LLOD and corpora - acoli.cs.uni-frankfurt.deacoli.cs.uni-frankfurt.de/courses/co/2016-06-22-conllrdf/conll-rdf... · LLOD and corpora Ch. Chiarcos, ACoLi CO, 2016, July 22 If you

LLOD and corpora

Ch. Chiarcos, ACoLi CO, 2016, July 22

If you have Un*x environment (Linux, BSD, Mac, Cygwin): - make sure you have JAVA installed and wifi works - lab members: svn checkout /intern/incubator/conll-rdf - others: download and unzip http://acoli.informatik.uni-

frankfurt.de/tmp/conll-rdf-prerelease.zip others: - find a neighbor who does

TODO

http://acoli.informatik.uni-frankfurt.de/tmp/conll-rdf-prerelease.zip









1. Linguistic Linked Open Data

and interoperability challenges wrt. corpora

2. State of the Art: NIF & POWLA

... and why neither is used in NLP nor corpus linguistics

3. Yet another format: CoNLL-RDF

Breaking the usage barrier ?

4. Working with CoNLL-RDF

LLOD and corpora

Linked Open Data

RDF, RDF vocabularies, Linking

Resource Description Framework (RDF)

• W3C standard (1999)

– generic data model: directed labeled graph

• nodes, edges, labels

– originally developed to provide metadata about resources

• e.g., journals in a bookstore and eBooks in an online shop

– resources are unambiguously identified in the web of data by Uniform Resource Identifiers URIs)

4

Resource Description Framework (RDF)

• a (labeled directed multi-) graph

– nodes („RDF resources“)

• anything we want to provide information about

– edges („RDF properties“)

• assigns a source node („subject“) a target node („object“) or a value („literal“)

– nodes and edges are unambiguously identified

• Uniform Resource Identifiers (URIs), e.g., URLs

5

RDF

thesaurus:bronstijd rdf:type thesaurus:period.

thesaurus: bronstijd

thesaurus: period

rdf:type

(the concept) „bronstijd“ is an (instance of concept) „period“ 6

RDF



thesaurus: period

rdf:type

abbreviated for URI <http://www.w3.org/1999/02/22-rdf-syntax-ns#type>

could be opened in a browser resolvable URIs may provide further information

7

RDF


thesaurus:bronstijd skos:prefLabel "Bronzezeit"@de.


thesaurus: period

rdf:type

in German (de), the preferred label for (the concept) „bronstijd“ is „Bronzezeit“

„Bronzezeit“@de

skos:prefLabel

8

RDF



thesaurus:bronstijd skos:prefLabel "bronze age"@en.


thesaurus: period

rdf:type

„Bronzezeit“@de

skos:prefLabel

„bronze age“@en

skos:prefLabel

in English (en), the preferred label for (the concept) „bronstijd“ is „bronze age“ 9

RDF




thesaurus:bronstijd skos:prefLabel "bronstijd"@nl .


thesaurus: period

rdf:type

„Bronzezeit“@de

skos:prefLabel

„bronze age“@en

skos:prefLabel

„bronstijd“@nl

skos:prefLabel

in English (en), the preferred label for (the concept) „bronstijd“ is „bronstijd“ 10

RDF






thesaurus: period

rdf:type

„Bronzezeit“@de

skos:prefLabel

„bronze age“@en

skos:prefLabel

„bronstijd“@nl

skos:prefLabel

graphical notation

triple notation (Turtle)

11

RDF Querying





SPARQL


12

PREFIX skos: <http://www.w3.org/2004/02/skos/core#>

SELECT ?de ?en

WHERE {

?a skos:prefLabel ?de.

?a skos:prefLabel ?en.

FILTER(langMatches(lang(?de), „de"))

FILTER(langMatches(lang(?en), „en“))

}

SPARQL „SQL meets Turte“

RDF Querying





SPARQL


13

PREFIX skos: <http://www.w3.org/2004/02/skos/core#>

SELECT ?de ?en

WHERE {

?a skos:prefLabel ?de.

