Teadmiste formaliseerimine

teadmised

Name: Knowlege representation Code: ITI8700 Link: http://lambda.ee/Teadmiste_formaliseerimine Lecturer: Tanel Tammet Practice work: Priit Järv Contact: tanel.tammet@ttu.ee, 6203457, TTÜ ICT-426 Archives of previous years: 2019.

Aine õppimine piirangurezhiimis

Loengud

• Loenguajaks paneb õppejõud üles senisest detailsemad soovitused, et mida kuni järgmise loenguajani iseseisvalt lugeda.
• Lisaks teeb õppejõud kas väikese sissejuhatava video või jutu nende asjade kohta, mida lugeda antakse, ja see video/jutt läheb vastava loengu peatükki wikis.
• Esimene piiranguaja loeng/materjalid on 20 märtsi omad siin allpool NLP blokis.

Praktikumid

Priit andis praktikumide jaoks järgmised juhtnöörid:

Kodutööde esitamiseks avasime moodles lehe:

https://ained.ttu.ee/course/view.php?id=320

Kõik üliõpilased, kes on midagi juba esitanud, on ka ainesse enrollitud ja gruppidesse pandud. Kui keegi on kogemata vales grupis (või ei õpi enda teada üldse seda ainet :) siis andke palun teada.

Kodutööd esitage kirjalikult, vajalik info on olemas moodles. Õppematerjalid, näited jne on endiselt lambda wikis http://lambda.ee/wiki/Teadmiste_formaliseerimine

Diskussioon kodutööde kohta võiks toimuda esialgu siin:

https://ained.ttu.ee/mod/forum/view.php?id=11102

Katsetame selle formaadi sobivust. See toimib eelkõige siis, kui küsite ja kommenteerite julgesti.

Time, place, result

Lectures: Friday 10:15-11:45 room ICT-315
Practical work: Friday 12:00-13:30 room ICT-315
Practical work will give 40% and exam 60% of points underlying the final grade. The exam will consist of several small excercises.

The first practical work time on 31. January at 12:00 will be used for introducing the practice work. Some of the last lecture times at the end of the course may be used as additional seminars/labs.

Focus

The main focus of the course is on knowledge representation and reasoning (KR), a part of symbolic AI, on a spectrum from simple (representing and using knowledge in databases) to complex (meaning of sentences in natural language and commonsense knowledge).

The overall goal of the labwork to understand issues facing a task of building a natural language question answering system a la Watson and to see and experiment with existing parts of the solution.

The course contains the following blocks:

• Representing knowledge in the SQL and document databases and hybrids.
• Representing and using simple facts and rules in symbolic AI and logic.
• General-knowledge databases: wikidata, wordnet, yago, conceptnet, nell, cyc.
• Knowledge conveyed by natural sentences: both statistical methods like word2vec and logic-based methods.
• More about reasoners and question answering systems.
• Confidence and relevance.
• Context, time, location, events, causality.

Check out this description of the whole KR area.

Books to use

• Ca half of the course themes are covered in this book with freely accessible pdf-s.
• The freely accessible pdf of the handbook of knowledge representation gives a detailed and thorough coverage of the subject; far more than necessary for the course.
• Recommended background for NLP: Speech and Language Processing by Dan Jurafsky and James H. Martin. here is the 2nd edition and here the web page for the draft 3rd edition
• Get the background about automated reasoning from the book chapter pages 297-345 by Tanel Tammet and optionally more details from tge the set of course notes from Geoff Sutcliffe.

Observe that a noticeable part of the course contents are not covered by these books: use the course materials and links to papers, standards and tutorials provided.

Practical work

There are three labs. They are all steps in a single project: build a simple natural-language question-answering system about geography.

The labs have to be presented to the course teachers and all students present at labwork time.

The labs can be prepared alone or by teams of two or three people.

Each lab is evaluated independently and gives the same number of points. NB! When you do not meet the lab deadline, you will be awarded half of the points for this lab.

First lab

Please read about the task for the first lab. Deadline: 28. February. It is better to present it earlier to have more time for the following labs.

Second lab

Deadline: 3 april.

The task in the second lab is to

• write a small set of additonal, nontrivial rules for geography not present in Yago or Wordnet,
• actually answering simple questions using logic and rules with the help of a reasoner.

See Details of the KR second lab in 2020

Third lab

Deadline: 8. may.

Add a simple NLP component to the second lab to answer questions posed in natural language. I.e, the NLP component should convert a natural language query to logic usable by your system. Details and instructions will appear here later.

Block 1: knowledge in the SQL and document databases and hybrids.

Lecture: Intro and background, 31. Jan

Lecture materials:

• Question answering
• Background: sql, logic, NLP
• schemaless databases, RDF and RDFS started with this for practice work

Lecture: Nosql, json and rdf in databases. 7. Feb

• Nosql links and notes
• Json in Postgresql intro tutorial and official json datatype and especially functions and operations
• schemaless databases, RDF and RDFS looked at some parts of this
• notes about schema for rdf / first lab

Block 2: simple facts and rules in symbolic AI and logic

Lecture: simple rules in rdfs, logic and owl. 14. Feb

• Main lecture material: schemaless databases, RDF and RDFS looked at rdfs rule meanings in logic, sparql and owl.

