Teadmiste otsing, formaliseerimine ja hoidmine

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Code: ITV0060
Link: http://www.lambda.ee/index.php/Teadmiste_otsing,_formaliseerimine_ja_hoidmine or http://www.lambda.ee/index/itv0060
Lecturer: Tanel Tammet
Contact: tammet@staff.ttu.ee, 6203457, TTÜ AK223
Archives of previous years: 2010, 2009, 2008, 2007, 2006, [http:/cs.ttu.ee/kursused/wav4130/ older].


Time, place, result

Semester: spring
Grading: exam
Points: 3.5

Lectures: every Monday 12.00-13.30, room IT-137a
Practical work: Mondays on odd weeks 10.00-11.30, room IT-213H:
31 jan, 14 feb, 28 feb, 14 mar, 28 mar, 4 apr, 18 apr, 2 may, 16 may.

Practical work will give 40% and exam 60% of points underlying the final grade.

Practical work

There are two labs on the same theme and an optional third lab.

First lab

The main goal of the first lab is to web-scrape data from the public sources using search engines and simple text analysis/statistics.

Details in Teadmised first lab 2011. The lab should be completed by March, later presentation will cut the points to half.

Second lab

The main goal of the second lab is to augment data obtained from the first lab by writing and using probabilistic rules with a rule engine. The lab should be finished by 21 May, details will be given in the beginning of March.

Wordnet will be used as one of the tools.

Optional third lab

The optional third lab can be done in parallel with the second. It will be a relatively complex lab: successful passing will give additional 20 points, thus the weight of the exam is diminished from 60% to 40% of the overall score. The lab should be finished by 21 May, details will be given in the beginning of March.

Course structure

We start with representing and converting facts, continue to rules, first monotonic, then probabilistic and nonmonotonic, then look into search and indexing of complex information, and finally, some learning.

Intro and paradigms of knowledge representation

Lecture materials:

For KR, declarative and procedural rep see:

Web mining by Nell:

Databases: meaning and representation of facts

Lecture material:

See also about databases and logic:

See also about RDF and related:

Scraping semistructured data

Rules in logic: apps in planning, robotics, ...

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

Lecture material:

See also:

Various rule/logic languages

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

See also:

Time, context, metainformation

Probabilistic and nonmonotonic reasoning

See also:

Personalized search and recommendations

Fast search of facts and rules using indexing

Learning

See also:


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