Wiktionary RDF extraction

Currently available languages:
English, German, French, Russian, Greek, Vietnamese
In the works: Greece, Vietnamese
Need data from other languages? Help us creating wrappers for each language editions (if you know Regex, XML and Wiktionary, an initial wrapper can be created in less than one day.)

Intro

Wiktionary, the free dictionary, is another project of the Wikimedia Foundation from where DBpedia extracts structured RDF data. Just like Wikipedia, Wiktionary comes in many languages, such as the English Wiktionary (http://en.wiktionary.org) and German Wiktionary (http://de.wiktionary.org). However, each of these independent sites contains entries in many languages. For the French word deux, there is one entry in the English Wiktionary (http://en.wiktionary.org/wiki/deux) and another entry in the German Wiktionary (http://de.wiktionary.org/wiki/deux). For a word such as in, a single wiki page in the English Wiktionary (http://en.wiktionary.org/wiki/in) contains sections for 24 different languages that use this word. (This is quite different from how Wikipedia handles disambiguation pages.) The exact structure for the entries differs between languages of Wiktionary, and slightly also between language entries within each site, so that Danish and Dutch entries in the English Wiktionary may use different kinds of wiki templates.


We aim to provide an open-source framework (based on DBpedia) to extract semantic lexical resources (an ontology about language use) from Wiktionary. The data currently includes language, part of speech, senses, definitions, synonyms, taxonomies (hyponyms, hyperonyms, synonyms, antonyms) and translations for each lexical word. Main focus is on flexibility (to the loose schema) and configurability (towards differing language-editions of Wiktionary). The configuration is done within an XML file which encodes language-mappings and parse-templates containing place holders. The goal is to allow the addition of languages just by configuration without the need of programming skills and without altering the Scala source code. This enables the quick and cheap ad(o|a)ption to new usage scenarios. The extracted data can (due to its semantically richness) be automatically transformed into the Lemon model or simpler domain specific formats – for example a CSV representation of the translation, which can be loaded into a relational database. By offering a Linked Data service, we hope to extend DBpedia's central role in the LOD infrastructure to the world of Open Linguistics. The RDF dump currently contains 100 million triples.


Note that Wiktionary, Wikipedia and DBpedia are all different projects.
Please send feedback to the official Wiktionary mailing list as we are coordinating all extraction approaches from there:
https://lists.wikimedia.org/mailman/listinfo/wiktionary-l


More information can be found in Jonas Brekle's master thesis
http://lips.informatik.uni-leipzig.de/pub/2012-12


or in the paper published at JIST 2012
or in the corresponding slides I prepared.
This page is organized by user type:


Contents

End User – get the data

Access

* Dumps: http://downloads.dbpedia.org/wiktionary/ (look for the latest file)
* SPARQL-Endpoint: http://wiktionary.dbpedia.org/sparql
* Linked Data: http://wiktionary.dbpedia.org/resource/dog (example)
* Virtuoso Faceted Browser: http://wiktionary.dbpedia.org/fct/

Sample data

triples for the word “dog” (shortened and using prefixes for better readability)
http://wiktionary.dbpedia.org/resource/dog


wr:dogdoap:creator<http://en.wiktionary.org/w/index.php?title=dog&action=history>
wr:doglemon:sensewr:dog-English-Noun-1
wr:dogrdfs:labeldog@en
wr:dogrdfs:seeAlso<http://en.wiktionary.org/wiki/dog>
wr:dogrdf:typelemon:LexicalEntry
wr:dogrdf:typewt:LexicalEntity
wr:dogwt:hasLangUsagewr:dog-English
wr:dog-Englishdc:languagewt:English
wr:dog-Englishwt:hasPoSUsagewr:dog-English-Noun-1
wr:dog-English-Nounwt:hasPoSwt:Noun
wr:dog-English-Nounwt:hasSensewr:dog-English-Noun-1
wr:dog-English-Noun-1dc:languagewt:English
wr:dog-English-Noun-1wt:hasPoSwt:Noun
wr:dog-English-Noun-1wt:hasExample'The `dog` barked all night long.'@en
wr:dog-English-Noun-1wt:hasMeaning'An animal, member of the genus `Canis` (probably descended from the common wolf) that has been domesticated for thousands of years; occurs in many breeds. Scientific name: `Canis lupus familiaris`.'@en
wr:dog-English-Noun-1rdf:typewt:Sense
wr:dog-English-Noun-1rdf:typelemon:LexicalSense
....................................................

notice the nested schema (lexical entry -> language-usage -> Po S-usage -> senses -> properties). This hierarchy represents the sections from the wiktionary article. There is no normalization/transformation happening. Different language editions of wiktionary could use a different schema, and you could call that inconsistent. But in fact we believe it is better to keep the data “as is” – efforts on consolidation should be taken on the wiktionary side.

