube ($1.6bn), Facebook ($50bn) Indirect network effects from use of products or services that have influence on related goods and services

115 TIPPING POINT WORD OF MOUTH EPIDEMICS

116 TIPPING POINT WORD OF MOUTH EPIDEMICS Connectors social glue

117 TIPPING POINT WORD OF MOUTH EPIDEMICS Connectors social glue Mavens information brokers on best deals etc.

118 TIPPING POINT WORD OF MOUTH EPIDEMICS Connectors social glue Mavens information brokers on best deals etc. Salesmen good at convincing you and getting you to act

119 TIPPING POINT WORD OF MOUTH EPIDEMICS Connectors social glue Mavens information brokers on best deals etc. Salesmen good at convincing you and getting you to act Amazon is reliant on ranking of reviewers to develop trust with customers

120 TIPPING POINT WORD OF MOUTH EPIDEMICS Connectors social glue Mavens information brokers on best deals etc. Salesmen good at convincing you and getting you to act Amazon is reliant on ranking of reviewers to develop trust with customers Six degrees of separation

121 WEB 2.0 PLATFORM Figure 7.11 Web 2.0 platform tools

122 BLOGS

123 BLOGS Blogs adding thoughts or diary of events

124 BLOGS Blogs adding thoughts or diary of events Podcasts audio blogs

125 BLOGS Blogs adding thoughts or diary of events Podcasts audio blogs Vlog video blog

126 BLOGS Blogs adding thoughts or diary of events Podcasts audio blogs Vlog video blog Trackbacks allows bloggers to see who s linking to them

127 BLOGS Blogs adding thoughts or diary of events Podcasts audio blogs Vlog video blog Trackbacks allows bloggers to see who s linking to them Can act as alternative to face-to-face meetings to engage in problem solving

128 BLOGS Blogs adding thoughts or diary of events Podcasts audio blogs Vlog video blog Trackbacks allows bloggers to see who s linking to them Can act as alternative to face-to-face meetings to engage in problem solving Engage with customers across boundaries

129 BLOGS Blogs adding thoughts or diary of events Podcasts audio blogs Vlog video blog Trackbacks allows bloggers to see who s linking to them Can act as alternative to face-to-face meetings to engage in problem solving Engage with customers across boundaries Twitter micro-blogging site

130 SYNDICATION & RSS FEEDS

131 SYNDICATION & RSS FEEDS Information from articles and photos repackaged for different customers

132 SYNDICATION & RSS FEEDS Information from articles and photos repackaged for different customers RSS (Really Simple Syndication) format to publish frequently updated content on websites

133 SYNDICATION & RSS FEEDS Information from articles and photos repackaged for different customers RSS (Really Simple Syndication) format to publish frequently updated content on websites Organisations can place feeds showing latest offerings or consumer information such as traffic news or weather forecasts

134 SYNDICATION & RSS FEEDS Information from articles and photos repackaged for different customers RSS (Really Simple Syndication) format to publish frequently updated content on websites Organisations can place feeds showing latest offerings or consumer information such as traffic news or weather forecasts RSS viral distribution engine for bloggers receive new material posted by favourite bloggers

135 MASHUPS

136 MASHUPS Allows content from different sources to combine with applications for different business processes

137 MASHUPS Allows content from different sources to combine with applications for different business processes E.g. Getting insurance quote from website

138 MASHUPS Allows content from different sources to combine with applications for different business processes E.g. Getting insurance quote from website E.g. Starbucks helps customers locate nearest café once they ve entered postcode

139 MASHUPS Allows content from different sources to combine with applications for different business processes E.g. Getting insurance quote from website E.g. Starbucks helps customers locate nearest café once they ve entered postcode Information from external sources can be inaccurate or may change significantly in future; even close down

140 MASHUPS Allows content from different sources to combine with applications for different business processes E.g. Getting insurance quote from website E.g. Starbucks helps customers locate nearest café once they ve entered postcode Information from external sources can be inaccurate or may change significantly in future; even close down Prone to threats from malware

141 WIKIS

142 WIKIS Web pages that can be viewed and modified by anyone

143 WIKIS Web pages that can be viewed and modified by anyone Allows to create or change web content

144 WIKIS Web pages that can be viewed and modified by anyone Allows to create or change web content Places power and freedom in hands of users rather than external expert

145 WIKIS Web pages that can be viewed and modified by anyone Allows to create or change web content Places power and freedom in hands of users rather than external expert Works in progress on virtual white boards

