NIF 2.0 Tutorial: Content Analysis and the Semantic Web

NIF Tutorial – 2013/09/24 – Page 1 http://lod2.eu
Creating Knowledge out of Interlinked Data
LOD2 Presentation . 02.09.2010 . Page http://lod2.eu
AKSW, Universität Leipzig
Sebastian Hellmann
Content Analysis
and the Semantic Web
NIF 2.0 Tutorial
http://nlp2rdf.org
http://lod2.eu
http://slideshare.net/kurzum

Sebastian Hellmann – researcher working on LOD2 EU Project
AKSW – Agile Knowledge and the Semantic Web research group in Leipzig -
http://aksw.org
InfAI – Institute for Applied Informatics - http://infai.org
ALL DEMOS ARE AVAILABLE AT:
http://nlp2rdf.org/leipzig-24-9-2013
Introduction

Introduction

End users have tasks for NLP, but:
Each new tool is a challenge:
• How to download and start it?
• What kind of annotations does it use?
• How good does it perform (on my domain)?
• If badly, are there any alternatives? How can I find them?
• Open source?
• Lot's of know-how needed to exploit NLP.
• Lot's of data needed to exploit NLP.
Barriers to NLP

The Semantic Gap

• Part 1: exploiting free, open and interoperable (FOI)
language resources
• Part 2: Connecting text to these resources
• Part 3: tools, demos, infrastructure
From a walled garden to
an interoperable infrastructure

• Part 1: exploiting free, open and interoperable (FOI)
language resources

http://lod-cloud.net
Linguistic/NLP Data currently filed
under “cross-domain”

http://lod-cloud.net
Linked Open Data
- All datasets provide open access to individual records via HTTP
- Many are free (no payment required, as in royalty-free)
- Some are openly licensed, e.g. CC-0 or CC-BY-SA
=> Open access also applies to published HTML on the WWW, but in LOD the data
itself is published unrendered via RDF

Question:
• Who knows how to add a new bubble to the LOD cloud?

• Who knows how to add a new bubble to the LOD cloud?
http://datahub.io/group/linguistics
https://github.com/jmccrae/llod-cloud.py
http://validator.lod-cloud.net/validate.php

Question:
• What are the most important data sets and ontologies for NLP?
• Who has used what?
FOI data

Analysis of mentions of Wikipedia / DBpedia at LREC 2012:
• https://www.google.com/webhp?q=site:http%3A%2F%2Fwww.lrec-conf.org%2
→ 163 papers
• https://www.google.com/webhp?q=site:http%3A%2F%2Fwww.lrec-conf.org%2
→ 24 papers
FOI data 1: Wikipedia / DBpedia

• Training data for NLP, e.g. URI, surrounding text, surface form
• Probabilities:
• P(sf|URI): P that “apple” refers to wikipedia:Apple_Inc.
• P(URI|sf): P that wikipedia:Apple_Inc. is “apple” in text
FOI data 1: Wikipedia / DBpedia
http://wiki.dbpedia.org/Datasets/NLP

FOI data: Wikipedia / DBpedia
http://lookup.dbpedia.org/api/search.asmx/KeywordSearch?
QueryString=sodium
http://lookup.dbpedia.org/api/search.asmx/KeywordSearch?
QueryString=sodium
Available data for “Sodium”
http://dbpedia.org/snorql
select ?labels where {
<http://dbpedia.org/resource/Sodium> rdfs:label ?labels .
} LIMIT 100
select ?altlabel where {
?redirect dbpedia-owl:wikiPageRedirects <http://dbpedia.org/resource/Sodium> .
?redirect rdfs:label ?altlabel .
} LIMIT 100
http://lcl.uniroma1.it/babelnet/explore.jsp?word=sodium&lang=EN

Wiktionary2RDF – Mediator Wrapper
http://dbpedia.org/Wiktionary

Mediator
Lemon

http://lcl.uniroma1.it/babelnet/explore.jsp?word=sodium&lang=EN
https://en.wiktionary.org/wiki/sodium#English
http://wiktionary.dbpedia.org/resource/sodium

Lemon Ontology - http://lemon-model.net

Lemon Ontology - http://lemon-model.net
IntersectiveDataPropertyAdjective ("extinct" ,
dbpedia:conservationStatus ,"EX")
IntersectiveDataPropertyAdjective ("endangered" ,
dbpedia:conservationStatus ,"EN")
https://github.com/cunger/lemon.dbpedia
Christina Unger, John Mccrae, Sebastian Walter, Sara Winter and Philipp Cimiano (2013):
A lemon lexicon for DBpedia. NLP & DBpedia Workshop

• Part 2: Connecting text to these resources

https://github.com/dbpedia-spotlight/dbpedia-spotlight/wiki

Overview of existing tools:
• http://en.wikipedia.org/wiki/Knowledge_extraction#Tools

Developers nightmare:
• All tools belong to similar class of NLP tools
→ Wikifier or Named Entity Linking, SOA principle
But they all have:
• Heterogeneous output formats (JSON, XML)
• Heterogeneous API parameters
• Heterogeneous ways of annotating text:
• Some remove HTML internally, offsets not usable
• Some use byte offset instead of char offset

