This document provides an overview of knowledge acquisition, representation, and publishing. It defines knowledge and discusses how knowledge can be captured, structured, and shared. Knowledge acquisition involves extracting knowledge from sources, structuring the knowledge, and organizing it for representation. Knowledge representation standards like the Resource Description Framework (RDF) and ontologies provide structured descriptions of knowledge through semantic annotations and metadata. Personal knowledge publishing allows individuals to share their knowledge through various online tools and formats.

1. KNOWLEDGE
A BRIEF INTRO TO ACQUISITION,
REPRESENTATION AND PUBLISHING
CCS515 Guest Lecture
by Dr. Gan Keng Hoon
27 October 2015

2. Outline
◦Knowledge Publishing
◦Knowledge Representation
◦Knowledge Acquisition

3. Knowledge
◦What is knowledge?
Knowledge is a familiarity, awareness or understanding of
someone or something, such as facts, information,
descriptions, or skills, which is acquired through
experience or education by perceiving, discovering, or
learning. (Wikipedia)

4. My Knowledge
◦ List your knowledge.
◦ Where did you get the knowledge?
◦ How do you want to share them?

5. Knowledge Management
Wikipedia (2015)
Knowledge management (KM) is the process of capturing, developing, sharing, and
effectively using organizational knowledge. It refers to a multi-disciplinary approach to
achieving organizational objectives by making the best use of knowledge.
Davenport (1994) offered the still widely quoted definition:
"Knowledge management is the process of capturing, distributing, and effectively using
knowledge.“
(Duhon, 1998):
"Knowledge management is a discipline that promotes an integrated approach to
identifying, capturing, evaluating, retrieving, and sharing all of an enterprise's
information assets. These assets may include databases, documents, policies,
procedures, and previously un-captured expertise and experience in individual workers."

6. Manipulating Knowledge
(Duhon, 1998):
“Knowledge management is a discipline that promotes an integrated approach to identifying,
capturing, evaluating, retrieving, and sharing all of an enterprise's information assets. These assets
may include databases, documents, policies, procedures, and previously un-captured expertise
and experience in individual workers.”
Knowledge
Representation
Knowledge
Acquisition
Knowledge
Publishing

7. Knowledge Publishing/Sharing
◦ Advantage
◦ Avoid losing of information an organization or one as acquired.
◦ Support quicker decision making, better efficiency.
◦ Reusable and benefited by endless users, staffs, readers.
◦ Build reputations in terms of expertise.
◦ Promote knowledge exchange and creation of new knowledge.

8. Tools for Knowledge Publishing
◦ Offline
◦ Books, newspaper, news etc.
◦ Online
◦ e-books, e-newspapers, e-news etc.
◦ Websites
◦ Blogger (Blog)
◦ Tumblr (Microblog)
◦ Twitter (Short message)
◦ Pinterest (Image)
◦ youTube (Video)
etc…

9. Personal Knowledge Publishing
◦ One way to communicate
personal knowledge is by
sharing and publishing.

10. Activities Involved in Personal
Knowledge Publishing
◦Exploring
◦Push
◦Share links via social
media
◦Pull
◦Enable SEO
◦Bookmarked

11. Personal Knowledge Publishing using
Blog
◦ Let’s blog.
◦ Why blog?
◦ Make implicit knowledge (e.g. not codified or structured) more
explicit.
◦ Reflect on own learning.
◦ Responsibility needed as it is publicly available.

13. Personal Knowledge Publishing using
Images
◦ Knowledge does not
limit to text.
◦ Can be covey using
images or other
medias.
◦ Pin a series of
images to show
some implicit idea.

14. Personal Knowledge Publishing using
Videos
◦Record your
knowledge.
◦What else
can be
recorded?

15. Personal Knowledge Publishing
Issues
◦ Knowledge overload.
Can our World Wide Web handle the capacity of ever growing size of
information?
BIG DATA, STORAGE, CLOUD, NETWORK
◦ Credibility
Do you have any idea which information source to trust?
FEEDBACK, SENTIMENT ANALYSIS, DATA ANALYSIS
◦ Discovery
Any better ways to discovery the published knowledge?
SEARCH, FRIENDS RECOMMENDATION

16. Knowledge Representation
How to make knowledge publishing better?
1. I want user to be able to locate my video.
2. I want user to discover the slides I share.
3. I want the navigation of my blog posts to be topics
related.
4. Many more…

17. Knowledge Representation
Tag a concept:
Automata
Computer
Science
Tag a name:
Michael Benjamin
Tag a name: Shai
Simonson

18. Knowledge Representation
◦ So, we have resources like videos, images, texts…
◦ We need a way to making them more meaningful.
Resource and Description
However, each resource has its own format.
Need standard form.
SOLUTION: Define a standard language for writing the
description -> Metadata (Semantic Web Terminology)

19. Knowledge Representation
Resource
Description
Instructor: Shai
Simonson
Title: Lecture 1 – Finite State
Machines (Part 1/9)
Duration: 9:59
Uploaded: May
7 2010

20. Knowledge Representation
- Resource Description Framework
◦ This leads to one of the Semantic Web main task
Metadata Annotation
- description of resources using standard language
◦ Useful for search and discovery.

