Transcript Document

Interoperability
- is it feasible -
Peter Wittenburg
The Language Archive – Max Planck Institute for Psycholinguistics
Nijmegen, The Netherlands
Why care about interoperability?
• e-Science & e-Humanities
• “data is the currency of modern research”
• thus need to get integrated access to many data sets
• data sets are
• scattered across many repositories => (virtual) integration
• created by different research teams using different
conventions (formats, semantics)
• often in bad states and quality => curation
• thus interoperability most used word at ICRI conference
• Big Questions:
• What is meant with interoperability?
• How to remove interoperability barriers to analyze large
heterogeneous and probably distributed data sets?
• Is interoperability something we need/want to achieve?
What is interoperability?
1. Wikipedia: Interoperability is a property of a system, whose
interfaces are completely understood, to work with other systems,
present or future, without any restricted access or
implementation.
2. IEEE: Interoperability is the ability of two or more systems or
components to exchange information and to use the information
that has been exchanged.
3. O’Brian/Marakas: Being able to accomplish end-user applications
using different types of computer system, operating systems, and
application software, interconnected by different types of local
and wide-area networks.
4. OSLC: To be interoperable one should actively be engaged in the
ongoing process of ensuring that the systems, procedures and
culture of an organization are managed in such a way as to
maximise opportunities for exchange and re-use of information.
What is interoperability?
• Technical Interoperability (techn.
encoding, format, structure, API,
protocol)
• Semantic Interoperability
• is it also about bridging
understanding between two or more
humans?
<hund>
humans – humans
we better speak about understanding
humans – machine same or?
machine – machine well here interoperability makes sense
<köter>
<dog>
What is interoperability?
•
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seems that every one speaks about technical systems when talking
about interoperability
do we include feeding machines with some mapping rules specified
by human users and then carrying out some automatic functions?
• when linguists hear about mapping tag sets some immediately
say that it is impossible and does not make sense
• why: tags are part of a whole theory behind it
well if you look to other disciplines (life sciences, earth
observation sciences etc.) that’s exactly what they do
why
• people want to work across collections and ignore theories
• some see tag sets just as first help but want to work on raw data
• some see the demand of politicians and society to come up with
answers and not with statements about problems
• AND there is much money (is it useless?)
Big Data in Natural Science
• numbers in regular structures
• how to find relevant data sets
• volcanology/earthquakes/Tsunamies/etc.
• X sensor datastreams (seismology)
(time, location, parameters)
• X human observations (biodiversity)
(time, location, nr. frogs (etc))
• window extraction to transfer and
manage data
• interpret regular structures (even frogs)
• time normalization, take care of
dynamics etc.
• visualize things coherently
Big Data in Natural Science
• numbers in regular structures
• how to find relevant data sets
• volcanology/earthquakes/Tsunamies/etc.
• X sensor datastreams (seismology)
(time, location, parameters)
• X human observations (biodiversity)
(time, location, nr. frogs (etc))
• window extraction to transfer and
manage data
• interpret regular structures (even frogs)
• time normalization, take care of
dynamics etc.
• visualize things coherently
Big Data in Environmental Sciences
• many different types of observations
• climate, weather, etc.
• species and populations according to multitude of
classification systems and schools
• grand challenge
• how can all these observations be used to stabilize our
environment
• how can it all be used to maintain diversity
• etc.
Big Data in Environmental Sciences
• many different types of observations
• climate, weather, etc.
• species and populations according to multitude of
classification systems and schools
• grand challenge
• how can all these observations be used to stabilize our
environment
• how can it all be used to maintain diversity
• etc.
many layers of interop: access
Enabling
Technologies
ID
ID
Discovery
Access
(ref. resolution,
protocols, AAI)
ID
ID
0100
0101..
ID 0100 ID
0101..
0100
0101..
ID
ID
need a high degree
of automation
ID
ID
ID
ID
ID
ID
Scientists, Data Curators,
End Users, Applications
ID
0100
0101..
ID
ID
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0101..
ID
Interpretation
what can be
Datasets automated
Accessed via Repositories
Reuse
many layers of interop: management
(mostly underestimated!!!)
Enabling
Technologies
ID
Collections +
Properties
Access
(ref. resolution,
protocols, AAI)
ID
ID
ID
0100
0101..
