computing and linguistics: a cognitive approach

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O N T O P E D I AThe Identity of Everything

Computing and LinguisticsA Cognitive Approach

or, Computing “As We May Think”

Steve [email protected]

University of Oslo, 2009-04-21

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Today’s “research questions”

How can linguistics – and in particular cognitive linguistics – inform our work with Topic Maps?

Can Topic Maps contribute in any way to the cognitive linguistics project?

Plan of action– I tell you about Topic Maps (conceptual model)– I draw some parallels with natural language– You correct me, elaborate and suggest new directions

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Relevance to you as linguists

As users of the technology– organizing data collected in your research

As consultants to users of the technology– e.g. universities, government agencies, private enterprise

As contributors to the standard– clarify some of the cognitive issues, establish best

practices, help extend the standard As lobbyists to the University of Oslo

– if you think the new UiO web site should be based onTopic Maps, please make your views known to the project group: http://www.admin.uio.no/prosjekter/nyuioweb/

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Relevance in general

1. We need to organize information in a new way– The summation of human experience is being

expanded at a prodigious rate, and the means weuse for threading through the consequent mazeto the momentarily important item is the sameas was used in the days of square-rigged ships.(Vannevar Bush, As We May Think, 1945)

2. We need new ways of managing knowledge– In today’s global knowledge economy, knowledge is

the key asset in many organizations... Topic Maps makes major contributions in both areas

– See the use cases presented at recent Topic Maps conferences http://www.topicmaps.com

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What is Topic Maps?

An ISO standard for computer-based informationand knowledge management

– “Provides the ability to control infoglut and share knowledgeby connecting any kind of information from any kind of source based on its meaning”

A “semantic technology”– Cf. Semantic Web (RDF, OWL)– A form of knowledge representation (primitive perhaps, but useful)

Widely used for web-based delivery of information– Plus: Information Integration, eLearning, Business Process

Modeling, Product Configuration, Business Rules Management, Asset Management, Knowledge Management, …

http://www.digi.no/808475/god-web-design-er-emnekart

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The problem with computing...

...is that it’s inside-out! People used to think the sun

revolved around the earth– Copernicus’ heliocentric theory

turned this idea inside out and revolutionized our understandingof the universe

Today we face a similar situation in computing

– Our computing universe has computers, applications and documents at the centre

– The concepts that our information is about are somewhere in outer space where they can’t be found

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A subject-centric revolution

This is wrong, because it does not reflect how humans think

– We think in terms of interrelated concepts (or subjects)

– Subjects are what interest us, not documents or applications

– And so subjects must be givencentre stage

We need a subject-centric revolution

– This has ramifications for every aspect of human-computer interaction, including user interfaces, operating systems, file systems, etc.

– Consider the typical user desktop...

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Today our Today our desktops are desktops are

application-application-centric and centric and document-document-

centriccentricIcons represent Icons represent

applicationsapplications and and documentsdocuments

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topic maps

tm2008

bantu semantics

LING 2110

INF 2820rana

keynote

OOXML

K185 gambiaopera

janacek

bayreuthhåkon

TM2008Topic pageEmailsDocumentsWeb pagesCopy PSIΨ

Why can’t they be subject-centric, with icons that represent the subjects we are interested in?

With links between related icons? And with context menus that allow us to find

everything related to a particular subject?

TM2008Topic pageEmailsDocumentsWeb pagesCopy PSIΨ

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Computing “As We May Think”

Bush’s solution to information overload:– Organize information “As We May Think”, i.e. associatively

His vision spawned the hypertext movement– Doug Engelbart, Ted Nelson, Bill Atkinson, Tim Berners-Lee, ...– The World Wide Web is its greatest triumph to date

But hypertext does not correspond to how we think– Our heads are not full of millions of interlinked documents– They are full of “interlinked” concepts (or subjects)

Topic Maps provides a close approximation to this– It is a technology that is based on cognitive principles

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Background to Topic Maps

Emerged from the SGML community in 1990’s– Use case: How to merge (digital) back-of-book indexes– Some input from library science– No input from linguists– Precious little input from computer scientists before 2001– Most of the SGML community came from the humanities

ISO 13250 first published in 2000 (recently revised)– A model for representing knowledge organization structures

(indexes, glossaries, thesauri, encyclopedias)– Plus interchange syntax, query language, constraint language, ...

