This paper describes a system for linking the thesaurus of the Netherlands Institute for Sound and Vision to English WordNet and dbpedia. We used EuroWordNet, a multilingual wordnet, and Dutch Wikipedia as intermediaries for the two alignments. EuroWordNet covers most of the subject terms in the thesaurus, but the organization of the cross-lingual links makes selection of the most appropriate English target term almost impossible. Using page titles, redirects, disambiguation pages, and anchor text harvested from Dutch Wikipedia gives reasonable performance on subject terms and geographical terms. Many person and organization names in the thesaurus could not be located in (Dutch or English) Wikipedia.
1.1
For our work on open domain question answering, information extraction, and
coreference resolution, we are interested in creating general, informal, taxonomies of entities
encountered in Dutch texts.1 As part of this work, we created a Dutch counterpart of
the Yago system [
The mapping from the thesaurus of the Netherlands Institute for Sound and Vision
(GTAA) and English Wordnet was accomplished using EuroWordNet [
The alignment procedure is schematically represented in figure 1.
EWN subject label −→
EWN id (near)syn −→
WNMAP
−→ WN1.5 id
WN2.0 id
Entries in the thesaurus are often plurals (afgevaardigden (representatives),
spoorwegen (rail roads), autobussen (buses)), whereas dictionary entries in EWN are
typically singular. To ensure coverage of these cases, all entries in the subject part of the
thesaurus were stemmed using the Alpino parser [
EuroWordNet is a multilingual wordnet, in which each synset is linked to one or
more inter language index ids (ILIs). ILIs in turn are linked to WordNet 1.5 ids. Links
can express among others a synonym, near-synonym, hyponym or hypernym relation.
We used only the synonym and near-synonym relations. Using the ILIs, each Dutch
synset can be linked to an English WordNet id. As we will explain below, this step is in
general one-to-many, as most Dutch synsets are connected to more than one ILI through
the near-synonym relation. In the final step, we mapped WordNet 1.5 ids to WordNet 2.0
ids (the version of WordNet that was used to create the RDF/OWL version of WordNet
that was the target of the mapping), using the WordNet mappings described in [
For linking GTAA entries to dbpedia, we decided to use Dutch Wikipedia as intermediary, and to aim for linking GTAA entries to English Wikipedia pages. Translation of English Wikipedia pages into dbpedia URI’s is done by means of a small script that adds the correct prefix, and deals with special characters. 2 available from from www.lsi.upc.es/∼nlp/tools/mapping.html
For our work on automatic annotation of web pages with links to Wikipedia [
The first step in the alignment is to generate all variants of a label. To match a term in the thesaurus with a Wikipedia page directly, for instance, it is necessary that the first letter is in upper case. Person names in GTAA are given as Lastname, Firstname, whereas Wikipedia simply uses Firstname Lastname. Subjects in GTAA are often plural, whereas they tend to be singular in Wikipedia. Singular forms are obtained from the parsed version of the subject labels that was also used in the aligment with WordNet. Finally, alternative labels provided by GTAA are considered as variants of the concept label.
For all variants of a GTAA concept label, we try to find a matching Dutch Wikipedia page. This can be achieved by an exact match with a Wikipedia page, by an exact match with a redirect page (in which case the target of the redirect is the desired Wikipedia page), by finding a matching anchor text (in which case the most frequent target page for that anchor is returned) or by an exact match with a disambiguation page (in which case all options are returned). Given a suitable Dutch page, we find the English page by following the cross-language link from Dutch to English Wikipedia. In some cases such a link was absent. If a Dutch page (with a corresponding English page) could not be found by means of the techniques above, we tried to find a matching page in English Wikipedia directly, using only page titles.
We expect that there will be a difference in accuracy between the various methods for finding an English page. Preference (and a high confidence score) is given to direct matches, followed by redirects, anchors, direct matches in English, and disambiguation pages. 1.4
The scripts used to produce the alignment can be found at www.let.rug.nl/gosse/ GTAA. Note that EuroWordNet data is missing, as this is a resource which is not in the public domain.
