The document discusses the NLP Interchange Format (NIF) 2.0, which aims to achieve interoperability between NLP tools, resources, and annotations. It notes that NIF 2.0 will be published within 6-8 weeks and is highly likely to become the de facto standard for modeling RDF tool output in NLP. The document analyzes problems in the current NLP landscape such as heterogeneity of technologies, formats, languages, and lack of open collaboration and standards. It presents NIF as a solution to these problems by defining text normalization, an RDF-based core ontology, modular ontologies, and infrastructure for validation, hosting, and adoption. Evaluation approaches and a positive impact and reception of N

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Creating Knowledge out of Interlinked Data
LOD2 Presentation . 02.09.2010 . Page http://lod2.eu
AKSW, Universität Leipzig
Sebastian Hellmann
PhD thesis intermediate report
NLP Interchange Format (NIF) 2.0
http://nlp2rdf.org
http://lod2.eu
http://slideshare.net/kurzum
DISCLAIMER:
this presentation is work in progress, example RDF is outdated

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NLP Interchange Format 2.0

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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.
• NIF 2.0 will be published in 6-8 weeks
• Highly probable to become the de-facto standard for modelling RDF tool
output in the NLP domain
NLP Interchange Format 2.0

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Introduction
Components have pre- and postconditions
auto configuration theoretical possible, but in reality a lot of manual work

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Introduction
Components have pre- and postconditions
auto configuration theoretical possible, but in reality a lot of manual work
Huge potential to save time and money at the interfaces

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Core problems:
1. Too much heterogeneity
2. Almost no standards available
3. No open collaboration
4. Difficult and large domain
Problem analysis

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Technical heterogeneity
• Technologies: XML, Relational Databases, CSV, DOC, PDF
• Similar to other domains
• Formats: Negra, CoNLL, GrAF, Paula, CAS (UIMA), Penn
• Virtually each tool has implemented readers for the 5-6 formats + its
own serialization
• Programming languages: Java, Python, ...
• Java has predominance
Problem analysis

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Domain heterogeneity
• Multilingualism
• Over 100 part of speech tags (several for each language)
• No open mappings exist
• About 20 different tasks listed on:
http://en.wikipedia.org/wiki/Natural_language_processing#Major_tasks_in_NLP
• Natural language is a difficult topic:
• The roulette dealer siad: “Rien ne va plus!”
– 8 words, 4 French, 4 English, one spelling mistake, impossible to
decide the language of the whole.
• Ban on Nude Dancing on Governor's Desk
Problem analysis

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Problem analysis

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Open collaboration
• LAF/GrAF is a recently released ISO standard
• But it is not open (60 Euros to view the document)
• Not in RDF (the main requirements for any Semantic Web tool)
• Large frameworks tend to only be “inward” compatible
• UIMA advocates say: “Why don't you just use UIMA?”
• Gate advocates: “Integrate it into GATE!”
• Generally, a large time investment and lock-in
Problem analysis

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Summary:
Hardly any reusability
• Free software (as in free beer), but no open licenses
• No standards and no mappings
• Integration is hard-wired (you have to write software)
Problem analysis

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• Definition for text normalization + URI Schemes (give URIs to Strings)
• NIF Core Ontology: default vocabulary for most often used annotations
• Predefined modules for most use cases
• Infrastructure
• for open collaboration / discussion
• persistent hosting
• validation and demo services
• Reference implementation
• Data conversion
NIF Overview

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Text Normalization + URI Schemes

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Text Normalization + URI Schemes
NIF 1.0:http://www.w3.org/DesignIssues/LinkedData.html#offset_717_729
NIF 2.0 uses RFC 5147 as base form:
http://www.w3.org/DesignIssues/LinkedData.html#char=717,729
User extensions possible:
http://www.w3.org/DesignIssues/LinkedData.html#your_own_scheme
(but you have to link to documentation on how it was created)

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As a Web Service
curl
--data-urlencode prefix="http://prefix.given.by/theClient#"
--data-urlencode input="[...]"
(--data-urlencode source=”http://www.w3.org/DesignIssues/LinkedData.html”)
http://nlp2rdf.lod2.eu/demo/NIFStanfordCore
The new namespace is http://persistence.uni-leipzig.org/nlp2rdf/nif-core#

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Ontologies:
• NIF Core Ontology (URI Scheme, String, Context, but also Token, Sentence,
lemma, stem, etc. ) for often used annotations.
• Simple Error Ontology to describe errors (fatal, message, timestamp)
• Vocabulary Modules for each purpose or ontology or project
Overview of Ontologies