21. Knowledge Representation
- Resource Description Framework
◦ Common language for describing
resource
◦ A statement with structure.
◦ A statement is a triple.
◦ Subject-predicate-object
◦ Subject: resource
◦ Predicate: a verb/property/relation
◦ Object: A resource/a literal string

22. Knowledge Representation
- Resource Description Framework
To describe the statement: "The instructor of https://www.youtube.com/watch?v=HyUK5RAJg1c is Shai
Simonson".
The subject of the statement above is: https://www.youtube.com/watch?v=HyUK5RAJg1c
The predicate is: author
The object is: Shai Simonson
Simplified RDF
<?xml version="1.0"?>
<RDF>
<Description about="https://www.youtube.com/watch?v=HyUK5RAJg1c">
<instructor>Shai Simonson</instructor>
<title>Lecture 1 – Finite State Machines (Part 1/9)</title>
</Description>
</RDF>
Study: http://www.w3schools.com/xml/xml_rdf.asp

23. Knowledge Representation
- Resource Description Framework
Source: Fulvio Corno, Semantic Web,
Metadata, Knowledge Representation,
Ontologies

24. Knowledge Representation
- Resource Description Framework
Solution: metadata standardization is required
Many standardization bodies are involved
General standard
e.g. Dublin Core (DC)
or may depend on goal, context, domain, …
e. g. educational resources (IEEE LOM), multimedia resources (MPEG-7),
images (VRA), people (FOAF, IEEE PAPI), geospatial resources (GSDGM),
bibliographical resources (MARC, OAI), cultural heritage resources
(CIDOC CRM)

25. Knowledge Representation - Ontology
Semantically rich descriptions need “understanding” the
meaning of a resource and the domain related to the resource
Disambiguation of terms
Shared agreement on meanings
Description of the domain, with concepts and relations among
concepts

26. Knowledge Representation - Ontology
◦ Controlled vocabularies
◦ Taxonomies
◦ Thesauri
◦ Faceted classification
◦ Ontologies
◦ Folksonomies
◦ Others

27. Knowledge Representation - Ontology
Taxonomy
Subject-based classification that arranges the
terms in the controlled vocabulary into a
hierarchy

28. Knowledge Representation - Ontology
◦ ACM Classification
system.
◦ Used to annotate
bibliography.

29. Knowledge Representation - Ontology
Model for describing the world that
consists of a set of types,
properties, and relationships.

30. Knowledge Representation - Ontology
Ontologies generally describe:
Individuals
◦ the basic or “ground level” objects
Classes
◦ sets, collections, or types of objects
Attributes
◦ properties, features, characteristics, or parameters that objects can have
and share
Relationships
◦ ways that objects can be related to one another

31. Knowledge Representation - Ontology
◦ How much knowledge do you
have about ice cream??

32. Knowledge Representation - Ontology
Web Ontology Language (OWL) is a family of knowledge
representation languages for authoring ontologies.
Built upon a W3C XML standard for objects called the
Resource Description Framework (RDF).
Computational logic-based language, exploited by computer
programs, e.g., to verify the consistency of that knowledge or
to make implicit knowledge explicit.

33. Knowledge Acquisition
Where does the knowledge comes from?
Manual
◦ Written by expert.
Automated
◦ Gathering from those written by expert.
◦ Allow aggregation, consolidation and organization for better usage.
◦ Allow enhancement like semantic annotation, classification.

34. Knowledge Acquisition
◦ Knowledge acquisition is the process of extracting, structuring and
organizing knowledge from one source, usually human experts.
◦ Extraction
◦ Get resource from texts.
◦ Structuring
◦ Annotate the resource.
◦ Organizing
◦ Store the resource in representation like ontology.

35. Knowledge Acquisition
Knowledge can be extracted from
Unstructured Text
◦ Web pages
◦ Article
◦ Scanned document
Semi Structured Text
◦ XML
◦ Excel
◦ CSV
◦ BIB

36. Knowledge Acquisition
Extraction from unstructured text
◦ Can you differentiate between Person and Organization?

37. Knowledge Acquisition
Extracting aspect and sentiment from a sentence.
Use Part of Speech Tagging.
Review sentence:
The room is beautiful.
POS tagged sentence:
The/DT room/NN is/VBZ beautiful/JJ./.
Representing the acquired knowledge:
RDF triple(hasSentiment, room, beautiful)
General simple rule (R1):
+.*(/nn1) +.*(/jj1) +
Mapping of aspect and opinion
(M1):
map (nn1, jj1)

38. Knowledge Acquisition
– Road Ahead
Too much knowledge out there to be acquired.
Lots of research opportunities, especially,
unstructured resource to structured resource
Identify relation in a resource
Identify implicit meaning in a resource

39. Contact
Gan Keng Hoon
khganATusm.my
Visit our works at
ir.cs.usm.my
Picture Source: http://www.mindonsolutions.com