Itv0060 20010 arhiiv

Allikas: Lambda

Code: ITV0060
Link:,_formaliseerimine_ja_hoidmine or
Lecturer: Tanel Tammet
Contact:, 6203457, TTÜ AK223
Archives of previous years: [ 2009, 2008, 2007, 2006, [http:/ older].

NB! Tegu on 2010 aasta arhiiviga, mitte kehtivate kursusematerjalidega!


Eelnadala jareltood kell 10.00 reedel, 27 augustil ruumis VII-131.

Exam results

Nimi Matr Eks punkte kokku punkte Hinne

  • MM 41 89 4
  • RM 11050 47 96 5
  • MS 93047 35 82 4
  • MT 93033 30 76 3
  • AS 93059 47 96 5
  • MH 930646 32 78 3
  • BS 93046 28 73 3
  • AJ 960650 25 70 3
  • KU 93038 47 97 5
  • SS 993072 18 61 2
  • MS 93050 45 96 5

Two alternative dates:

  • 28 May: 12.00-15.00 room VI-201
  • 4 June: 12.00-15.00 room VI-229

Time, place, result

Semester: spring
Grading: exam
Points: 3.5

Lectures: every Monday 15.00-16.30, room IT-137a
Practical work: every Monday after the lecture: 16.45-17.30

Practical work will give 40% and exam 60% of points underlying the final grade. There will be an optional (hard) third practical work: choosing this will give 60% from labs and 40% from exam.

Practical work

  • two labs on the same theme.
  • optional third lab

First lab

Date: 10 March.

Implement a system which can read html annotated according to the RDFa standard. The data read should be saved and printed in several formats:

  • csv
  • rdf xml syntax

NB! You may not use a ready-made RDFa parser/converter. Instead, use a html/xml parser and build the RDFa converter on top of that. Using xslt is also an OK option. Programming language and the html/xml parser choice is free.

You will have to find suitable annotated html pages to demonstrate your solution.

Additional reading:

You may also look at some practical non-obligatory programming suggestions in Estonian: rdfa esimese praksi soovitusi.

Second lab

Using the results of first lab for rule-based derivation of new information.

Details of the second lab: rule system for tourist suggestions.

Third lab

Third lab is optional. If you do it, it will give additional 20% of points underlying the final grade. There are two alternative ways to do the third lab: select yourself:

  • First alternative: download and use Wordnet to automatically create additional rules used in the second lab. These rules would mostly probably stem from the Wordnet taxonomies. You do not have to create rules for all the wordnet taxonomies: select a sensible subset which would be usable for the second lab.
  • Second alternative: use Attempto to create a system such that you can write rules and/or facts and/or queries for the second lab in restricted english. These restricted english sentences have to be automatically converted to the form usable by Otter/Plan9.

Materials for the exam

Use and read the following

RDF, RDFS, OWL and conversion to 1st order logic

Understand RDF and RDFS:

Understand basics of owl:

RDFa, reading and using RDF data from web pages

Understand main parts:

RDF relation to logic

Understand a bit of:

Semantics of ordinary relational base

First order logic in query engines and planning

Different languages for 1st order logic: KIF, CL, restricted english jms

Uncertain knowledge logic

Traditional database indexes

Fulltext indexes

Term indexes

Understand basics of discrimination tree trie-type term indexes and path indexes from

Course structure

Intro and paradigms of knowledge representation

1 lecture.

Overview of knowledge representation:

  • Procedural
  • Declarative
    • logic
    • relational, network, object databases
    • semantic networks
    • frame systems
    • property/value pairs
    • rule/expert systems
    • rdfs and owl

Different property/value pair representations

Introduction to RDFa

1 lecture

Relational databases, logic and RDF

1 lecture


  • propositional, first order, higher order
  • semantics/models
  • proofs and deriving new information
  • automated reasoning


With logic and reasoning-based methods.

Specific versions of 1st order logic and rule systems

  • KIF
  • CL
  • ontologies
  • wordnet
  • cyc
  • restricted english systems


Probabilistic and default reasoning

Practical reasoning with procedural attachments: robot example

Representing time, epistemics, context

Personalized suggestions with probabilistic rules

Search and indexing