Widely adopted in Norway (esp. public sector)– And gaining ground elsewhere

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The TAO of Topic Maps

The core concepts are derived from the back-of-book index

Extended and generalized for use with digital information

Consider a two-layer model consisting of

– a set of information resources (below)– a “knowledge map” (above)

This is like the division of a book into content and index

knowledge layer

information layer

(INDEX)

(CONTENT)

Callas, Maria …………………… 42Cavalleria Rusticana … 71, 203-204Mascagni, Pietro Cavalleria Rusticana . 71, 203-204Pavarotti, Luciano ……………… 45Puccini, Giacomo ………. 23, 26-31 Tosca ………………. 65, 201-202Rustic Chivalry, see Cavalleria Rusticanasingers ………………………. 39-52 baritone ………………………. 46 bass ……………………….. 46-47 soprano ……………… 41-42, 337 tenor ………………………. 44-45 see also Callas, PavarottiTosca ………………… 65, 201-202

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(1) The information layer

The lower layer contains the content– usually digital, but need not be– can be in any format or notation or location– can be text, graphics, video, audio – whatever

This is like the content of the book to which theback-of-book index belongs

information layer(CONTENT)

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(2) The knowledge layer

The upper layer consists of (typed) topics and associations– Topics represent the subjects that the information is about

Like the list of topics that forms a back-of-book index

– Associations represent relationships between those subjects Like “see also” relationships in a back-of-book index

knowledge layer

composed by

born in

composed by

Puccini

Tosca

Lucca

MadameButterfly

(INDEX)

Domain:Italian opera

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Occurrences link the layers

Occurrences represent relationships between information resources and the subjects that they are “about”

The links (or locators) are like page numbers in a back-of-book index

Occurrences canalso be typed (e.g.bio, map, synopsis)

knowledge layer

information layer

Puccini

Tosca

Lucca

composed by

born in

composed by

MadameButterfly

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Summary of core concepts

A pool of information or data, and

information

Associations– representing relationships between

subjects

composed by

born in

composed by

Occurrences– links to information that is somehow

relevant to a given subject

= The TAO of Topic Maps

a knowledge layer consisting of

knowledge

Topics– a set of topics representing the key

subjects of the domain in question

Puccini

Tosca

Lucca

MadameButterfly

Let’s look at some TAOsin the Omnigator…

Plus: topic types, association types, occurrence types – each of which are represented by topics...

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About the Omnigator

A free topic map browser from Ontopia– Download from http://www.ontopia.net (part of “OKS Samplers”)– Java-based, runs on any computer

Completely generic– Not optimized for any particular ontology– Display and navigate any conforming topic map

A teaching aid– Not designed for end-users (no attempt to hide technical jargon)– Also used for prototyping and debugging

Not to be used for most real world applications!– These require custom interfaces based on a specific ontology– (see http://www.topicmaps.com for a good example)

http://www.ontopia.net/

http://www.topicmaps.com/

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Omnigator interface

current topic

multiple (typed) names

topic type(s)

typedoccurrences (internal and external)

typedassociations

Demo

a typical topic page

identifier(s)

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Typing topics revisited

Basic building blocks of the TAO model are– Topics: e.g. “Puccini”, “Lucca”, “Tosca”– Associations: e.g. “Puccini was born in Lucca”– Occurrences: e.g. “http://www.opera.net/puccini/bio.html

is a biography of Puccini”

Each of these constructs can be typed– Topic types: “composer”, “city”, “opera”– Association types: “born in”, “composed by”– Occurrence types: “biography”, “street map”, “synopsis”

All such types are also topics– The set of typing topics constitutes an ontology

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Capabilities of the TAO model (1)

Represent subjects explicitly– Topics represent the “things” users are interested in

Capture relationships between subjects– Associations provide user-friendly navigation paths to information

(navigation “as we may think”)– Associations also promote serendipitous knowledge discovery

through browsing

Make information findable– Topics provide a “one-stop-shop” for everything that is known

about a subject (collocation of information and knowledge)– Occurrences allow information about a common subject to be

aggregated across multiple systems, irrespective of location

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Capabilities of the TAO model (2)

Represent taxonomies and thesauri– Associations can (also) represent hierarchical relationships– With Topic Maps you can have multiple, interlinked hierarchies

and faceted classification

Transcend simple hierarchies– Rich associative structures capture the complexity of

knowledge and reflect the way people think

Manage knowledge– The topic map is the embodiment of “organizational memory”– Provides a structured way to capture people’s knowledge of

things, events, relationships, etc.

