Knowledge Acquisition, Representation and Publishing

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