• studied at the University of Tartu, Estonia
• worked 2002-2013 at the University of Zurich, Switzerland
• mainly in projects related to controlled natural languages
  • REWERSE (2004-2008): using Attempto Controlled English (ACE) as a semantic web language
  • MOLTO (2012-2013): building a multilingual semantic wiki based on ACE and Grammatical Framework (GF)
  • joint work with: Norbert E. Fuchs (group leader) and Tobias Kuhn
• other interests:
  • grammar-based speech recognition applications
  • Estonian grammar in GF
  • biomedical text mining
• see also: http://google.com/+KaarelKaljurand

• semantic web (SW)
  • knowledge representation languages (OWL, SWRL) and reasoning
  • user interfaces for the SW (Protégé, Semantic MediaWiki)
• controlled natural languages (CNLs)
  • Attempto Controlled English (ACE)
  • its construction and interpretation rules, discourse representation
• ACE as a user interface language for the SW
  • translating between ACE and OWL
  • end-user tools: ACE View, AceWiki
• Grammatical Framework (GF)
  • GF as a framework for defining multilingual CNLs
  • resource grammar library (RGL)
• ACE-in-GF: ACE in ~20 languages
• AceWiki-GF: multilingual collaborative knowledge editor

• original vision by Berners-Lee, Hendler, Lassila (2001)
  • smart personal agents that book flights and negotiate hotel prices
  • concepts are denoted by URIs
  • ontologies relate concepts to each other
• simple knowledge representation language Resource Description Framework (RDF)
  • knowledge as a set of URI triples <subject,predicate,object>, e.g.
  • <john,like,mary>, <john,rdf:type,man>, ...
• highly-expressive ontology and rule languages
  • OWL (1st version from 2004, 2nd in 2009), SWRL (2004), RIF (2010)
  • fragments of first-order logic, OWL based on description logics (DLs)
  • core reasoning tasks have decidable algorithms
  • focus on reasoning efficiency (=> define efficient OWL profiles: EL, QL, RL)
• ~2008: web of linked open data ("little semantics goes a long way")
  • publish data as RDF and access it using the query language SPARQL
  • use very little OWL reasoning

• formal language for defining terms (URIs) via other terms
• entities: classes (aka concepts), individuals, object and data properties (roles, relations)
• simple and complex classes and properties
  • uri://.../animal # denotes the class of animals
  • uri://.../eats # denotes the relation of eating
  • (uri://.../animal AND uri://.../African) # "African animal"
  • InverseOf(uri://.../eats) # the relation of being eaten by something
• complex classes can use: negation, conjunction, disjunction, existential and universal restrictions, cardinality restrictions
  • (african AND animal) OR (NOT (InverseOf(eat) SOME animal))
• axioms (ontology is a set of axioms)
  • lion SubClassOf animal
  • (african AND animal) SubClassOf (InverseOf(eat) SOME lion)

• many different syntaxes
  • RDF/XML
  • OWL/XML
  • Turtle
  • functional syntax
  • Manchester syntax
• some based on the RDF idea of triples (RDF/XML, Turtle)
• some based on description logic style syntax (functional-style, Manchester)
• in this tutorial: functional-style and Manchester syntax, e.g.
  • SubClassOf(:country ObjectSomeValuesFrom(:border :country))
  • country SubClassOf border SOME country
• also in this tutorial: ACE-based syntax, e.g.
  • Every country borders a country.

The meaning of OWL elements is defined via set theory.

Unique Name Assumption (UNA): differently named individuals are necessarily different instances of the domain. OWL does not make the UNA.

Open World Assumption (OWA): unstated facts are not assumed to be false. OWL makes the OWA.

Thus, OWL is different from some database/knowledgebase languages (SQL, Prolog) where a missing assertion means that it is false rather than unknown.