146 WIKIS Web pages that can be viewed and modified by anyone Allows to create or change web content Places power and freedom in hands of users rather than external expert Works in progress on virtual white boards Agendas and minutes can be placed on wikis

147 WIKIS Web pages that can be viewed and modified by anyone Allows to create or change web content Places power and freedom in hands of users rather than external expert Works in progress on virtual white boards Agendas and minutes can be placed on wikis Can be open to manipulation and vandalism

148 WIKIS Web pages that can be viewed and modified by anyone Allows to create or change web content Places power and freedom in hands of users rather than external expert Works in progress on virtual white boards Agendas and minutes can be placed on wikis Can be open to manipulation and vandalism Maintenance can be time-consuming

149 ONLINE SOCIAL NETWORKS

150 ONLINE SOCIAL NETWORKS Individuals interact with others in community

151 ONLINE SOCIAL NETWORKS Individuals interact with others in community Social network sites (SNS): Facebook, MySpace, LinkedIn, Friendster

152 ONLINE SOCIAL NETWORKS Individuals interact with others in community Social network sites (SNS): Facebook, MySpace, LinkedIn, Friendster SNS tend to support pre-existing relationships rather than new ones

153 ONLINE SOCIAL NETWORKS Individuals interact with others in community Social network sites (SNS): Facebook, MySpace, LinkedIn, Friendster SNS tend to support pre-existing relationships rather than new ones Benefit from social capital and self-presentation

154 ONLINE SOCIAL NETWORKS Individuals interact with others in community Social network sites (SNS): Facebook, MySpace, LinkedIn, Friendster SNS tend to support pre-existing relationships rather than new ones Benefit from social capital and self-presentation Risk over privacy from third party securing personal information

155 ONLINE SOCIAL NETWORKS Individuals interact with others in community Social network sites (SNS): Facebook, MySpace, LinkedIn, Friendster SNS tend to support pre-existing relationships rather than new ones Benefit from social capital and self-presentation Risk over privacy from third party securing personal information Allows interaction with different people in network

156 3-D VIRTUAL WORLDS

157 3-D VIRTUAL WORLDS Computer-simulated worlds where users interact in real time through avatars

158 3-D VIRTUAL WORLDS Computer-simulated worlds where users interact in real time through avatars Avatars are 3-D electronic cartoons of users; form of alter ego

159 3-D VIRTUAL WORLDS Computer-simulated worlds where users interact in real time through avatars Avatars are 3-D electronic cartoons of users; form of alter ego Second Life has over 15m users and internal currency of Linden dollars (L$)

160 3-D VIRTUAL WORLDS Computer-simulated worlds where users interact in real time through avatars Avatars are 3-D electronic cartoons of users; form of alter ego Second Life has over 15m users and internal currency of Linden dollars (L$) Conduct meetings, workshops and recruitment

161 3-D VIRTUAL WORLDS Computer-simulated worlds where users interact in real time through avatars Avatars are 3-D electronic cartoons of users; form of alter ego Second Life has over 15m users and internal currency of Linden dollars (L$) Conduct meetings, workshops and recruitment Multinationals such as IBM, Dell, Ericsson, Bain

162 3-D VIRTUAL WORLDS Computer-simulated worlds where users interact in real time through avatars Avatars are 3-D electronic cartoons of users; form of alter ego Second Life has over 15m users and internal currency of Linden dollars (L$) Conduct meetings, workshops and recruitment Multinationals such as IBM, Dell, Ericsson, Bain Strathclyde and Coventry Universities bought islands

163

164 STORING KNOWLEDGE Data Warehouse

165 STORING KNOWLEDGE Data Warehouse Database with query and reporting tools

166 STORING KNOWLEDGE Data Warehouse Database with query and reporting tools Stores current and historical data from internal and external sources

167 STORING KNOWLEDGE Data Warehouse Database with query and reporting tools Stores current and historical data from internal and external sources Data mart subset of data warehouse which contains summarised or highly focused data for certain users

168

169 PRESENTING KNOWLEDGE Visualisation

170 PRESENTING KNOWLEDGE Visualisation Modelling way of representing objects e.g. journal covers, weather maps, flows of citations

171 PRESENTING KNOWLEDGE Visualisation Modelling way of representing objects e.g. journal covers, weather maps, flows of citations Rendering makes computer generated image look like photograph e.g. texture mapping

172 PRESENTING KNOWLEDGE Visualisation Modelling way of representing objects e.g. journal covers, weather maps, flows of citations Rendering makes computer generated image look like photograph e.g. texture mapping Virtual reality

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