?a skos:prefLabel ?en.

FILTER(langMatches(lang(?de), „de"))

FILTER(langMatches(lang(?en), „en“))

}

triples with variables

FILTERS with XPath-like functions

RDF Querying


thesaurus: period

rdf:type

„Bronzezeit“@de

skos:prefLabel

„bronze age“@en

skos:prefLabel

„bronstijd“@nl

skos:prefLabel

graphical notation


14





=> list with two cols

RDF

RDF graphs can represent arbitrarily complex structures, can be freely extended with additional nodes, links (edges pointing to external resources), etc.


thesaurus: period

rdf:type

„Bronzezeit“@de

skos:prefLabel

„bronze age“@en

skos:prefLabel

„bronstijd“@nl

skos:prefLabel

thesaurus: arch_concept

rdfs:subClassOf

(every) „period“ is an „arch_concept“ „period“ is subclass of „arch_concept“ 15

RDF vocabularies

Specialized vocabularies for different kinds of information do exist and can (and should) be re-used

=> uniform information representation


thesaurus: period

rdf:type

„Bronzezeit“@de

skos:prefLabel

„bronze age“@en

skos:prefLabel

„bronstijd“@nl

skos:prefLabel


rdfs:subClassOf

RDF und RDF Schema (RDFS) basic vocabulary for taxonomies

16

RDF vocabularies


thesaurus: period

rdf:type

„Bronzezeit“@de

„bronze age“@en

skos:prefLabel

„bronstijd“@nl

skos:prefLabel


rdfs:subClassOf

Additional vocabularies (SKOS, OWL, etc.) extended vocabulary for taxonomies and ontologies

skos:prefLabel

Specialized vocabularies for different kinds of information do exist and can (and should) be re-used

17

RDF vocabularies


thesaurus: period

rdf:type

„Bronzezeit“@de

„bronze age“@en

skos:prefLabel

„bronstijd“@nl

skos:prefLabel


rdfs:subClassOf

thesaurus toy vocabulary for archeology

Specialized vocabularies for different kinds of information do exist

Domain-specific vocabularies can be

specified as required

18

RDF vocabularies

• graphical browsers and editors

e.g.

– http://labs.sparna.fr/skos-play (SKOS)

– http://protege.stanford.edu/ (RDF, OWL, SKOS)

19

RDF vocabularies

• graphical browsers and editors do exist …

… as well as

• data bases,

• W3C-standardized query language,

• APIs,

• reasoners,

• usw. 20

RDF vocabularies

• RDF resources and edges are defined by URIs

– as a URL, they may be accessed remotely

re-usable vocabularies & knowledge bases

– link to community-maintained data types/terms instead of defining your own

• „Linked Open Data Cloud“ (LOD)

– a great variety of resources that are linked with each other and released under an open license

21

Linked Data

• Linked Data

– rules of best practice for publishing data on the web

• use URIs as names for things (1) – links to external URIs (links) allow us to retrieve more

information from these sites

• if they can be resolved via HTTP (2)

• and provide information as RDF* (3)

• and they include links to other URIs (4)

then, this is Linked Data (informal)

http://www.w3.org/DesignIssues/LinkedData.html

22



Linked Data

• Linked Data


=> Information integration

– Structural interoperability

• comparable formats and protocols to access data

=> the same query language for different data sets

23

Linked Data

• Linked Data




– Conceptual interoperability

• develop and (re-)use a shared vocabularies for equivalent concepts

=> the same query on different data sets

24

Linked Data

• Linked Data




– Conceptual interoperability

– Federation

• data published on the web – with a query interface (SPARQL end point)

=> a single query to query different datasets simultaneously

25

Linked Open Data (LOD)

26

LOD cloud: Aug 2014

http://lod-cloud.net/

bibliography

life sciences social networks

geo information

government data

media linguistic LOD

27

Linking Corpora …

• … with other schemes

• … with terminology repositories

• … with lexical-semantic networks

• corpora as Linked Open Data

=> network effects

Structurally Interoperable Language Resources

NLP Interchange Format (NIF):

Interoperability for NLP pipelines

29

Linked Data Corpus Creation with NIF

(NIF Reference Card)

Best practices to follow for the generation of Linked Data text

corpora, using the NLP Interchange Format (NIF).