The results of our alignment can be found at www.let.rug.nl/gosse/GTAA/ bouma-vlcr.tgz . 2
We only tried to link GTAA subject entries to WordNet. An overview of the results is given in table 1. Note that coverage is quite reasonable between GTAA and EWN. Where no link could be found, this is mostly due to multiword subject labels (such as alternatieve energie (alternative energy) or bedreigde diersoorten (endangered species)) and compounds. Multiword phrases are generally absent from EWN, and we made no attempt to search for these in English WordNet directly. Other subjects that could not be linked often consist of a compound noun. As compounding is a productive process, we do not expect all compounds to be present in EWN. Given the fact that we do have a morphological analysis, we could have linked compound nouns to a more general concept (i.e. the head noun) by means of a hypernym link. Such links were not part of the task, however. Together, multiword phrases and compounds account for over 80% of the subject labels that could not be linked. 5% coverage was lost in the mapping from WordNet 1.5 to WordNet 2.0.
subject labels
3878 linked to EWN 2617 (67%) unique ILIs 3703 avg. ambiguity 1.4 linked to WN2.0 2392 (62%) unique synsets 3676 avg. ambiguity 1.5
Ambiguity of the target is a serious problem. This is not only caused by the fact that
a word may belong to more than one synset (word sense ambiguity), but also by the fact
that the mapping between synsets in EWN and WN through ILI links is highly
ambiguous. The Dutch nouns part of EWN contains only 631 synonym relation ILIs (which
tend to be unique), and no less than 4641 near-synonym relation ILIs (which tend to
link to several WN targets). One might consider reducing the ambiguity by selecting
the most appropriate word sense for a given subject label. This is by no means trivial
however (see [
vlcr: GTAA to dbpedia Table 2 gives some results for linking the four different parts of the GTAA thesaurus (subject/concepts, names/organisations, locations, and persons) to English Wikipedia. Coverage is best for subjects and locations. GTAA contains many names of persons and concept
EWN synset brons % 10527 & 38608 −→ 03038788 −→ bronze-noun-1 &% −→ 08841702 −→ bronze-noun-2 organisations that seem to be absent in both Dutch and English Wikipedia. It should also be noted that coverage of location names is high only because many location names are found in English Wikipedia directly. This holds partly for names of organisations as well, but less so for person names. For 6 - 9% of the concepts, a Dutch Wikipedia target could be found, but no corresponding English page existed.
subject link type links
name % links
location % links
person % links nlpage redirect anchor enpage In general, it seems that even with relatively modest technology, a mapping between two resources in different languages can be achieved. It should be noted, however, that the mapping to WordNet owes much to the existence of EuroWordNet, which solves the most difficult (cross-language) part of the task to a large extent. On the other hand, EuroWordNet does not help much in deciding which synset for a given English term is the appropriate one.
Our results for Wikipedia linking could still be improved in a number of ways. We hardly employed categorical constraints. The GTAA thesaurus comes in four parts. Each part is a different category. This information could be used to block the link from A4 in the locations file to A4 (paper format) in Wikipedia. Similarly, concept labels often come with a skope note. Word overlap could be used to select the correct target page (i.e. to prefer highway A4 in the Netherlands over that in Austria)). Alternatively, one might use the information that concepts with the same scope note are likely to be linked to Wikipedia pages with identical or closely related Wikipedia categories to detect outliers. For selecting the most promising target, we experimented with a simple preference scheme (which always prefers the link given by the most reliable relation), and a simple weighting scheme (which adds scores when multiple links to the same target are found). Weighting was used for the final results. No doubt, more subtle schemes could be developed. For instance, at the moment we only take into account the most frequent target of an anchor text. Alternatively, one might consider all targets pointed to by anchor text as potential targets, and use the frequency of these links as a weight.
Somewhat surprisingly, we discovered that cross-language links are not reversible. Initially, we used cross-language links harvested from English Wikipedia, as this is the larger resource, and we expected that this might also be more thorough in providing cross-language links. However, since English Wikipedia has more pages than Dutch Wikipedia, several English pages may be linked to the same Dutch page (i.e. Bowling and Ten pin Bowling both point to the Dutch page Bowling). If one works with cross-language links harvested from Dutch Wikipedia, this situation does occur less frequently, although similar problems can occur here as well (i.e. in the versions of Wikipedia we used, the Dutch A4 highway was linked to an English page which redirected to a general page on Dutch highways). 4
We have presented a method for linking the thesaurus of the Netherlands Institute for Sound and Vision with two English resources, WordNet and Wikipedia. We used an ad-hoc method which relied on the existence of cross-language links for similar data, namely EuroWordNet, a multi-lingual wordnet with cross-language links, and Dutch Wikipedia, which contains cross-language links to English Wikipedia.