=Paper=
{{Paper
|id=Vol-2773/wdw8
|storemode=property
|title=SectionLinks: Mapping Orphan Wikidata Entities onto Wikipedia Sections
|pdfUrl=https://ceur-ws.org/Vol-2773/paper-14.pdf
|volume=Vol-2773
|authors=Natalia Ostapuk,Djellel Difallah,Philippe Cudre-Mauroux
|dblpUrl=https://dblp.org/rec/conf/semweb/OstapukDC20
}}
==SectionLinks: Mapping Orphan Wikidata Entities onto Wikipedia Sections==
https://ceur-ws.org/Vol-2773/paper-14.pdf
SectionLinks: Mapping Orphan Wikidata
Entities onto Wikipedia Sections
Natalia Ostapuk1 , Djellel Difallah2 , and Philippe Cudré-Mauroux1
1
University of Fribourg, Fribourg, Switzerland {firstname.lastname}@unifr.ch
2
New York University, New York, USA djellel@nyu.edu
Resource Type: Dataset
Permanent URL: http://doi.org/10.5281/zenodo.3840622
Abstract. Wikidata is a key resource for the provisioning of structured
data on several Wikimedia projects, including Wikipedia. By design, all
Wikipedia articles are linked to Wikidata entities; such mappings repre-
sent a substantial source of both semantic and structural information.
However, only a small subgraph of Wikidata is mapped in that way –
– only about 10% of the sitelinks are linked to English Wikipedia, for
example. In this paper, we describe a resource we have built and pub-
lished to extend this subgraph and add more links between Wikidata
and Wikipedia. We start from the assumption that a number of Wiki-
data entities can be mapped onto Wikipedia sections, in addition to
Wikipedia articles. The resource we put forward contains tens of thou-
sands of such mappings, hence considerably enriching the highly struc-
tured Wikidata graph with encyclopedic knowledge from Wikipedia.
Keywords: Wikidata · Wikipedia · Linked Data.
1 Introduction
Knowledge Graphs (KGs) provide a rich, structured, and multilingual source
of information useful for a variety of applications that require machine-
readable data. KGs are leveraged in search engines, natural language un-
derstanding, and virtual assistants, to name but a few examples. A KG is usu-
ally represented as a graph of vertices denoting entities and connected with
directed edges depicting their relationships. KGs can be constructed auto-
matically using information extraction techniques, or semi-automatically, as
is the case with Wikidata3 , a KG built and maintained by a community of
volunteers. Wikidata has the advantage of being curated by humans and of
being tightly integrated with multiple Wikimedia projects (e.g., Wikipedia,
Wikimedia Commons, and Wiktionary). For example, every Wikipedia article
3
https://www.wikidata.org/
Copyright ©2020 for this paper by its authors. Use permitted under
Creative Commons License Attribution 4.0 International (CC BY 4.0).
2 N. Ostapuk et al.
across all languages has a corresponding and unique language-independant
Wikidata entity. This mapping between Wikipedia and Wikidata is beneficial
for both projects. On one hand, it facilitates information extraction and stan-
dardization of Wikipedia articles across languages, which can benefit from
the standard structure and values of their Wikidata counterpart, e.g., for
populating infoboxes. On the other hand, Wikipedia articles are routinely
updated, which in turn keeps Wikidata fresh and useful for online applica-
tions.
However, the Wikipedia editorial guidelines require that an entity be no-
table or worthy of notice to be added to the encyclopedia, which is not the
case of Wikidata. Hence, only a fraction of Wikidata entities has a corre-
sponding article in any language. We refer to the remaining entities, without
an article, as orphans. In the absence of a textual counterpart, orphans often
suffer from incompleteness and lack of maintenance.
Our present work stems from the observation that a substantial number
of orphan entities are indeed available in Wikipedia, but not at the page
level; orphan entities can be described within existing Wikipedia articles in
the form of sections, subsections, and paragraphs of a more generic concept
or fact. Interestingly, even a short section describing an orphan Wikidata en-
tity can carry useful information that could enrich the entity with additional
facts and relationships. Such pieces of information are unfortunately buried
inside long articles without direct relevance to the main subject. Instead,
we propose to establish a fine-grained mapping between Wikidata orphan
entities and Wikipedia (sub)-sections.
