conll-rdf is a tool package for converting between formats of annotated linguistic corpora and annotations, as well as linking and enriching these with external ontologies.
It consists of a number of JAVA modules which can be chained to construct a data processing pipeline.

CoNLL-RDF can read and write any annotation format that fulfills the following criteria:

• tab-separated columns (TSV format, i.e., CSV with TAB as separator)
• every row represents one unit of annotation (e.g., a word) along with its annotations
• every column represents one type of annotation (e.g., string form, lemma, parts-of-speech, ...)
• empty lines as sentence separators (optional)
• comments and headlines marked by # (optional)

In particular, it supports all TSV formats used in previous shared tasks of the Conference of Natural Language Learning (CoNLL), the formats of the Universal Dependencies (UD: CoNLL-U, CoNLL-U Plus), Universal Morphologies (UniMorph) and Universal Propositions (UP: skel format) initiatives, and those of SketchEngine and CWB. Some of these formats introduce special notations that extend the basic TSV model (e.g., XML tags). CoNLL-RDF does support these, as well.

• convert any CoNLL-like TSV format (e.g. .conll, .conllu, .tsv, .skel) to conll-rdf (.ttl).
• perform SPARQL Updates on conll-rdf data.
• visualize conll-rdf structure.
• convert conll-rdf back to (any) CoNLL format.

In particular, it serves as a generic converter between various CoNLL/TSV formats. For doing so, it requires the user to provide column labels. Also see CoNLL-Transform for an effort on automated transformation and transformation validation on this basis.

In general, we read data line by line from stdin, process it and write results to stdout.
For quick set-up, we recommend using .sh scripts to pipe your data through the tools.
Each pipeline element can be called via ./run.sh $CLASS [args].

Another method is to configure the pipeline in a config-json and call the entire pipeline with ./run.sh CoNLLRDFManager -c $config-json.

Of course you can also use the provided classes within java as any other library.

Download the repository from GitHub: git clone https://github.com/acoli-repo/CoNLL-RDF.git.

The source is compiled automatically once you run the tool via the run.sh bash script.

• JDK (OpenJDK or Java SE), version 1.8 or higher.
  • run java -version to check if java is installed.
  • run javac -version to check if your version of the compiler is sufficient.
• Maven 3.3+ (Optional but highly recommended).
  • run mvn -version to check if Maven is installed.

All required java libraries are contained in lib/.

• if your pipelines broke with an update in 2020-09 or soon after, you're likely calling the classes directly with javaand not via ./run.sh. You can change your scripts to call ./run.sh (or copy the changes we made to run.sh into your scripts).
• you might get an error like bash: ./../test.sh: Permission denied when trying to run a script. Use this command to change the filemode: chmod +x <SCRIPT>
• an error starting like ERROR CoNLLRDFUpdater :: SPARQL parse exception for Update No. 0: DIRECTUPDATE [...] when running the RDFUpdater can be raised if the path to a sparql query is wrong. Check for extra or missing ../.

All relevant classes are in src/. Documentation for them is found in doc/.

A hands-on tutorial with a variety of sample-pipelines, which you can adapt to your needs, can be found in examples/. These convert data found in data/.

In case your corpus directly corresponds to a format found there you can directly convert it with given scripts into conll-rdf.

Suppose we have a corpus example.conll and want to convert it to conll-rdf to make it compatible with a given LLOD technology. We can do this with a simple shell-command:

cat example.conll | ./run.sh CoNLLStreamExtractor my-baseuri.org/example.conll# \
    ID WORD LEMMA POS_COARSE POS FEATS HEAD EDGE > example.ttl

This will create a new file example.ttl in conll-rdf by simply providing

• a base-URI (ideally a resolvable URL to adhere to the five stars of LOD).
• the names of the CoNLL columns from left to right.

run.sh is used to make things feel more bash-like. It determines the classpath, updates class files if necessary and runs the specified java class with the provided arguments.

• eg. cat foo.ttl | ./run.sh CoNLLRDFFormatter > foo_formatted.ttl would pipe foo.ttl through CoNLLRDFFormatter into foo_formatted.ttl.
• In case the respective .class files cannot be found, run.sh calls compile.sh to compile the java classes from source. Of course, you may also run compile.sh independently.

In-depth information on all the classes of conll-rdf can be found in the documentation.

IMPORTANT USAGE HINT: All given data is parsed sentence-wise (if applicable). Meaning that for CoNLL data as input a newline is considered as a sentence boundary marker (in regard to the CoNLL data model). The ID column (if present) must contain sentence internal IDs (if this is not the case this column must be renamed before conversion/parsing) - if no such column is provided sentence internal IDs will be generated. Please refer to the paper mentioned below under Reference.