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Beyond the TAO

Formal data model– Topic maps can be queried, e.g.– Give me all composers that composed operas that were

based on plays that were written by Shakespeare Interchange syntax

– Topic maps can be interchanged– Increased reuse = added value

Robust identity model– Topic maps can be merged– Potential to federate knowledge

Scope– Topic maps can capture context

Reification– Topic maps can express different levels of detail– Similar to scaling in cartography

For more details, see Pepper 2009

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Break – any questions so far?

After the break:Topic Maps and natural language – towards a linguistic perspective

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Parallels with natural language

Basic grammatical classes Nouns and verbs Nominals and nouns Clauses and verbs Valency Semantic roles Categories and schemas Hyponymy Synonymy and homonymy Nominalization Grounding / co-reference Information structure

TAO model Topics and associations Topics and their types Associations and their types Arity Association roles Typing topics Type hierarchies Naming Reification Subject identity / collocation Navigation

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Basic principles, basic classes

In elementary school, I was taught that a noun is the name of a person, place, or thing. In college, I was taught the basic linguistic doctrine that a noun can only be defined in terms of grammatical behavior, conceptual definitions of grammatical classes being impossible. Here, several decades later, I demonstrate the inexorable progress of grammatical theory by claiming that a noun is the name of a thing.(Langacker 2008)

The basic grammatical classes are nouns and verbs– They prototypically profile things and relationships

They correspond to topics and associations

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Grounding

“Grounding is characteristic of the structure referred to in CG as nominals and finite clauses. More specifically, a nominal or a finite clause profiles a grounded instance of a thing or process type.”

“A noun designates a type of thing, and a verb a type of process.”

“A nominal or a finite clause profiles a grounded instance of a thing or process type.”

Nominal grounding (determiners and quantifiers)– the, this, that, some, a, each, every, no, any

Clausal grounding (mood and tense)– -s, -ed, may, will, should

Langacker 2008: 259ff (esp. 264)

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Nouns and nominals

Topic types represent classes of topics– Conceptual “groupings of things”, e.g. composer, opera, city, ...– They correspond to Langacker’s nouns (“types of thing”)

However, topics can have multiple names– (This is how we handle synonymy and multilingualism)– In one sense it is topic names that correspond to nouns

Topic instances represent individual subjects– They correspond to Langacker’s nominals (“instances of types”)– Their names are typically proper nouns, e.g. Puccini, Tosca, Lucca

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Verbs and clauses

Association types represent classes of relationships– They correspond to Langacker’s verbs (“types of process”)– (Often named accordingly, e.g. born in, composed by, killed by, ...)

Individual associations represent specific relationships– They correspond to Langacker’s clauses (“instances of processes”)– e.g. Puccini was born in Lucca; Tosca was composed by Puccini

Langacker distinguishes processes (temporal) and non-processualrelationships (non-temporal). The latter are (prototypically) profiledby adjectives, adverbs, prepositions, and participles. This distinctionis not made explicitly in Topic Maps.

Note: There are two predefined association types– type-instance (the relationship between a topic and its type)– supertype-subtype (a relationship between types, see Hyponymy)

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Valency

Associations can involve one, two or more topics– Binary associations, e.g. Puccini composed Tosca, are most

common and correspond to transitive verbs– Ternary associations, e.g. Tosca killed Scarpia with a knife, can

correspond to ditransitive verbs– Unary associations, e.g. Turandot was unfinished, correspond

(sort of) to intransitive verbs (or binary properties)

The arity of an association– Corresponds to the valency of a verb

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Semantic roles

An association does not have “directionality”

Instead of direction, Topic Maps uses roles– Roles are classified by type– Role types specify the nature of each topic’s involvement

in the relationship. They correspond to semantic roles.– (Role types are also topics)

Role types are different from topic types...

Puccini Toscacomposed

composed by

composer work

RDFTopic Maps

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Roles and types

T R APuccini

R TTosca

T T Tcomposer workcomposed

Tcomposer

Topera

The role type can be– the same as the role playing topic’s topic type (composer = composer)– a supertype of the topic type (work > opera)– a subtype of the topic type (teacher < person)– a subtype of the topic type’s supertype (source < work)

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Association roles Semantic roles

Italian Opera Topic Map– composed: composer, work– born in: person, place– appears in: character, work– based on: source, result– revision of: source, result– part of: part, whole– exponent of: person, style– located in: container, containee– pupil of: teacher, pupil

Association roles tend to be much more specific– Variable practice – as yet no established conventions– Might (cognitive) linguists have something to offer here?