T-Box

• terminological statements / universal statements (universal quantification in first-order logic), e.g.
  • Every EU-country is a country.
  • If X border Y then Y borders X.
• conceptionally complex (domain, range, reflexivity, inheritance, ...)
• usually small and static

A-Box

• instance data, e.g.
  • Switzerland is not a EU-country.
  • Switzerland borders Germany.
• conceptually much simpler
• usually very large and often changing

• rule-like statements with explicit variables
  • OWL: eu-country SubClassOf country
  • SWRL: IF eu-country(?X) THEN country(?X)
• more expressive extension of OWL
  • OWL does not support unrestricted variable sharing
  • SWRL: IF man(?X) AND car(?Y) AND own(?X, ?Y) THEN like(?X, ?Y)
  • English: Every man that owns a car likes the car.
  • OWL approx.: (man AND own SOME car) SubClassOf like SOME car
  • SWRL representation of OWL approx.: IF man(?X) AND car(?Y) AND own(?X, ?Y) THEN like(?X, ?Z) AND car(?Z)
• still a fragment of first-order logic, but reasoning is not decidable

• reasoning helps to detect modeling errors during development time
  • e.g. necessarily empty classes should be avoided
• reasoning can be used during runtime to classify new instances
• reasoning tasks
  • is the ontology consistent?
  • which axioms does the ontology entail? (e.g. classification = entailment of simple SubClassOf-axioms)
  • is a class satisfiable (i.e. can it contain individuals?)
  • which (smallest) subsets of the ontology cause a class to be unsatisfiable?
  • which classes and individuals does the given class contain? (DL Query)
• reasoning is decidable, i.e. will always solve the task in finite time (although sometimes very slowly)
• many off-the-shelf reasoners: Pellet, HermiT, FaCT++, ...
• integrated into end-user tools (Protégé, Semantic MediaWiki, AceWiki, ...)

(written in ACE for easier readability)

Ontology

Every country is a territory.
If X borders Y then Y borders X.
If X borders something then X is a country.
Germany borders Switzerland.

Entailments

Switzerland borders Germany.
Switzerland is a country.
Switzerland is a territory.
Germany is a country.
Germany is a territory.
It is false that Germany does not border Switzerland.
...

Unknown truth value

Switzerland is not Germany. # no UNA in OWL
Switzerland borders exactly 1 territory.
Switzerland borders itself.
...

• SNOMED Clinical Terms
  • large (300k concepts) collection of medical terms, with NL lables and formal relations
  • multilingual lables on concepts, currently English (~1m labels), Spanish, Danish, Swedish
  • EL fragment of OWL (excludes disjunction, negation, cardinality constraints, ...)
  • Common-cold SubClassOf causative-agent SOME Virus
  • used >50 countries
• content management with semantic wikis
  • e.g. Semantic MediaWiki, OntoWiki
  • small subset of OWL considered for reasoning
• DBpedia
  • structured part of Wikipedia (infoboxes)
  • background ontology (SubClassOf, domain, range)
  • multilingual

• semantic web is for both machines (agents) and humans
• humans require a user-friendly language to interact with the SW
• RDF's triple-based data model is very simple
  • does not directly allow for a user-friendly syntax for complex structures
  • originally was written in RDF/XML (unnecessarily verbose)
• OWL (version 2009) syntax is much more elegant (based on Description Logics)
  • still remote from natural language
  • lots of syntactic sugar for various types of SubClassOf-constructs
• separate ontology, rule, and query languages
• little focus in the SW community on UI issues, see e.g. D. Karger ESWC 2013 keynote "A Semantic Web for End Users"
  • http://videolectures.net/eswc2013_karger_semantic/

• open-source and widely used OWL editor (actually predates OWL)
• user interface
  • sub class tree hierarchy with drag-and-drop
  • otherwise reflects the OWL axiom types 1-to-1
  • integrates Manchester OWL Syntax (MOS) for expressing classes and axioms
• focus on T-Box editing
• integrates reasoners
  • query answering
  • entailment explanation
• WebProtégé: simpler web-based editor with focus on collaborative editing (e.g. revision history support)

Smallest subset that explains why mad-cow is an empty class, indicating that the ontology contains a modeling error.

Wiki

• user-friendly collaborative environment for knowledge management
• content typically unconstrained natural language, therefore not easily automatically processable
• powered by software, e.g. MediaWiki, best known example: Wikipedia

Semantic wiki = wiki + formal semantics

• provides a richer query language + improves consistency by reducing redundancy: the same information stated only once
• content typically in natural language enriched with typed links (i.e. RDF triples)
• user interface
  • wiki-style: plain text with embedded meta information
  • forms for filling templates
• software: Semantic MediaWiki, Freebase, ...