SPARQL Access

get meanings of a word

SELECT ?meaning 
FROM <http://wiktionary.dbpedia.org> 
WHERE {
  ?lexword <http://www.w3.org/2000/01/rdf-schema#label> "house"@en .
  ?lexword <http://www.monnet-project.eu/lemon#sense> ?sense .  
  ?sense <http://wiktionary.dbpedia.org/terms/hasMeaning> ?meaning
}

get 10 translation pairs
SELECT ?a ?b 
FROM <http://wiktionary.dbpedia.org> 
WHERE { 
  ?a <http://wiktionary.dbpedia.org/terms/hasTranslation> ?b 
LIMIT 10


or a bit more fine granular

PREFIX terms:<http://wiktionary.dbpedia.org/terms/>
PREFIX rdfs:<http://www.w3.org/2000/01/rdf-schema#>
PREFIX dc:<http://purl.org/dc/elements/1.1/>
SELECT ?sword ?slang ?spos ?ssense ?twordRes ?tword ?tlang
FROM <http://wiktionary.dbpedia.org>
WHERE {
    ?swordRes terms:hasTranslation ?twordRes .
    ?swordRes rdfs:label ?sword .
    ?swordRes dc:language ?slang .
    ?swordRes terms:hasPoS ?spos .
    OPTIONAL { ?swordRes terms:hasMeaning ?ssense . }
    OPTIONAL { 
           ?twordBaseRes terms:hasLangUsage ?twordRes . 
           ?twordBaseRes rdfs:label ?tword .
    }
    OPTIONAL { ?twordRes dc:language ?tlang . }
}

Statistics

There is a global graph http://wiktionary.dbpedia.org/ that contains all 4 datasets combined. But you can also query them separately. When using Linked Data, you will always see all data.


triples per graph

http://en.wiktionary.dbpedia.org/ 64,358,374
http://fr.wiktionary.dbpedia.org/ 32,528,568
http://ru.wiktionary.dbpedia.org/ 12,110,892
http://de.wiktionary.dbpedia.org/ 5,077,573

properties count

http://www.w3.org/2000/01/rdf-schema#seeAlso30010086
http://www.w3.org/2000/01/rdf-schema#label12830014
http://wiktionary.dbpedia.org/terms/hasMeaning2415709
http://wiktionary.dbpedia.org/terms/hasTranslation1410045
http://wiktionary.dbpedia.org/terms/hasEtymology1350075
http://wiktionary.dbpedia.org/terms/hasCollocation539448
http://wiktionary.dbpedia.org/terms/hasPronunciation329372
http://wiktionary.dbpedia.org/terms/hasAnagram316553
http://wiktionary.dbpedia.org/terms/hasExampleSentence308288
http://wiktionary.dbpedia.org/terms/hasSynonym304992
http://wiktionary.dbpedia.org/terms/hasAlternativeForm57622
http://wiktionary.dbpedia.org/terms/hasExample53368
http://wiktionary.dbpedia.org/terms/hasPluralForm50119
http://wiktionary.dbpedia.org/terms/hasAntonym47931
http://wiktionary.dbpedia.org/terms/hasDerivedTerm37668
http://www.w3.org/2002/07/owl#sameAs25155
http://wiktionary.dbpedia.org/terms/hasQuotation19567
http://wiktionary.dbpedia.org/terms/hasHomonym17048
http://wiktionary.dbpedia.org/terms/hasDifferingOrthography15235
http://wiktionary.dbpedia.org/terms/hasHyponym7551
http://wiktionary.dbpedia.org/terms/hasProverb6891
http://wiktionary.dbpedia.org/terms/hasCoordinateTerm4438
http://wiktionary.dbpedia.org/terms/hasHyperonym3056

Usage Scenarios

  • Reference, Annotation – annotate corpora with unique identifiers. Then you get all infos from Wiktionary via linked data
  • disambiguation – for a given lexical word (a sequence of characters) one can look up possible usages in languages, and possible meanings (each meaning should have a definition and a example sentence). The definition can help to determine which meaning was intended by the author (by comparing the context to possible definitions, etc.)
  • synset reduction – for a given word one can lookup synonyms and replace the word by a deterministically chosen representative of its synset.
  • translation – the provided translations are defined on the meanings, which gives you a context aware translation.

Wiktionary 2 RDF – Live

We will provide a live version of the ontology soon, that reflects changes to the wiki within seconds. This should encourage users of the RDF data to contribute to Wiktionary. If you are unhappy with the data quality of the automatically generated ontology, you just edit the wiki, improve the guideline-compliance there and you will get your high-quality semantic data right away.