Target audience

Scope

Corpus creators and users seeking to make corpora interoperable

and to publish them as linked data. Basic knowledge of RDF is

mandatory for conversion. Basic knowledge of linked data and

web server access is needed for publication.

Conversion of existing corpora into RDF using NIF, as well as

creation of linked data corpora from textual data.

30

Website: http://site.nlp2rdf.org Github: http://github.com/nlp2rdf Example corpus: http://brown.nlp2rdf.org

http://site.nlp2rdf.org/


http://gitbub.com/nlp2rdf


http://brown.nlp2rdf.org


31

Core concepts


Corpus

We understand a corpus as a collection of documents. Documents contain text, represented as strings

of characters and annotations that provide more information about these strings. NIF provides a way to

identify strings using URIs and annotate them using an ontology.

String identification via URI:

Strings are identified using a URI scheme consisting of: the prefix of the corpus URI; the character

indices of beginning and end of the string; and a scheme identifier between document URI and string

position identifier. Character indices in NIF are counted offset based, starting at zero before the first

character and counting the gaps between the characters until after the last character of the referenced

string: http://example.org/corpus/document#offset4_10

This URI scheme is valid for text/plain. Other mime types may require different URI schemes.

String annotation

After assigning URIs to meaningful strings of the corpus, these URIs can be annotated using the NIF

core ontology.







http://example.org/corpus/document









http://persistence.uni-leipzig.org/nlp2rdf/ontologies/nif-core/nif-core.html





32

Example


String URIs

◦ Strings are the basis of analysis

◦ nif:anchorOf => string value

◦ offset-based

@base <http://example.org/prefix>

<#char=3,12>

<http://example.org/prefix#char=3,12>

Context

◦ Contains document text in nif:isString

◦ nif:beginIndex is always 0

◦ Strings refer to Context with

nif:referenceContext







33

Example

Pre-defined categories

◦ Word, Phrase, Sentence, Paragraph

◦ rdfs:subClassOf String

◦ hierarchy => subString

Pre-defined properties

◦ head, lemma, stem, posTag, …

oriented towards industrial applications

primarily used for Entity Linking (taldentRef)

limited in scalability

limited support of dependency annotations, no

support for relational semantics

34

Example

Information Integration

◦ Annotations are attached to strings

◦ Implicit unification of divergent annotations

◦ If different tools annotate the same string, this refers to the same URI

35

NIF Core ontology

Rich vocabulary

• OWL-based

• redundant properties

• transitive

• inverse

• before ~

previousWord

• NIF is used in – EU projects

– community projects (e.g., Apache Stanbol)

– industrial applications

• Why not in NLP and linguistics? – ignorance

“Standard data formats … I I'm not sure these are important: if someone can use a parser, they can probably also write a Python wrapper”

Mark Johnson (2012), Computational Linguistics. Where do we go from here?, invited plenary talk at the 50th Annual Meeting of the ACL, Jeju

37

NIF and computational linguistics





– readability human-readable?

38


<http://example.org/sem#offset_4_16> a nif:String , nif:Phrase, nif:OffsetBasedString ; nif:anchorOf "Semantic Web"@en ; nif:beginIndex "4"^^xsd:int ; nif:endIndex "16"^^xsd:int ; nif:oliaLink <http://purl.org/olia/penn.owl#NNP> ; itsrdf:taIdentRef <http://dbpedia.org/resource/Semantic_Web> ; nif:referenceContext <http://example.org/sem#offset_0_32> .