• extension of MediaWiki / Wikipedia
• add: "semantic annotations" structured formats for
  • attaching attributes to concepts
  • representing lists, tables, etc.
  • representing maps, timelines, calendars, etc.
• queries that automatically generate wiki pages
• supports subset of OWL for query answering
  • subclass, subproperty, inverse property, equality
  • excludes e.g.: transitivity, domain/range, cardinality restrictions
• focus on A-Box editing

Example: sentence in an article Berlin is entered as

Berlin is the capital of [[Is capital of::Germany]]
and has a population of [[Has population::3,292,365|3.3 million]].

and is rendered as

Berlin is the capital of Germany
and has a population of 3.3 million.

and adds two triples to the underlying model

<Berlin, Is_capital_of, Germany>
<Berlin, Has_population, 3292365>

• RDF, OWL, ... keywords are in English
• most ontologies are written with English entity names
• recommendation (in biomed ontologies) to name entities in a language-neutral way (with ID codes)
• entity and axiom annotations provide a way to include free-form natural language in the ontologies
  • e.g. via rdf:label with the ISO language tag
• The Lexicon Model for Ontologies (lemon)
  • new proposal for annotating ontologies with linguistic information
  • each entity is mapped to its corresponding lexical entries specifying the word forms and part of speech categories (for different languages)

• wikis: multilingual articles but interlinked only at the document level
• WikiMedia: proposal for multilingual Wikipedia
  • http://meta.wikimedia.org/wiki/A_proposal_towards_a_multilingual_Wikipedia
• AceWiki-GF: a prototype for creating ontologies in a multilingual way

• the original semweb paper
  • The Semantic Web. T Berners-Lee, J Hendler, O Lassila. Scientific American (2001)
• OWL specification: http://www.w3.org/TR/owl2-overview/
• Protégé: http://protege.stanford.edu/
• Semantic MediaWiki: http://semantic-mediawiki.org/
• Dagstuhl Seminar 12362 (2012): The Multilingual Semantic Web
  • http://www.dagstuhl.de/12362
• lemon: http://lemon-model.net/

• motivation
  • natural language with tool support, e.g. auto-completion, paraphrasing, consistency checking, question answering, translation
  • wide-coverage parsers achieve only ~90% accuracy, and on simpler tasks (e.g. detecting subjects and objects)
• formal syntax, but fragment of a natural language
• formal semantics
  • ambiguity handling
  • translations to other (natural or formal) languages
  • reasoning, paraphrasing, etc.
• many languages with different goals and formal properties
  • see Tobias Kuhn. A Survey and Classification of Controlled Natural Languages, Computational Linguistics
• well-known example: Attempto Controlled English (ACE)
• frameworks: Grammatical Framework (GF)

• subset of English
• controls ambiguity and synonymy
  • A dog hates a cat. == There is a dog that hates a cat.
  • A dog hates a cat. =/= Every dog hates a cat.
  • Every dog hates a cat. == If there is a dog then the dog hates a cat.
• end-user documentation: construction and interpretation rules, as restrictions of English
  • construction rules, interpretation rules, ...
  • easy to learn
• well-defined translations to first-order logic, OWL, ...
• tool support for end-users
• developed in the Attempto project at the University of Zurich, lead by Norbert E. Fuchs

• subset of natural English
  • conjunction (and), disjunction (or), negation (no, not, it is false that), if-then, ...
  • anaphoric references: pronouns (he, it), definite noun phrases (the man), variables (X, Y123)
  • quantifiers: every, no, at least 3, ...
  • content words: proper names, common nouns, verbs, adjectives, adverbs
• grammar is fixed, but users can change content words
• deterministic ambiguity handling
  • anaphora resolution (France borders Spain and it borders Portugal.)
  • quantifier scope (Every country borders a country.)
  • attachment (Every EU-country borders a country that is a EU-country and is a NATO-country.)

English: Koalas eat eucalyptus leaves.

Violates the ACE construction rules:

• every noun must have a determiner ('every', 'a', 'at least 2', ...)
• multi-word nouns are not allowed

Correct ACE:

• Every koala eats an eucalyptus-leaf.
• If a koala eats something X then X is an eucalyptus-leaf.
• ...

English: Every EU-country borders a country that is a EU-country and is a NATO-country.

The attachment differences must be expressed in a lexically different way in ACE:

• Every EU-country {borders a country that is a EU-country} and {is a NATO-country}.
• Every EU-country borders a country {{that is a EU-country} and {that is a NATO-country}}.

(The {curly brackets} are not part of ACE. They are used here to denote the scopes.)