Contributor – add new Wiktionary

The main target of this approach is to be extendible for new Wiktionary language editions by non-programmers.
This is done by supplying a new configuration file for example “config-en.xml”. To create this config, you will need to follow these steps:

Step 1: get a copy the software


  1. Install git and Maven
  2. checkout the repo

git clone https://github.com/dbpedia/extraction-framework.git dbpedia


  1. build

cd dbpedia && mvn install


Step 2: Download the Wiktionary XML dump

If you just want to get started, you can use the example files in wiktionary/sample-xml-dumps. If you want the real data,
go to the wikimedia dump archive, search the latest version of the targeted language. Download the article dump file "[...]-pages-articles.xml" and put into a directory structure like the one in wiktionary/sample-xml-dumps .


Step 3: Configure

  1. Get the configuration files from https://github.com/dbpedia/dbpedia-wiktionary-configuration and place them in the wiktionary/config directory
  2. Copy the config.properties.default to config.properties and adjust it 
  3. Check configuration in the folder wiktionary/config

config.xml //general config for loglevel and language
config-xx.xml // most action happens here


Look at existing configs to get started, a complete documentation will be available soon.
For debugging, you can also test the configuration with single pages: Media Wiki can export pages in the dump format easily. Just put a "Special:Export/" before the page name. example. The extractor will use the file with the latest modified timestamp in the “wiktionaryDump” folder.
After changes to the config, try and run in the wiktionary directory

mvn scala:run


The extracted triple can be found in the output folder.
To determine what goes wrong, you can also increase the loglevel (to 4), pipe the debug to a file, and try to trace the error. That is very verbose, and hard to read, but the best way to find out whats happening.


When you are satisfied to the data, you can load it into virtuoso (use isql console)

ttlp_mt (file_to_string_output (#039;/path/to/file/wiktionary.dbpedia.org_en.nt'), '', 'http://wiktionary.dbpedia.org/');

Developer – improve the framework

I will try to explain internals here, but its quite complex and I omitted some details. If you cant follow, ask on the mailinglist or wait for my thesis (first week of August 2012).


The idea is somewhat different from DBpedia (although we use the framework):
Instead of info boxes and very specific extractors we tried to make a meta-extractor of declarative nature instead of imperative.
The rational is that although there exist some scrapers, none of them allows to parse more than a 3–5 languages.
So we encode the language-specific characteristics of each Wiktionary in a machine-readable format (e.g. the “config-de.xml”) and create a generic extractor that interprets the config.
Top-down these properties are:

Entry Layout (EL)

e.g. in the german Wiktionary, a given page has the structure:

Lexical Entity
  languages it occurs in 
    part of speech it is used as
      different senses/meanings
        properties like 
        synonyms, 
        example sentence,
        pronunciation, 
        translations, etc.

In the English Wiktionary there is a etymology section after the language. The used schema can differ between language-editions of wiktionary. So we came up with a simple encoding scheme, to represent the expected schema for each language. We configure the EL with nested XML-nodes named „block“'s (and the top level node is named „page“). Each block can contain three things:
further blocks (to represent the hierarchy)
and indicator template – if it is encountered on the page, this block starts
declaration on how to parse the page on this level (see below).
More about the EL: http://en.wiktionary.org/wiki/Wiktionary:Entry_layout_explained

Wiki Templates

now we come to the core of the extraction. We made an engine that can
match a given Wiktionary page to several “extraction templates” (ET).
So consider this dummy page (written in German – to show the effect of language mappings):

== green (Englisch) ==
  === Adjektiv ===
  [1] eine Farbe
  [2] umweltfreundlich

We can start with this snippet wikitext and transform it to a generic schema definition by introducing placeholders a.k.a. variables and control symbols that indicate the possible repeating of parts (like the regex "(ab)*" matches “ababab”). The engine then fills the placeholders with information scraped from the page (in other words binds variables). The configuration contains declaration on
what to do with the bound variables, often that is a “use it as literal object of predicate x” but also arbitrary transformations
like “format to URI sprintf-style” or “hand bindings to a static method on class y, that returns triples”. An example: we have the example page and we defined a config like this:
<page name="page">
  <block name="lang">
    <indicator name="lang-start">
      <resultTemplates>
        <resultTemplate>
          <triples>
            <triple s="$block" p="http://wiktionary.dbpedia.org/terms/hasLangUsage" o="$block-uri(map($language))" oType="URI" oNewBlock="true" />
            <triple s="$block" p="http://purl.org/dc/elements/1.1/language" o="http://wiktionary.dbpedia.org/terms/uri(map($language))" oType="URI" />
          </triples>
        </resultTemplate>
      </resultTemplates>
      <wikiTemplate>== $word ({{Sprache|$language}}) ==</wikiTemplate>
    </indicator>
    <block name="pos">
      <indicator name="pos-start">
        <resultTemplates>
          <resultTemplate>
            <triples>
              <triple s="$block" p="http://wiktionary.dbpedia.org/terms/hasPoSUsage" o="$block-uri(map($pos))" oType="URI" oNewBlock="true" />
              <triple s="$block" p="http://wiktionary.dbpedia.org/terms/hasPoS" o="http://wiktionary.dbpedia.org/terms/uri(map($pos))" oType="URI" />
            </triples>
          </resultTemplate>
        </resultTemplates>                    
        <wikiTemplate>=== {{Wortart|$pos|$language}} ===</wikiTemplate>
      </indicator>
      <templates>
        <template name="definitons">
          <resultTemplates>
            <resultTemplate>
              <triples>
                <triple s="$block" p="http://wiktionary.dbpedia.org/terms/hasDefinition" o="$definition" oType="literal" />
              </triples>
           </resultTemplate>
         </resultTemplates>  
         <wikiTemplate>([$id$definition
)*
</wikiTemplate>
        </template>
     </block>
  </block>
</page>