(Frawley 1992)– (logical actors)

agent, author, instrument– (logical recipients)

patient, experiencer, benefactive

– (spatial roles)theme, source, goal

– (non-participant roles)locative, reason, purpose

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Naming of associations

Intuitive naming requires flexibility– i.e. multiple AT names that change

depending on the “direction” of the association Puccini was born in Lucca Lucca was the birthplace of Puccini

Alternative CG view– Naming should be based on whether

the agent or the theme is in focus The focus becomes the trajector

– Point of focus = Current topic

Some strategies...

Voice-based– Active / passive forms of the verb

composedVa / composedVp by

– Works well in SVO languages. Less satisfactory with SOV.

Role-based teacherN of/pupilN of

Nominalization composition

– Tends to be used by Japanese, Koreans (and Germans??)

Combinations bornV in / birthplaceN of partN of/consistsV of

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Categories and prototypes

Topic types define categories of things– But are they Aristotelian or prototypical categories?

Aristotelian– Category membership is binary– All instances are equally representative. No standard notion of “similarity”.

Prototypical– Not defined by “necessary and sufficient conditions” (cf. OWL)

The decision is up to the conceptualizer (a.k.a. topic map author)

– A topic can have more than one type Boïto is a composer and a librettist

– The same topic can be a topic type and a role type e.g. Puccini is a composer; Puccini plays the role of composer in …

– Should we establish conventions for goodness of example? Could be useful in automated classification

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Schemas and constraints

Other types can also be said to define categories– association types, (occurrence types, name types, role types)

But these are more schematic (in the CG sense)– Schemas are “abstract templates obtained by reinforcing the

commonality inherent in a set of instances”(Langacker 2008, p.23, in the context of grammatical rules)

Rules can be defined as templates and constraints:

T R APuccini

R TTosca

T T Tcomposer workcomposedT

composer

Topera

“Puccini composed Tosca”

The composer Puccini plays the role of composer in the “composition” relationship in which the role of work is played by the opera Tosca.T

AGENT

TTHEMEelaboration sites

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Hyponymy

Topic Maps has two predefined association types:– type-instance (relationship between a topic and its type)– supertype-subtype (relationship between the denotations of

a hyponym and its hyperonym)

Mammal

Primate Canine

HumanChimp WolfDog

Steve Ron

LEGEND

types

instances

supertype-subtype

type-instance

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Synonymy and homonymy

Synonyms– One subject, multiple names– In thesauri: USE and USED FOR

TMs are subject-centric– A topic can have multiple names– Names can be typed

Typical name types:– nickname, synonym, alternate name

– Context can be expressed using scope Typically names in different natural languages– composer, komponist, 작곡가 , ...

– Names can also have “variants” Often used to capture orthographic variation:– Tchaikovsky, Чайко́вский, Tsjajkovskij,

Tschaikowski Also useful for sort names, pronunciation, etc.

Homonyms– One name, multiple subjects– In thesauri: problematic

TMs are based on identifiers– Same name can be used by more than

one topic– Disambiguation in UI is left to the

application– Two main disambiguation strategies

Default: qualify by type, e.g.– Tosca (opera) vs. Tosca (character) Fallback: qualify by some other

relationship, e.g.– Paris (France) vs. Paris (Texas)– La Bohème (Puccini) vs. La Bohème

(Leoncavallo)

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Nominalization

(A topic map consists of assertions about subjects) Assertions are made using statements:

– names, e.g. a certain subject has the name “Tosca”– associations, e.g. “Tosca is set in Rome”– occurrences, e.g. “http://en.wikipedia.org/wiki/Rome is a web

page about Rome” Any statement can be reified

– Reification results in a topic that has the same referent as the reified statement

– e.g. Tosca is set in RomeA The setting of Tosca in RomeT

– The (new) reifying topic can have names and occurrences,and it can play roles in associations

Derivation of nouns from other words, including verbs, adjectives etc.

e.g. meetV meetingN

http://en.wikipedia.org/wiki/Rome

http://localhost:8080/omnigator/models/topic_nontopoly.jsp?tm=ItalianOpera.ltm&id=tosca

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Subjects and topics

Topics represent subjects– the topic is the representation– the subject is the referent

Or, in Saussure’s terms– signifiant and signifié

A subject can be anything:A subject is any “thing” whatsoever, whether or not it exists or has any other specific characteristics, about which anything whatsoever may be asserted by any means whatsoever.

Is the topic/subject pairing a symbolic assembly?