The_Mediterranean_Sea is a sea. Every country that does not border a sea is a landlocked-country. Switzerland does not border the sea.

• DRS
  • interpreted as a first-order logic formula
  • ACE content words map to atomic conditions
  • ACE function words (and, or, not, if-then) introduce various complex DRS "boxes"
  • variables identify content words
  • variables denote anaphoric references to nouns, the DRS box structure reflects accessibility constraints on references

It is possible to express the same meaning (the same DRS) in syntactically different ways, e.g. every = if-then:

• Every koala eats an eucalyptus-leaf. # M1
• If there is a koala then the koala eats an eucalyptus-leaf. # M1
• Everything that a koala eats is an eucalyptus-leaf. # M2
• If a koala eats something X then X is an eucalyptus-leaf. # M2

relative clause = coordination

• Every mammal that is endemic-to Australia is a marsupial or is a .... # M3
• If a mammal is endemic-to Australia then the mammal is a marsupial or is a .... # M3
• If there is a mammal and the mammal is endemic-to Australia then the mammal is a marsupial or is a .... # M3

(M{1,2,3} indicate sentences with the same DRS.)

Content words are user-defined. They are classified by their word class and have one or more surface forms.

• nouns: 2 forms: singular: man, plural: men
• proper names: 1 form: singular: John
• verbs
  • intransitive (wait), transitive (eat), ditransitive (give)
  • 3 forms: 3rd person finite: eats, infinite: eat, past participle: eaten
• adjectives
  • intransitive (rich), transitive (fond-of)
  • 3 comparison forms (rich, richer, richest)
• adverbs (3 comparison forms)

The lexicon allows the definition of aliases but does not otherwise provide any semantics, e.g. statements like "Every man is a human." can be made only at the level of ACE.

• ACE parser (APE): maps ACE to DRS
• DRS verbalizer: maps DRS to ACE
  • the roundtrip: ACE -parser-> DRS -verbalizer-> ACE usually results in a syntactically different ACE text thanks to syntactic sugar
• DRS translators: map DRS to
  • OWL/SWRL
  • TPTP
  • ...
• look-ahead editor for a subset of ACE
• native ACE reasoner: RACE
• OWL verbalizer: maps OWL to ACE
• end-user ACE / SW ontology editors:
  • ACE View
  • AceWiki

• user-friendly language because based on English
  • standard English instead of various formal notations
  • easy to read (and read out-loud)
  • easy to write (?)
• single language instead of 3 different languages for ontologies, rules and queries
• different (more natural) motivation for the syntactic sugar
• usage:
  • verbalizing existing ontologies
  • writing new and modifying existing ontologies
  • querying existing ontologies
  • presenting entailments and entailment explanations

• OWL naming conventions for entities
  • usually: nouns for classes, verbs for properties
  • sometimes contain complex structure: nonNormal, MaleOrFemalePatient
• simple one-to-one mapping of axioms to sentences
  • other approaches also reorder/combine axioms

• Manchester OWL Syntax (MOS)
  • concise notation for OWL
  • part of the OWL (2009) standard
  • used in editors like Protégé
• Tobias Kuhn. The Understandability of OWL Statements in Controlled English (2013)
  • compare the ACE and MOS representation of wide variety of OWL axiom types
  • evaluation with 64 users (students, but not of computer science)
  • ask subjects to look at diagrams and evaluate corresponding ACE and MOS statements as true or false
  • result: ACE is easier to learn and understand
  • no evaluation of "writability"

• need to support word-forms (e.g. in the lexicon editor):
  • 2 for nouns: man, men
  • 3 for verbs: eats, eat, eaten by
• English-specific
  • can be misleading (if interpreted as English, and not ACE)
  • non English speakers might prefer a more language-neutral notation
• harder to implement tool support (parser, verbalizer, etc.) for ACE than for MOS
  • covering a wide variety of programming languages

• ACE-based OWL/SWRL editor, viewer, query interface
• plug-in for Protégé 4+
• provides set of ACE "views" to the ontology
• integration with other Protégé views
  • changes done via standard Protégé views are reflected in the ACE views
  • motivation: sometimes graphical UI is simpler to use than plain text
• integrates both ACE->OWL/SWRL and OWL->ACE
• ACE-based entailment explanation

Presenting the complete ontology as a sortable/searchable set of ACE sentences.