Itv0060 2011 arhiiv

Allikas: Lambda
teadmised

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.


Järeleksam 2011 sügis

Järeleksam toimub 26 august (reede): kell 10 auditoorium IT-137a

Eksam ja konsultatsioon

16. mai: eksamiks õppimise materjalide ülevaade ja fikseerimine.

Eksam:


järeleksam 15 juuni (kolmapäev): kell 9 auditoorium IT-137a

15 juuni tulemused:, eksamipunktid viidud 0-60 skaalale:

  • HP eksam 52p + 40p ettekanded, hinne 5
  • GJ eksam 31p + 32p ettekanded, hinne 2

3 juuni (reede): kell 9 auditoorium VI-121

3 juuni tulemused:, eksamipunktid viidud 0-60 skaalale:

  • TT eksam 36p + 40p ettekanded, hinne 3
  • RL eksam 40p + 40p ettekanded, hinne 4
  • HP eksam 28p + 40p ettekanded, hinne 2
  • VP eksam 22p + 40p ettekanded, hinne 2
  • DA eksam 57p + 40p ettekanded, hinne 5

8 juuni (kolmapäev): kell 9 auditoorium VII-131

8 juuni tulemused:, eksamipunktid viidud 0-60 skaalale:

  • KL eksam 60p + 40p ettekanded, hinne 5
  • ML eksam 51p + 40p ettekanded, hinne 5
  • ET eksam 48p + 30p ettekanded, hinne 3
  • DF eksam 58p + 40p ettekanded, hinne 5

Järeleksam kevadel:

  • 15 juuni (kolmapäev): kell 9 auditoorium IT-137a

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/

Materials to study for the exam

Eksamiküsimused:

Väikesed ülesanded: data ja reeglite esitamine ja kasutamine

Andmebaas nö tüüpilise relatsioonilise mudelina, key-value ja rdf kujul

Ülesanne blocks worldist: esita mingi olukord, mingi reegel ja näita rakendamise tulemust

RDF, RDFS ja OWL põhiasjad: väike ülesanne. Süntaksid ja eridetailid ei ole olulised.

RDF ja RDFS esitada loogikas faktide ja reeglitena, mõni lihtne OWLi näide võib ka olla

Arusaamine RDFa põhiasjadest, oskus teha näidet.

MÕni üldküsimus Nelli ja Attempto kohta.

Arusaam et mis on mittemonotoonne loogika ja mis ei ole, mis oleks viisid esitada vasturääkivaid teadmisi. Võib olla väike ülesanne mingite asjade esitamise kohta: näide ja selgitus.

Default loogika ülesanne a la linnud ja Quaker diamond: panna kirja reeglid ja viia läbi järeldused ja vbl selgitada lisaks. Seos CWA, Prolog ja default loogika vahel võib samuti küsimuse alla tulla.

Indeksid: arusaam baasindekseerimis-ideedest ja B+ treest. Ntx ehitada B+ mingi väikese datahulga jaoks. Sama hashi kohta ja bit indeksite kohta. Eriteemad a la indeksi pakkimine ja erinevad hashi viisid ei tule teemaks.

Täistekstiindeksid: osata ehitada näite pealt. Vt presentatsioonist erinevaid viise, ntx Patricia trie.

Termiindeksid: piisab discrimination tree indeksist ja näite põhjal indeksi ehitamisest.


Declarative and procedural representations

Lecture material:

Databases: meaning and representation of facts

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

Lecture material:

See also:


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

Lecture material:

Understand RDF and RDFS:

Understand basics of owl:

RDFa, reading and using RDF data from web pages

Understand main parts:

RDF relation to logic

Notes from lecture:

Understand a bit of:

Semantics of ordinary relational base

First order logic in query engines and planning


Data extraction

Web mining by Nell:

Restricted english jms

Uncertain knowledge logic

Indexes

Traditional database indexes incl B+ tree:

Bitmap indexes:

Hash indexes:

Fulltext indexes:

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

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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