Have a brief a look of these, no need to read thoroughly:

• Official w3c rdf and rdfs primer
• Official w3c owl primer

More resources:

• sparql implementations in wikipedia
• A short W3c page with links/resources for rdf, including a list of software tools.

Lecture: rules in logic with provers to answer questions, 21. Feb

These should be looked at in parallel:

• rules, logic, provers
• Reasoner examples with gkc 2020
• River example from lecture

Lecture: what the reasoner does, 28. Feb

We will look in detail into the same presentation:

• rules, logic, provers

and will explain details of the second practice work.

Block 3: General-knowledge databases

Lecture: looking into main large knowledge bases. 6. March

We will have a look at the goals, main content and differences between:

• wordnet see tptp axioms
• dbpedia, see also classes and dbpedia wiki
• wikidata
• BabelNet
• yago
• babelnet described in wikipedia
• conceptnet described in wikipedia
• nell website (sometimes down) and in wiki and a good paper.
• framenet described in wikipedia, see example
• cyc but just read wikipedia and see whitepaper also tptp small random selection of axioms
• adimen-sumo see sumo in wikipedia and tptp axioms
• tptp: a large set of axioms and problems in logic usable for automated reasoners
• schema.org: property markup vocabulary suggested by Google, Microsoft and others.

Lecture 6: Big annotation systems. 13. March.

First, big annotation systems:

• Google structured data on the webpage. See also Google knowledge graph in wikipedia.
• Facebook Open Graph markup on the webpage.

Importantly, see also:

• json-ld (check out playground examples) currently most popular triple representation language on top of json. see also wikipedia and w3c standard: for the latter, read the basic concepts chapter.
• rdfa triple markup language suited for html, see also wikipedia
• schema.org we looked at in the last lecture: property markup vocabulary suggested by Google, Microsoft and others.

Block 4: natural language and question answering

Lecture: Intro to NLP. 20 March and the following self-study week

A lot of reading this week. The main material is in the following chapters 25 and 26 of the Jurafsky and Manning book, the rest is relevant overviews/background. Lecture materials for the week:

• Intro to the relevant parts of the whole NLP field: nlp and watson: you may skip the watson part and read it from the following book chapter instead. These three wikipages give also useful details, but are not really necessary to read: Natural language processing, Knowledge extraction, Natural language understanding.
• In case you have not studied machine learning techniques for natural language processing, read chapter 6: Vector Semantics and Embeddings from the Jurafsky and Martin book: this is a good background for the next two chapters 25 and 26, but not strictly necessary: we will look more into vector semantics during the next week anyway.
• Read from the Jurafsky and Martin book: chapter 25: Question Answering
• Read from the Jurafsky and Martin book: chapter 26: Dialogue Systems and Chatbots and the supporting slides for conversational agents
• Not obligatory, but highly recommended and cool 15-minute philosophy podcast episode to listen: Jonathan Webber on deceiving with words

Lecture: vector representation of words

Lecture materials:

• First, listen to text vectorization episode from the linear digressions podcast.
• Second, listen to word2vec episode from the linear digressions podcast.
• Read the beginning (until you get tired ...) of the Vector Semantics chapter from the Jurafsky & Martin book.

Useful additional materials from (roughly) easier to more complex:

• word vectors: a short intro
• vector semantics part I presentation from the Jurafsky & Martin book for for chapter 6.
• vector representation: a short tutorial with code examples.
• word2vec tutorial from tensorflow
• vector semantics part II presentation from the Jurafsky & Martin book for chapter 6.

Probabilistic models:

• LDA explanation by Jordan Boyd-Graber

Also interesting to read:

• Aetherial Symbols by the ultimate machine learning guru aka "godfather of machine learning" Geoffrey Hinton
• Autocomplete using Markov chains

Block 5: More about reasoners and symbolic question answering

TPTP and the landscape of reasoners

• TPTP problem set for first order logic
• See how to run gkc on the Wordnet TPTP version
• CASC prover competition

Blocks world

Some axiom sets:

• PLA001-0.ax
• PLA001-1.ax

And some planning problems to solve from these axiom sets:

• PLA004-1.p
• PLA005-1.p
• PLA019-1.p

Also:

Alternative axiomatization

Block 6: confidence and relevance

Lecture: various classical ideas for discrete reasoning with uncertainty

We will look at some discrete ways:

• default logic: refresher from previous lecture.
• circumscription
• Autoepistemic logic: long tutorial Ijcai93.pdf

And start with numeric:

• Uncertain_prob_fuzzy.ppt‎ Intro to Bayesian and fuzzy reasoning.
• Notes about numerical confidence, popularity, etc measures

Lecture: various classical ideas for numeric reasoning with uncertainty

Classical numeric ways to reason about uncertainty:

• Uncertain_prob_fuzzy.ppt‎ Intro to both Bayesian and Fuzzy reasoning.

Some newer approaches:

• Vienna_tanel_4.pdf Additional examples and combining.
• Problog start with a tutorial

Block 7: context, time, location, events, causality.

To be filled.