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.

Eksam ja konsultatsioon

16. mai: eksamiks õppimise materjalide ülevaade ja fikseerimine.

Eksam:

  • 25 mai (kolmapaev): kell 9 auditoorium VII-131
  • 3 juuni (reede): kell 9 auditoorium VI-121

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.

Details in Teadmised second lab 2011.

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.

What to do: 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. It is a good idea to avoid probabilities in these rules, since it would be very hard to encode probabilities using attempto.


See: http://attempto.ifi.uzh.ch/site/ , http://attempto.ifi.uzh.ch/ape/

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:


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

Lecture material:

Topics:

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

See also:

Various rule/logic languages

Lecture material:

Several languages:

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

See also:

Scraping semistructured data

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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