Itv0060 2012 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: Kalle Tomingas, Tanel Tammet
Contact: kalle.tomingas@gmail.com, 50 40 568
tammet@staff.ttu.ee, 6203457, TTÜ AK223
Archives of previous years: 2011, 2010, 2009, 2008, 2007, 2006, [http:/cs.ttu.ee/kursused/wav4130/ older].

See on 2012 arhiiv: kehtivate materjalide jaoks vaata ITV0060

Järeleksam 2012 sügis

  • Järeleksam: 29. august (kolmapäev) kell 9.00-12.00, IT majas ruumis IT-140.

Exam and results

Two times available (choose one):

  • 1. June (Friday) at 9.00-12.00, room II-102
  • 8. June (Friday) at 9.00-12.00, room II-102

Tulemused 2012 eksamisession:

JT 3 TT 3 RT 5 MS 5 KI 4 RKA 4 RK 5 JK 2 AS 4 NV 5 MM 5

Time, place, result

Semester: spring
Grading: exam
Points: 3.5

Lectures: every Monday 17.45-19.15, room IT-137a
Practical work: Mondays on odd weeks 19.15-20.00, room IT-137a:
6 feb, 20 feb, 5 mar, 19 mar, 2 apr, 16 apr, 30 apr, 14 may, 21 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 2012. 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 about second lab will be available on beginning of April. Previous year lab can be seen here: Teadmised second lab 2012. You may also have a look at the 2011 version of the lab 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. Nende esituste omavahelise teisendamise oskus.

Ülesanne blocks worldist: esita mingi olukord, mingi reegel ja näita rakendamise tulemust. Hea oleks aru saada erinevatest võimalustest

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

RDF ja RDFS esitada loogikas faktide ja reeglitena.

Arusaamine RDFa põhiasjadest, oskus teha näidet. ??? Kallelt küsida.

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.

Arusaam fuzzy ja probabilistic logic erinevustest ja võimalikest rakenduskohtadest.

Indeksid: arusaam baasindekseerimis-ideedest ja B+ treest. Ntx ehitada B+ mingi väikese datahulga jaoks.


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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