CoNLLRDFManager processes a pipeline provided as JSON.
Synopsis: CoNLLRDFManager -c [JSON-config]

CoNLLStreamExtractor expects CoNLL from stdin and writes conll-rdf to stdout.
Synopsis: CoNLLStreamExtractor baseURI FIELD1[.. FIELDn] [-s SPARQL_SELECT]

CoNLLRDFUpdater expects conll-rdf from stdin and writes conll-rdf to stdout. It is designed for updating existing conll-rdf files and is able to load external ontologies or RDF data into separate Graphs during runtime. This is especially useful for linking CoNLL-RDF files to other ontologies.
It can also output .dot graph-files (or triples stdout).
Synopsis: CoNLLRDFUpdater -custom [-model URI [GRAPH]] [-updates [UPDATE]]

CoNLLRDFFormatter expects conll-rdf in .ttl and writes to different formats. Can also visualize your data.
Synopsis: CoNLLRDFFormatter [-rdf [COLS]] [-debug] [-grammar] [-semantics] [-conll COLS] [-query SPARQL]

It can write:

• canonical conll-rdf as .ttl.
• .conll of specified columns.
• TSV to stdout based on a given sparql select query.
• debug highlighted .ttl to stderr, e.g. highlighting triples representing conll columns or sentence structure differently.

Example from universal dependencies.

• grammar: writes conll data structure in tree-like format to stdout. (/ resp. \ are pointing in direction of conll:HEAD)

Example from universal dependencies.

• semantics seperate visualization of object properties of conll:WORD using terms: namespace, useful for visualizing knowledge graphs. EXPERIMENTAL

• can be used to manually annotate / change annotations in .ttl files.
• will visualize input just like CoNLLRDFFormatter -grammar. Will not make in-place changes but write the changed file to stdout (e.g. ./run.sh CoNLLRDFAnnotator file_old.ttl > file_new.ttl)
  • Note: Make sure that input file (file_old.ttl) and output file (file_new.ttl) are different. Piping output into old file in this way is not supported (by your operating system)! Will result in data loss.

• CoNLL2RDF contains the central conversion functionality. For practical uses, interface with its functionality through CoNLLStreamExtractor. Arguments to CoNLLStreamExtractor will be passed through.
• CoNLLRDFViz is an auxiliary class for the future development of debugging and visualizing complex SPARQL Update chains in their effects on selected pieces of CoNLL(-RDF) data
• conll-rdf assumes UTF-8.

• Christian Chiarcos - [email protected]
• Christian Fäth - [email protected]
• Benjamin Kosmehl (2017-2018) - [email protected]
• Luis Glaser (2018-2020)
• Leo Gott (2020-)

See also the list of contributors who participated in this project.

• Chiarcos C., Fäth C. (2017), CoNLL-RDF: Linked Corpora Done in an NLP-Friendly Way. In: Gracia J., Bond F., McCrae J., Buitelaar P., Chiarcos C., Hellmann S. (eds) Language, Data, and Knowledge. LDK 2017. pp 74-88.

This repository has been created in context of

• Applied Computational Linguistics (ACoLi)
• Specialised Information Service Linguistics (FID)
  • funded by the German Research Foundation (DFG, funding code CH1413/2-1)
• Linked Open Dictionaries (LiODi)
  • funded by the German Federal Ministry of Education and Research (BMBF, funding code 01UG1631)
• QuantQual@CEDIFOR (QuantQual)
  • funded by the Centre for the Digital Foundation of Research in the Humanities, Social and Educational Science (CEDIFOR, funding code 01UG1416A)
• Prêt-á-LLOD. Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors (Pret-a-LLOD)
  • Research and Innovation Action of the H2020 programme (ERC, grant agreement 825182)
  • In this project, CoNLL-RDF has been applied/developed/restructured to serve as backend of the Flexible Integrated Transformation and Annotation Engineering (FINTAN) Platform.

This repository is being published under two licenses. Apache 2.0 is used for code, see LICENSE.main. CC-BY 4.0 for all data from universal dependencies and SPARQL scripts, see LICENSE.data.

├── src/  
├── lib/  
├── examples/  
│	├── analyze-ud.sh  
│	├── convert-ud.sh  
│	├── link-ud.sh  
│	└── parse-ud.sh  
├── compile.sh  
└── run.sh  

├── data/  
└── examples/  
	└── sparql/  

Please cite Chiarcos C., Fäth C. (2017), CoNLL-RDF: Linked Corpora Done in an NLP-Friendly Way. In: Gracia J., Bond F., McCrae J., Buitelaar P., Chiarcos C., Hellmann S. (eds) Language, Data, and Knowledge. LDK 2017. pp 74-88.