ID 0100 ID
0101..
0100
0101..
need a high degree
of automation
ID
ID
ID
ID
ID
ID
formalized policies
workflow engine
ID
ID
ID
ID
Data Managers
Data Scientists
ID
0100
0101..
0100
0101..
ID
need a high degree
Datasets of automation
Accessed via Repositories
Assessment
simple but essential example: PIDs
• it’s similar to TCP/IP with all its core machinery that brought us the
Internet and thus interoperability with respect to communication
• email system works when we abstract from content and thus the
semantics of our human messages and focus on the semantics of
attributes, parameters etc.
• let’s assume that you want to use a certain file and first want to be
sure that the file has not been modified
• you look up in metadata
• that automatically looks for the PID
• the PID is resolved automatically and a checksum is retrieved
• the checksum is automatically compared with the checksum of
the file accessed
• a warning is given automatically if the two don’t match
• this would be a great service (and will come)
Internet machinery
DNS email WWW phone
SMTP HTTP RTP…
TCP UDP…
Value Added
Services
Internet
Protocol Suite
IP
ethernet PPP…
CSMA async sonet…
copper fiber
(collaboration CNRI and MPI)
Network
Technology
radio
all applications making use of the same basic protocol where the
“packet” is the basic object and where endpoints have addresses and names
Data machinery (collaboration CNRI and MPI)
Persistent
Citation
Reference
Custom
Plug-Ins
Clients
Analysis
Apps
Resolution System
Value Added
Services
Typing
Persistent
Identifiers
PID
Digital Objects
Data Sets
RDBMS
Local Storage Cloud
Files
Computed
Data
Sources
points to instances
describes properties
bit sequence
(instance)
describes
properties
& context
PID record
attributes
point to
each other
metadata
attributes
all applications making use of the same basic protocol where “data” is
the basic object and where PID and metadata attributes describe object properties
Layers of interoperability
• Protocols/APIs: defined formats, semantics, processes
• SCSI: how to read/write/etc. blocks to/from SCSI disc
• File System: how to read/write/etc. to/from logical entities
how to organize files on a machine (virtualization)
how to organize files across machines
• OAI/PMH: how to serve metadata descriptions
• SRU/CQL: how to do distributed content search
• etc. etc.
• all based on standards or widely accepted best practices
• advantage: standards establish a 1:N relation constant over time
• large number of standards/BP for metadata (structure, semantics)
back to linguistics
• where are we in the linguistics domain?
• what happened in some well-known projects
• do we miss the big challenges which other disciplines have and
that would force us to ignore schools, vainness, etc.
• 4 examples
• metadata
• DOBES
• TDS
• CLARIN
metadata is kind of easy
• DC/OLAC – CMDI mapping examples:
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•
•
•
•
•
•
•
•
•
•
DC:language
DC:language
DC:language
DC:language
DC:date
DC:date
DC:date
DC:date
DC:format
DC:format
DC:format
DC:format
CMDI:languageIn
CMDI:dominantLanguage
CMDI:sourceLanguage
CMDI: targetLanguage
CMDI:creationDate
CMDI:publicationDate
CMDI:startYear
CMDI:derivationDate
CMDI:mediaType
CMDI:mimeType
CMDI:annotationFormat
CMDI:characterEncoding
crucial for
machine
processing
• everyone accepts now: metadata is for pragmatic purposes and
not replacing the one and only one true categorization
• mapping errors may influence recall and precision – but who
cares really
truth in metadata usage – still !!
Rebecca Koskela: DataONE
DOBES – some facts
DOBES = Documentation of Endangered Languages
some facts
• started 2000 with 7 international teams and 1 archive team
• 2012: now 68 documentation teams working almost every where
•
•
crossdisciplinary
approach:
linguists,
ethnologists,
musicologists,
biologists, ship
builders, etc.
every year one
workshop and
two training
courses
DOBES Agreements
• in first 2-3 years quite some joint agreements
• formats to be stored in the archive – interoperability
• principles of archiving such as PIDs
• workflows determining the archive-team interaction
• organizational principles to manage and manipulate data
• metadata to be used to manage and find data
(pragmatics vs. theory)
• joint agreement on Code of Conduct
• short discussions on more linguistic aspects failed
• agreement on joint tag set - NO
• agreement on joint lexical structures - NO
• etc.
