Lecture 7 the nature of digital knowledge

Jashapara, Knowledge Management: An Integrated Approach, 2nd Edition, © Pearson Education Limited 2011
Slide 7.1
Analysis 1: Evidence and the Nature of
Knowledge in the Digital Age
Topic: The Nature of Digital Knowledge
Topic Number: 7

Slide 7.2
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

Slide 7.3
Questions to think about during the
session
• 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

Slide 7.4
COMPONENT TOOLS TYPOLOGY
Figure 7.1 A typology of knowledge tools and component technologies

Slide 7.5
DIFFERENT FORMS OF KNOWLEDGE
Figure 7.2 Different forms of knowledge

Slide 7.6
ORGANISING KNOWLEDGE: ONTOLOGY
& TAXONOMY
Figure 7.3 Ontology and taxonomies

Slide 7.7
ONTOLOGY GENERATION
TECHNOLOGIES
• Manually
• ‘Part of speech’ tagging
• ‘Word sense disambiguation’
• ‘Tokeniser’
• ‘Pattern matching’
• Semi-automatic generation with machine learning

Slide 7.8
INTEGRATING
ONTOLOGIES
(see Ding & Foo, 2002)
Figure 7.4 Ontology integration techniques

Slide 7.9
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

Slide 7.10
CAPTURING KNOWLEDGE
Indexing a Text Database
Figure 7.8 Indexing a text database

Slide 7.11
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

Slide 7.12
CAPTURING KNOWLEDGE
Retrieval Process
Figure 7.9 Information retrieval process

Slide 7.13
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

Slide 7.14
CAPTURING KNOWLEDGE
Search Engines: Crawler Indexer
Figure 7.10 Search engine: crawler-indexer architecture

Slide 7.15
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

Slide 7.16
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

Slide 7.17
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

Slide 7.18
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’

Slide 7.19
Data mining
• Uses variety of neural network, decision trees and
genetic modelling algorithms
• Use sophisticated data search capabilities using
algorithms to discover patterns and correlations in
vast amounts of data

Slide 7.20
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

Slide 7.21
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

Slide 7.22
SHARING KNOWLEDGE
• E-mail
• Text-based conferencing
• Yellow Pages
• Computer-based training/e-learning
• Security

Slide 7.23
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

Slide 7.24
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

Slide 7.25
WEB 2.0 PLATFORM
Figure 7.11 Web 2.0 platform tools

Slide 7.26
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

Slide 7.27
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

Slide 7.28
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

Slide 7.29
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

Slide 7.30
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

Slide 7.31
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

Slide 7.32
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

Slide 7.33
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

34
Reading and preparatory work to be done
Read:
• Jashapara, A. (2011) “ Knowledge Management:
An Integrated Approach” Pearson Education,
Chapter 7
Work to be done before the seminar:
• Carry out all the reading above
• Answer the questions on the hand-out
• Bring your work to the seminar
34

Slide 7.35
Essential work for next week
• Please consult the OLE for details of:
– Essential readings*
– Seminar/workshop preparation work*
– Recommended further readings
– Any additional learning
* Essential readings and preparation work must always be completed in time
for the next session
35

Lecture 7 the nature of digital knowledge

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