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Each ontology consists of three sets of axioms:
- Terminology model (definitions)
- Inference model (especially transitivity)
- Validation model (consistency)
1) nif-core.ttl
2) nif-core-inf.ttl imports 1
3) nif-core-val.ttl imports 1 and 2
Logical Modularity

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NIF simple:
• Only one truth
• Easy to understand and to query
• Least amount of triples
NIF + Stanbol (Apache Project)
• Several ranked alternatives
• Provenance of annotations
• In collaboration with Apache Stanbol
Open Annotation (W3C group)
• Rich model
• Not only text, but everything (images)
Granularity Modularity
- More triples
- more complexity
- worse usability
- lossless
conversion
Well-defined conversions
between the different levels
- easier queries
- higher performance
- lossful conversion

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Strucural Interoperability:
- URI schemes provide normalization
- RDF provide graph data model
- OWL provides the logical model
Conceptual Interoperability
- NIF Core Ontology and mapping to most often used annotations, e.g. lemma,
stems
- Vocabulary Module to include other terminologies and ontologies
Interoperability

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• ITS 2.0
• FISE used in Apache Stanbol (IKS-EU Project)
• LAF/GrAF XML – ISO standard, recently published
• Fragment Identifiers by IETF and W3C
• Lemon ontology from Monnet EU Project
• NERD ontology from EURECOM and LinkedTV EU Project
• Xpointer/XPath URI scheme
• Open Annotation
• ISOCat
NIF 2.0 tries to be compatible to (Vocabulary Module)

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• Tibeto-Burman languages: http://purl.org/olia/tibet.owl#VNst
• Russian TreeTagger :
http://purl.org/olia/russ.owl#partizip_prt_sg_neut_passiv_gen_langform
• German STTS: http://purl.org/olia/stts.owl#VAPP
• English Penn: http://purl.org/olia/penn.owl#VBG
→ all map to http://purl.org/olia/olia.owl#NonFiniteVerb
Ontologies of Lingingustic Annotation (OLiA) contain mappings for over 50 Tagsets (free
and open, CC-By)
Vocabulary Module: OLiA

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NIF can be extended by Vocabulary Modules
OliA
http://purl.org/olia
Conceptual Interoperability

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• Java-Maven implementation
• PHP implementation
• Reference implementations: DBpedia Spotlight, Stanford Parser, Korean POS
tagger, Keyword Search
• Wiki: http://wiki.nlp2rdf.org
• Validators
• Code generators (convert vocabulary modules to code stubs)
• NIF is free and open (CC-0 / CC-BY / Apache)
• All ontologies will be hosted persistently by University Leipzig
•http://persistence.uni-leipzig.org/nlp2rdf/
NIF 2.0 Infrastructure for adoption

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• Huge collection of use cases
• e.g. Ali wants to exchange different NLP service for RDFace
• LOD2 from Wolters Kluwer
• A selection will be implemented. Assumption:
• NIF is good, if it fulfills many use cases
Evaluation 1

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• There are about 10 to 20 third party implementations
Evaluation 2

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Analysis of existing frameworks and formats. Criteria:
• Convertability (Adequacy)
• Do the graph models match?
• Coverage
• Quantitative analysis of used annotations
• Does NIF Core provide terms for the most common annotations, are
there any gaps?
Evaluation 3

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Data Conversion

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Data Conversion

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Data Conversion
Data is available as
free, open, interoperable (FOI) language resources at
http://linguistics.okfn.org/resources/llod/
(work in progress)

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Project has a very good impact:
• Many adopters
• Industrial uptake
• Inclusion in a W3C standard for ITS 2.0:
http://www.w3.org/International/multilingualweb/lt/drafts/its20/its20.html
• Several projects involved as stakeholders (LOD2, Monnet, ...)
• Several motivated open-source developers
• Funding is coming in
Critical judgement

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Scientific merit ?
• provides scientific infrastructure
• Easier to write and combine software
• Free, open, interoperable (FOI) language resources
• Free, open NLP test benchmarks (Future work)
• What part is scientific and what part is community work and negotiation?
• No progress in state of the art in NLP methods, yet
• Difficult to judge were to put the emphasis on. Lot of “soft evaluation”
topics, no key performance indicators(KPI) .
Critical judgement

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• 2011: Open Knowledge Conference
• 2012: Workshop and book “Linked Data in Linguistics”
• 2012: Linked Data Cup @ I-Semantics
• 2012: Web of Linked Entities @ ISWC
• 2012: MLODE@ Sabre
• 2013: Semantic Web Journal: Special Issue on Multilingual Linked Open Data
(MLOD)
• Future work: DBpedia & NLP @ ISWC 2013
Conference + Workshops + Proceedings

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Thanks for your attention