Demo
• http://rdface.aksw.org/new/tinymce/examples/rdface.html

ITS 2.0 - http://www.w3.org/TR/its20/
The Internationalization Tag Set (ITS) 2.0 – enhances the foundation to
integrate automated processing of human language into core Web
technologies.
• Currently last call
• Driven by localization industry
• Embed translation aids into HTML and XML
• Robust way to encode NLP information in HTML
• ITS 2.0 describes 20 data categories → ontology

NIF overview
Summary
• Motivated the Walled Garden problem
• Overview of the emerging Web of Language resources
• Motivated the NLP tool heterogeneity problem
• Introduction of ITS 2.0 Use case for NIF
• Now: NIF 2.0

The NLP Interchange Format (NIF) is an RDF/OWL-based format that aims to
achieve interoperability between Natural Language Processing (NLP) tools,
language resources and annotations.
• Reuse of existing standards such as RDF, OWL 2, the PROV Ontology, LAF
(ISO 24612), Unicode and RFC 5147
• Standardize access parameters, annotations (e.g. tokenization), validation
and log messages.
• A NIF workflow, however, can obviously not provide any better performance
(F-measure, speed) than a properly configured UIMA or GATE pipeline with
the same components.
• Lower entry barrier, easy data integration, reusability of tools and
conceptualisation, off-the-shelf solutions for common tasks.
NIF Overview

Relation of NIF and UIMA and Gate
• A Formal Framework for Linguistic Annotation (2000) by Steven Bird, Mark
Liberman
• take home message: generic annotation formats should be based on
graphs
• Ontologies in NIF (e.g. OliA, lemon) can be hard compiled for internal use (as
is done in Stanbol)
WP3 Task 3.2 – Community work: NLP2RDF
Not primarily aimed at
increasing features or
performance (F-Measure)

WP3 Task 3.2 – NIF overview

• NIF turns out to have a Unique selling proposition regarding NLP and RDF
• NIF will be the recommended RDF conversion of the Internationalisation
Tagset 2.0 of W3C (ITS 2.0) - http://www.w3.org/TR/its20/
• There was no alternative RDF vocabulary for this conversion available.
NIF Overview

WP3 Task 3.2 – Community work: NLP2RDF
RDFa parsers loose all provenance information:
<http://examples.com/books/wikinomics> dc:title ''Wikinomics'' .
https://en.wikipedia.org/wiki/RDFa

Available resources:
http://persistence.uni-leipzig.org/nlp2rdf/
Disclaimer
Migration to the online presence is still on-going, but there are 15 scientific
publications, e.g.
Integrating NLP using Linked Data. Sebastian Hellmann, Jens Lehmann, Sören Auer, and Martin Brümmer. 12th
International Semantic Web Conference, 21-25 October 2013, Sydney, Australia, (2013) -
http://svn.aksw.org/papers/2013/ISWC_NIF/public.pdf
NIF Overview

Question:
• What is a String?
NIF Basics

Counting strings is more difficult than it seems:
• Three ways to count Unicode:
• Code Units
• Code Points
• Graphems
• Encoding:
• UTF-8, 16, 32
NIF Basics Unicode

• Code Unit. The minimal bit combination that can represent a unit of encoded
text for processing or interchange. The Unicode Standard uses 8-bit code
units in the UTF-8 encoding form, 16-bit code units in the UTF-16 encoding
form, and 32-bit code units in the UTF-32 encoding form.
• Code Point. (1) Any value in the Unicode codespace; that is, the range of
integers from 0 to 10FFFF16. Not all code points are assigned to encoded
characters. See code point type. (2) A value, or position, for a character, in
any coded character set.
• Unicode Normal Form C
• http://unicode.org/reports/tr15/#Norm_Forms
Unicode

• Recommendation for RDF Literals
• http://unicode.org/reports/tr15/#Norm_Forms
Unicode Normal Form C

• NIF uses Unicode Normal Form C
• NIF counts in Code Points
Unicode

• Sadly, there are still implementation problems:
• Java length() vs. PHP strlen() function
• curl --data-urlencode i=" 대 " -d f=text "http://nlp2rdf.lod2.eu/nif-ws.php"
• Korean Character is URL encoded (#%EB%8C%80) and counted as 3
characters (not NFC in PHP)
Demo

• Now some RDF (finally):
• Note that in NIF the document is != content of the document.
• two different documents can have the same content
=> must not have the same URI
Context

Annotations

Tokenization
Christian Chiarcos, Julia Ritz, Manfred Stede: By all these lovely tokens... Merging conflicting tokenizations.
Language Resources and Evaluation 46(1): 53-74 (2012)

NIF
Demo:
http://nlp2rdf.lod2.eu/demo.php

• SPARQL queries produce (find) errors
• http://persistence.uni-leipzig.org/nlp2rdf/ontologies/testcase/lib/nif-2.0-suite.t
• RLOG – An RDF Logging Ontology
• ./validate.jar -i nif-erroneous-model.ttl -t file
• Demo → character count
• Demo → all errors
Validation over specification

NIF
Demo:
http://nlp2rdf.lod2.eu/demo.php

NIF

• http://www.w3.org/TR/its20/#conversion-to-nif
• http://www.w3.org/TR/its20/#nif-backconversion
NIF

• Demo
• Load Terminological model or Inference Model
Reasoning

Open Community – All feedback is welcome!
http://slideshare.net/kurzum
Websites:
http://dbpedia.org
http://nlp2rdf.org
http://lod2.eu
Thanks for your attention

NIF 2.0 Tutorial: Content Analysis and the Semantic Web

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