There are several things to notice: the nested structure of the block-nodes (implying the schema), the indicator-nodes, which contain templates, that if they are encountered on the page, imply that the following belongs to that block. Then you will see the normal tempate-nodes that contain information on how to parse the page, using placeholders, and how to emit triples, again with these placeholders. And (in the templates) you will see the control structures looking like Regular Expressions but we only allow ()*, ()+, ()?.
We hope you agree, that this approach is pretty generic and declarative, rather than imperative, thus enabling a quick creation of new configs.
For the definition template, the engine will find a set of bindings:
definition -> "eine Farbe"
definition -> "umweltfreundlich"

and then generates triples according to the resultTemplates, which again contain the placeholders
wr:green-english-adjective wiktionary:hasDefinition "eine Farbe" .
wr:green-english-adjective wiktionary:hasDefinition "umweltfreundlich" .

the used properties and namespaces are open to discussion.


Our prototype recognizes the EL and thus gives information about Languages and Po S usages of all
words in the Wiktionary and has ETs for the definitions, hyphenation, synonyms (to have community based wordnet), translations, hyponyms, hyperonyms and example sentences.

Mapping

a mapping from language-specific tokens to a global vocabulary. In contrast to the DBpedia mappings, we dont map property labels to property URIs, but we map any token or value to a shared vacabulary (not URIs directly). It just means occurring labels within the EL. Their meaning has to be translated to a shared, global vocabulary.
In this wiki snippet there are two tokens: “Englisch” and “Adjektiv”. They
indicate that the following section is about a english word and its part
of speech is adjective. Obviously we need a mapping from them to a
shared vocabulary. Such a mapping is easy and part of the configuration.
But a nice thing to have would be a better ontology backing of this vocabulary.
some ontology about POS (GOLD?) and language families (ISO 639–3?)) – we
should discuss what to use there.

Use Case: Relational View on Translations

A requirement for a related project was the transformation of translations into a relational schema.
This is achieved with a SPARQL query, that interprets the graph model to a row based schema and retrieves them.
The result is saved into a CSV file – which can be loaded into a SQL DBMS with the built-in LOAD DATA INFILE command.
There are two scripts to accomplish that functionality:
to-csv.php – a php shell script that queries the public SPARQL endpoint of wiktionary.dbpedia.org and retrieves 10.000 wordpairs each request and transforms and writes them into a CSV file.
The columns are:
sword (the lexical source word), slang (the language of the word), spos (the part of speech of the word), ssense (a sense of the word), tword (the target word), tlang (the language of the target word).
An example entry could be:
«abändern»,"German","Verb","to change, to alter","change","English"
The process of the extraction takes a few hours. There are no configuration options but the script can be changed easily.
Optionally you should remove duplicate lines with uniq.


load-csv.sql – a trivial sql script that creates a table with the above explained columns and inserts the CSV with a LOAD DATA command.


an example dataset for english <-> german, can be found here

Vocabulary: Lemon Model

the dataset contains contains triples using the lemon vocabulary.
This is achieved by postprocessing the raw dataset with a recursive algorithm that “presses” the hierarchical schema into a flat one.
For example

A
  p1=x
  A1
    p2=y
    A11
      p3=z
    A12
      p4=w

becomes
A
  instance of Lexical Entry
  A11
    instance of Lexical Sense
    p1=x
    p2=y
    p3=z
  A12
    instance of Lexical Sense
    p1=x
    p2=y
    p4=w

notice the propagtion of properties along the path, down to the leaves. This transformed data is then added as an overlay over the data – thus creates shortcuts from the top entry to the leaves.

Todo / Issues

  • resolving templates
  • use a nice vocabulary
  • live
  • link with other resources (wordnet, etc.)
  • document used vocabulary

 
There are no files on this page. [Display files/form]
There is no comment on this page. [Display comments/form]

Information

Last Modification: 2014-11-29 16:49:54 by Karl Bartel