Teadmiste otsing, formaliseerimine ja hoidmine

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.

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/

Materials to study for the exam

Declarative and procedural representations

Lecture material:

• Intro lecture: declarative and procedural representations

Databases: meaning and representation of facts

• relation of plain data in databases to logic & representing complex structures in databases
• key/value pairs and rdf, compared to the relational model

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

Lecture material:

• Rules and logic: refresher and as ppt

See also:

• Geoff reasoning course notes: just for background, no questions in exam
• planning and blocks world
• blocks world axiom/query examples from lecture

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

Lecture material:

• RDFS: rdf schema and as ppt

Understand RDF and RDFS:

• RDF wikipedia
• w3c RDF primer

Understand basics of owl:

• OWL wikipedia
• w3c OWL guide

RDFa, reading and using RDF data from web pages

Understand main parts:

• wiki intro
• Media:portaalidekoosvoime.ppt or as pdf Media:portaalidekoosvoime.pdf
• w3c rdfa primer

RDF relation to logic

Notes from lecture:

• Notes about RDF and OWL: logical meaning

Understand a bit of:

• Hayes & Guha Lbase

Semantics of ordinary relational base

• rdf tutorial addendum for logic and dbases

First order logic in query engines and planning

• Geoff reasoning course notes: just for background, no questions in exam
• planning and blocks world
• blocks world axiom/query examples from lecture

Data extraction

Web mining by Nell:

• Nell

Restricted english jms

• Attempto restricted english

Uncertain knowledge logic

• nonmonotonic logic
• default logic
• Reasoning with Conflicting Knowledge

Indexes

Traditional database indexes incl B+ tree:

• G. Molina lecture 4

Bitmap indexes:

• wiki

Hash indexes:

• wiki

Fulltext indexes:

• good intro and overview

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

• mccune paper

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:

• Intro lecture: declarative and procedural representations
• roboswarm example of combining dec and proc

For KR, declarative and procedural rep see:

• Short wiki overview of knowledge representation
• Long and philosophical MIT overview of knowledge representation
• Pointers to classical papers
• A Survey of Cognitive and Agent Architectures
• Classical KRL paper from 1977: Bobrow & Winograd

Web mining by Nell:

• Nell
• Richard Wang
• boowa extractor

Databases: meaning and representation of facts

Lecture material:

• relation of plain data in databases to logic & representing complex structures in databases
• key/value pairs and rdf, compared to the relational model

See also about databases and logic:

• Logic and Database Queries (readable)
• logic in databases workshop (complex stuff)

See also about RDF and related:

• rdf tutorial addendum for logic and dbases
• RDF wikipedia
• w3c RDF primer
• wiki intro
• Media:portaalidekoosvoime.ppt or as pdf Media:portaalidekoosvoime.pdf
• w3c rdfa primer
• rdfa and microformats deployment now
• microformats: a similar technology
• Hayes & Guha Lbase

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

Lecture material:

• Rules and logic: refresher and as ppt

Topics:

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

See also:

• Geoff reasoning course notes: just for background, no questions in exam
• planning and blocks world
• blocks world axiom/query examples from lecture

Various rule/logic languages

Lecture material:

• RDFS: rdf schema and as ppt
• Notes about RDF and OWL: logical meaning

Several languages:

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

See also:

• RDF wikipedia
• w3c RDF primer
• OWL wikipedia
• w3c OWL guide
• CL main page,
• Attempto restricted english

Probabilistic and nonmonotonic reasoning

See also:

• nonmonotonic logic
• default logic

Fast search of facts and rules using indexing

• G. Molina lecture 4
• G. Molina lecture 5
• good intro and overview
• mccune paper about term indexes

Learning

See also:

• intro
• robots/cars
• backpropagation
• good set of lecture materials
• another good full set of lecture materials
  • sh neural netw chapter