The DT _ Semantic Web NNP http://dbpedia.org/resource/Semantic_Web is VB _ a DT _ good JJ _ idea NN _

NIF

CoNLL





– readability

– limited expressivity • non-String annotations / empty strings?

• non-phrasal MWEs?

• hard-wired properties and concepts – limited to morphosyntax, NER and dependency syntax

39






– readability

– limited expressivity

– established formats for „basic“ annotations • Do we get anything that CoNLL-TSV doesn‘t give us yet?

40






– readability

– limited expressivity

– established formats for „basic“ annotations

– developed with a focus on Semantic Web applications • neither linguistics nor NLP

41


• TELIX

– motivation similar as NIF

– RDFa annotations, added to XML documents

• popularity decreases with the popularity of XML

• OpenAnnotation

– intended for expressing metadata over content elements in HTML

• limited support for linguistic annotations

42

NIF Alternatives

• POWLA

– RDF/OWL reconstruction of an XML standoff format

• capable to represent any annotation faithfully – comes from a small and specialized sub-community in NLP

and linguistics (multi-layer corpora, discourse annotation)

• as unreadable as the original XML standoff format – but better to process

43

NIF Alternatives

CoNLL-RDF

Yet another corpus formalism

• use off-the-shelf technologies for – data storing (RDF triple/quad stores),

– querying (SPARQL),

– manipulation (SPARQL Update), and

– access (SPARQL end points)

• structurally interoperable with – NLP output (NIF) and other RDF-based corpus

formats,

– dictionaries (lemon), and

– terminology bases

45

Technical motivations for corpora in RDF

=> flexible information integration

• RDF-based formalism – rather than OWL-based (NIF, POWLA)

– minimal: no transitive and inverse properties

– generic: no pre-defined categories nor properties

• Grounded in an established and widely used formats in the field – tab-separated values: CoNLL format family

• comfortable – import and export to human-readable

representations

46

CoNLL-RDF

47

48

49

CoNLL-X format (2006)

50 Tab-Separated Values

51

CoNLL-2009 additions: SRL

52

CoNLL-U: Universal Dependencies

53

Tool-specific CoNLL variants: SENNA

• comments begin with #

• sentences separated by empty line

• one word per line

• annotations separated by tab (=> columns)

• empty columns left empty, contain -, _ or O

• HEAD points to another word in the same sentence – „foreign key“, identified by sentential position/ID

• all other annotations assign string values

54

CoNLL formats

• generate URIs

– word: base URI + „s“ + sentence nr + „.“ + word ID

– sentence: word with ID „0“ ( CoNLL root)

– base URI should refer to the original document in a corpus, e.g.,

http://cormand.huma-num.fr/01npogotiginin_kokorobola.dis.html#

55

CoNLL-RDF

• generate URIs


• supply labels for all columns

datatype property in conll namespace

http://ufal.mff.cuni.cz/conll2009-st/task-description.html#<COLNAME>

or

conll:<COLNAME>

56

CoNLL-RDF

• generate URIs




• special treatment of HEAD

– object property pointing to head URI

57

CoNLL-RDF

• generate URIs






• minimal use of NIF vocabulary

– nif:Word, nif:nextWord, nif:nextSentence

58

CoNLL-RDF

• generate URIs






• minimal use of NIF vocabulary

• conventionally formatted to resemble CoNLL

59

CoNLL-RDF

60

CoNLL vs. CoNLL-RDF

...