• good reason: the languages are so different
• “bad” reason: agreements require effort
recent DOBES Questions
• now after >10 years we have so much good data in the archive
• what can we do with it ????
• traditional: every researcher looks at his/her data and publishes of
course taking into account what has been published by others
• new: can researcher teams come to new results while working on the
raw and annotated data?
• what does this require in case of “automatic” or “blind” procedures?
(remember that the researchers do not understand the language)
• you need to know the tier labels to understand the type of
annotation
• you need to know the tags used to understand the results of the
linguistic analysis work (morphological, syntactical, etc.)
an text example
what’s this?
Example from Kilivila (Trobriand Islands – New Guinea)
p1tr
p1en
Ambeya
Where do you go?
p2tr
Bala
bakakaya
p2w-en I will go
I will take a bath
p2en
I will go to have a bath
p2tr
p3gl
Bila
3.Fut-go
bikakaya bike’ita
3.Fut-bath 3.Fut-come back
p2w-en
He will go - he will have a bath - he will come back – he will stay he will work.
p2en
He will take a bath and afterwards work with us.
bisisu
bipaisewa
3.Fut-be 3.Fut-work
what’s this?
big question: what can we do with searches, statistics –
thus semi-automatic procedures across different corpora
Hum. Example: Multi-verb Expressions
mixed glossing
POS tagging
a multimodal example
Interaction Study: 12 participants + exper; per part. 7 tiers
tier names
from Toolbox
tier names – an area of creativity
5 cross-corpora projects
• demonstratives with exophoric reference
(morpho-syntactic and discourse pragmatic analysis incl. gestures)
• discourse and prosody – convergence in information structure
• relative frequencies of nouns, pronouns and verbs
• cross-linguistic patterns in 3-participant events
• one rather large program with 13 teams covering different languages
• primary topic is “referentiality”
• bigger question: how to do this kind of cross-corpus work
• strategy: define new tag set and add a manually created tier
• yet no agreed tags – committee has been formed
• now in a process to determine selection of corpora
• question: will existing tags help to find spots of relevance
TDS (LOT etc.)
Typology Database System - offering one semantic domain to look for
phenomena in 11 different typological databases created independently and
covering many languages.
straight
complex
mapping
mapping
many
descriptive
parameters
&
differences in
structure,
terminology
and
theoretical
assumptions
Database schemataLocal database ontologies Global linguistic ontology
Topic taxonomies
(any DDL)
(SKOS)
Database developer
(DTL)
(OWL)
TDS Knowledge engineer
Domain expert
TDS (LOT etc.)
Typology Database System - offering one semantic domain to look for
phenomena in 11 different typological databases created independently and
covering many languages.
straight
complex
mapping
mapping
many
descriptive
parameters
&
differences in
structure,
terminology
and
theoretical
assumptions
Database schemataLocal database ontologies Global linguistic ontology
Topic taxonomies
(any DDL)
(SKOS)
Database developer
(DTL)
(OWL)
TDS Knowledge engineer
Domain expert
subject-verb agreement
• Q1: which languages have subject-verb agreement?
• db A: exactly this question with Boolean answer
• no distinction thus simple
• db B: bundle of information
• sole argument of an intransitive verb
• agent/patient/recipient-like arguments of transitive verb
• in general “yes” for s and a cases (but not always clear)
• Q2: which languages are of type a for transitive verbs
• db A: ambiguous – so give all languages or none
• db B: simple answer
• a pre-query stage allows user to decide about options
• what when several parameters are used to describe a phenomenon
Did TDS work?
• let’s assume that
• the local ontologies represent the conceptualization correctly
• the global ontology forms a useful unifying conceptualization
(is there such an accepted unifying ontology?)
• the 2-stage query interface offers proper help
• THEN TDS sounds like an excellent, scalable approach
• why did TDS not yet take up?
• TRs rely on papers and are not interested in databases ?
• TRs don’t understand and rely on the formal semantics blurb ?
• TRs would need to invest time – do they take it ?
(occasional usage, small community of experts)
• what is WALS then – just a glossary for non-experts ?
What happens in CLARIN?