A subject inthe real world

T

A topic in the computer domain

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Co-reference and collocation

Grounding singles out referents and enables co-reference– between speaker and listener– across a sequence of utterances

In Topic Maps the central objective is collocation– By definition, each topic represents a single subject (one subject per topic)– A topic is intended to be a point of collocation for everything that is known

about a particular subject– Therefore the goal is to have only one topic per subject

To achieve that we need to know which subject a topic represents

– (This is sometimes referred to as the “intentionality” of the relation between a symbol and its referent.

– We call it subject identity.

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Subject identity

The identity of a subject is expressed using globally unique identifiers called subject identifiers

– If two topics share a subject identifier, they are deemed to represent the same subject and must be merged

SUBJECTS

TOPICS

MadameButterfly

Tosca

Lucca

Puccini

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The subject is identified by a URL

• The URL is called asubject identifier

Subject identifiers

GiacomoPuccini

topic

http://psi.ontopedia.net/Giacomo_Puccini

subject identifier

The URL is the address of a web page

• The web page describes the subject such that a human can know what subject is referred to

• This web page is called a subject descriptor

Giacomo PucciniItalian composer, b. Lucca 22nd Dec 1858, d. Brussels, 29th Nov 1924. Best known for his operas, of whichTosca is one of the most popular and well-known.

subject descriptor

http://psi.ontopedia.net/Giacomo_Puccini Humans use the descriptorBy inspecting the web page the person responsible for assigning the identifier can be sure that it does not refer to, say, Giacomo’s grandfather Domenico (who was also a composer of operas)

Machines use the identifierThe link is not resolved. Instead simple lexical comparison is used. If the strings are identical, the subject is deemed to be the same and the topics are merged.

subject

Is the subject identifier/ subject descriptor pairing a symbolic assembly?

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Information structure

Intuitive navigation is a key feature of Topic Maps But what is its cognitive basis?

– I claim that it corresponds to the way we think (i.e., associatively)– Can linguistics back up this claim?

topic vs. comment in linguistics (Bussmann, 487)– “Analysis of sentences according to communicative criteria into the

topic (what is being talked about) and the comment (what is being said about the topic)”

– “Analysis of utterances according to the communicative criteria of given/known information vs. new information”

– Cf. theme vs. rheme in Halliday’s functional grammar

Consider our earlier tour of Italian opera...

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Navigation as narrative

Giacomo Puccini was a composer. He was born in Lucca in 1858.

Lucca is a city, located in Italy. It was the birthplace of Puccini and Catalani.

Catalani was a composer who composed 5 operas. He died in Milan.

Milan is the home of La Scala, which was the venue for many premiére performances, including that of Madam Butterfly.

Madam Butterfly is set in Nagasaki, which is located in Japan.

Japan is (also) the setting for Iris, [which is] an opera [which was] composed by Mascagni, who was a pupil of Ponchielli who was (also) the teacher of Puccini...

Giacomo Puccini was a composer. He was born in Lucca in 1858.

Lucca is a city, located in Italy. It was the birthplace of Puccini and […] Catalani.

Catalani was a composer who composed 5 operas. He died in Milan.

Milan is the home of La Scala, which was the venue for many premiére performances, including that of Madam Butterfly.

Madam Butterfly is set in Nagasaki, which is located in Japan.

Japan is (also) the setting for Iris, [which is] an opera [which was] composed by Mascagni, who was a pupil of Ponchielli who was (also) the teacher of Puccini...

THEME: new theme continuing themeRHEME: predicate with potential new theme

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“Now! …. That should clear up a few things

around here!”

Discussion

Questions, comments, corrections?– What have I missed? Where else should I look?

What might linguists contribute?– A better understanding of the nature of roles?– Approaches to representing temporal knowledge?– ...

Can Topic Maps inform linguistics?– After all, it is a technology that captures (some

degree of) (some form of) knowledge– It seems to have a reasonable cognitive basis– It emerged through usage (librarians, indexers, etc.)– And last but not least, it works!

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References

Bussman, H. Routledge Dictionary of Language and Linguistics (London 1996)

Frawley, W. Linguistic Semantics (Hillsdale 1992)

Langacker, R. Cognitive Grammar (Oxford 2008)

Pepper, S. Italian Opera Topic Map – http://www.ontopedia.net/ItalianOpera

Pepper, S. “Topic Maps” in Bates, M.J. and Maack, M.N. (eds) Encyclopedia of Library and Information Sciences (CRC Press, forthcoming 2009)

– http://www.ontopedia.net/pepper/papers/ELIS-TopicMaps.pdf

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