• three simple java programs

svn:/intern/incubator/conll-rdf/

– CoNLL2RDF basic converter

read CoNLL file, write Turtle

61

CoNLL-RDF processing




– CoNLLStreamExtractor graph manipulation

read CoNLL file sentence by sentence, for every sentence, apply SPARQL Update statements, write Turtle

62






– CoNLLRDFFormatter basic visualization

read CoNLL-RDF, output: CoNLL-like visualization

• special treatment of depencencies

• limited to conll namespace

• coloring on Unix shells

• imposes some naming conventions 63






– CoNLLRDFFormatter basic visualization

– anything beyond this is handled by SPARQL update queries

• modular pipeline (examples in *.sh)

• re-usable modules (given appropriate documentation) – maybe hard to read, but easy to write your own!

64


An example

65

Requires a Unix shell

Please look into the code!

66

An example: acoli-example.sh

67


read one or several files if you have one file only, say FILE.conll, write „cat FILE.conll | \“ instead

68


call CoNLLStreamExtractor (run.sh just adds the classpath)

base URI (can be any URL, etc.)

column labels in the order they occur in the CoNLL file

69


do some layouting

$* means that command-line arguments are passed to CoNLLRDFFormatter but these are not necessary

70


do some layouting

$* means that command-line arguments are passed to CoNLLRDFFormatter but these are not necessary

Please, try for yourself

71

Manipulating the data

Between reading and writing, manipulations can be applied to the graph.

–u => next are some SPARQL Update queries these queries are read from the files in the argument optionally, a number of iterations can be supplied in {0}

• For more complicated manipulations, we also require insertions

– Use INSERT { ... } before DELETE

72

sparql/remove-ID.sparql

• This is from another pipeline, see shift-reduce-example.sh

73

shift-reduce/initialize-SHIFT.sparql

• Given the UD annotations

– write a query that extracts the main verb (predicate) of a sentence

– add this information to the graph ?verb a conll:predicate.

– add this query to acoli-example.sh and run it

74

Task: Write a SPARQL update query

use this as a template.

Example, extended

75

Linking with OLiA

Linking Corpora OLiA

(Schmidt et al. 2006, Chiarcos 2008, 2010, 2012)

English

EAGLES

MULTEXT/ East

15 (mostly) Eastern European languages

MULTEXT/ East

MULTEXT/ East

11 European languages

EAGLES EAGLES

STTS

TIGER German Connexor

TüBa-D/Z

Annotation Models for German

Penn

Brown

Susanne etc.

Reference Model

GOLD

ISOcat (morpho-

syntax)

OntoTag (morpho-

syntax)

TDS ontology

OLiA

External Reference Models

(Terminology Repositories)

Annotation Models

Linking: given a POWLA individual i

if annotations of i match

OLiA annotation model

specs

then declare i an instance of

the corresponding OLiA class

77

Integrating external information

link English POS / dependencies with OLiA

78

sparql/link-penn-POS-simple.sparql

79

Task II: Extend this such that ?a is also an instance of superclasses of ?concept

Example, further extended

80

Consult an online dictionary

• SPARQL permits accessing endpoints and web services on the web

– in the SELECT part of the query

SERVICE <URL> { ... }

• SPARQL permits accessing endpoints and web services on the web

– e.g. DBnary, an RDF edition of various Wiktionaries

• http://kaiko.getalp.org/sparql

81

Getting German glosses for English text

82

Exploring an end point

There seems to be a German DBnary

What datasets are in here ?

83


lemon:LexicalEntry may be what we‘re looking for

What does the German dictionary contain?

84

Exploring an end point Examples for LexicalEntries

Ok, let‘s look into „Abenteuer“

85

Exploring an end point Examples for LexicalEntries

Indeed, this contains dbnary:isTranslationOf

86


Check an example translation

targetLanguage => language dbnary:writtenForm => String

87

Explore an end point

etc., until we have pairs of strings from DBnary

88

Explore an end point

89

Adding German translations sparql/gloss-en-to-de-DBnary.sparql

90

Sample output

• hints: { ... } UNION { ...} => logical or FILTER(NOT EXISTS { ... }) => negation

91

Task III: return a word-by-word German „translation“ (and nothing else)