• well Metadata is obvious –> Virtual Language Observatory
• harvesting and mapping is not the problem
• bad quality is the problem (as for Europeana etc.)
• planned is f.e. distributed content search
SRU/
CQL
what is comparatively easy?
• what if we only look at Dutch or German texts?
• searching just for textual patterns (collocations)
• could make use of SUMO, Wordnets to extend query etc
• but can/should we compete with Google?
• what if we search across languages?
• well – need some translation mechanism for textual
patterns – could be trivial translation
• does it make sense – will people use it?
• AND: it is mainstream – so Google will do it
what is more difficult and special?
• assume some annotated texts, audios, videos
• assume some standard type of linguistic annotations such as
morphosyntax, POS, etc.
..TAGSET1 ...TAGSETT2 ...TAGSET3 ...
21
QUERY
33
C1
C2
C3
C4
C5
C6
C7
...
12
EXPAND/
Not Expand
44
1. Select corpora
2. Select Tag sets
3. Formulate query
4. Expand by rules
(relations between tags)
semantic bridges: how?
• assume that we have two corpora: one encoded by STTS
and the other one by CGN and assume that they have
some linguistic annotation (morphosyntax, POS, etc.) to
be used in a distributed search or statistics
(take care: searching != statistics)
• what to do now to exploit both collections?
1. do separate searches – well ...
2. create rich umbrella ontology and complex refs
(comparable to TDS)
•
•
•
•
well - could become a never ending story ...
people disagree on relations etc.
relations partly depend on pragmatic considerations
expensive, static, require experts, not understandable, etc.
Are flat category registries ok?
3. flat registries of linguistic categories such as ISOcat (12620)
sound like a solution for some tasks
• easy mapping between two (or more) categories
• users can easily create their own mappings or re-use some
• maintenance is more easy and thus allows dynamics
• etc.
• so it seems that we could overcome the TDS barrier
• but we are reducing accuracy and losing much information
• too simple for statistics ??
• sufficient for searches ??
What about Jan’s examples?
• e0: annotations are structured: “np\s/np”
• e1: “JJR” -> “POS=adjective & degree=comparative”
• e2: “Transitive” -> “thetavp=vp120 & synvps=[synNP] &
caseAssigner=True”
• e3: “VVIMP” -> “POS= verb & main verb &
mood=imperative”
• where to put annotation complexity if “ontology” is simple
• complexity needs to be put into schemas
• who can do it – is it feasible?
• mapping must be between combinations of cats or graphs
• who can do it – is it feasible?
are there conclusions?
1. do we want/need cross-corpora operations?
• for many other communities this is a MUST
• don’t we have “society relevant” challenges?
• do they just get more money?
• given all regularity finding machines – is linguistic
annotation relevant at all?
2. is it for us more difficult to do?
• well - that’s what all claim – don’t believe that anymore
are there conclusions?
3. are we interested to try it out?
• well – yet there are not so many people committed
• is it not of relevance?
• is it lack of money?
• some are opposing strictly
• is it a sense of reality?
• is it lack of vision?
• is it vainness?
4. if interested, how do we want to tackle things?
• pragmatic – stepwise – simple first
• will people use it then?
• do we have evangelists?
Bedankt voor het luisteren.
useless Cloud debate
some just call for Cloud – what does it solve?
just collect also
all content into
one big pot
all the issues about interoperability remain the same
searching will be more efficient – no transport etc.
What about metadata?
• TEI example 1
resp annotation supervisor and developer
date from="1997" to="2004"
name Claudia Kunze
• which date is it? need to interpret context
• which role is it? need to interpret context
• TEI example 2
name Dan Tufiş
resp Overal editorship
name Ştefan Bruda
resp Error correction and CES1 conformance
• which role is it? need to interpret context
• very simple examples show
• meant to be read by humans
• (too) much degree of freedom
• no CV for responsibility role
just a bit of school
Concept
Refers To
Referent
Symbolizes
Stands For
Slide adapted from (c) Key-Sun Choi for Pan Localization 2005
from the slide of [Bargmeyer, Bruce, Open Metadata Forum, Berlin, 2005]
C.K. Ogden/I.A. Richards, The Meaning of Meaning
A Study in the Influence of Language upon Thought and The Science of Symbolism
London 1923, 10th edition 1969
“Orange”
Term