Our main contribution is a dataset of such mapping between Wikidata
and Wikipedia sections that we created using several algorithmic methods,
ranging from string matching to graph inference.
2 Related Work
To the best of our knowledge, we are the first to come up with a resource
providing fine-grained mappings between Wikipedia and Wikidata; our map-
pings come in addition to the existing links that Wikipedia provides to Wiki-
data through section anchors (see Section 3).
A similar effort of matching entities to Wikipedia articles was made by
Tonon et al. in [18]. The paper addresses the problem of constructing a
knowledge graph of Web Entities and mapping it onto DBpeadia with Wikipedia
articles acting as DBpedia entries.
Our effort is not directly related to link prediction [10,12], which typically
operates in a homogeneous domain (e.g., when trying to infer new links in
a given social network or knowledge graph), while we operate across two
heterogeneous domains (i.e., Wikidata and Wikipedia). It is however related
to Ad-hoc Object Retrieval techniques [13,17], which retrieve target entities
SectionLinks: Mapping Orphan Wikidata Entities onto Wikipedia Sections 3
based on keyword or natural language queries, as well as to Entity Linking
[16,4,1,11], which attempts to link mentions in Web text to their referent
entities in a knowledge base.
A special case of Entity Linking is Wikipedia Linking, which aims at dis-
covering links between Wikipedia documents. This task was broadly studied
within the Wiki track of INEX4 conference [7,5,6]. Participants were invited
to establish links between Wikipedia articles both at the page and text level
(i.e. detect an anchor point in the text of the source document and a best
entry point in the text of the target). The task of linking documents at the
text level is of particular interest to us as it is a general case of linking a
document to a section and closely relates to the main topic of this paper. A
number of interesting approaches were developed both for identifying link
source and target pages [8] and detecting the best entry point inside the
text of the target [3].
Our work is also directly related to information extraction [15] and KG
construction [14] efforts. In that context, a number of systems have re-
cently been proposed to extract information, often in the form of triples,
from structured or unstructured content and link it to a semi-structured
representation like a knowledge graph. DeepDive [19], for instance, is a
well-known tool that employs statistical learning, inference and declarative
constructs written by the user to build a knowledge base from a large col-
lection of text. FRED [2] is a machine-reading tool that automatically gen-
erates RDF/OWL ontologies and linked data from multilingual natural lan-
guage text. MapSDI [9] is a recent rule-based mapping framework for inte-
grating heterogeneous data into knowledge graphs. It takes as input a set of
data sources and mapping rules to produce a consolidated knowledge graph.
None of those tools is readily applicable to our problem of linking Wikipedia
sections to Wikidata entities, however.
3 Relevance and Use Cases
In Wikidata, entities are characterized by a unique identifier (a sequential
integer prefixed with Q), multilingual labels, descriptions, and aliases when
available. Each entity may have multiple statements to express a property or
a relationship with another entity. An entity can have Sitelinks5 referencing
other Wikimedia projects. These are hyperlinks that establish an identity
mapping between the entity and, for instance, a Wikipedia page. Thanks to
Sitelinks, Wikidata is often utilized as a hub for multilingual data, connecting
a given concept to articles written in a dozen languages.
To understand Wikidata’s Sitelinks coverage, we collected the number of
label entities per language. We focus on the 15 languages having +1 Million
4
International Workshop of the Initiative for the Evaluation of XML Retrieval
5
https://www.wikidata.org/wiki/Help:Sitelinks
4 N. Ostapuk et al.
English
Dutch
Spanish
French
German
Italian
Russian
Swedish
Ukranian
Chinese
Portuguese
Polish
Vietnamese
Japanese
Orphan Entities
Arabic Articles
1 5 10 30 50
Count (in Mio, log scale)
Fig. 1. Language statistics and gaps in Wikidata.
Wikipedia articles (see Section 4). We examined the number of orphan enti-
ties (defined above in Section 1) having a label in each language, as shown
in Figure 1, which we contrast to the number of available Wikipedia arti-
cles. We see that the gap between the number of orphans and articles is
much higher for languages having more labels. In fact, English Wikipedia,
the largest and most active project of all Wikis, links to only about 10% of
all Wikidata entities having an English label. This discrepancy signals a ne-
cessity to close this gap using alternate methods.
This work aims to identify potential orphan entity textual content that
may exist within Wikipedia in the form of sections. This content can be linked
using anchor links to article sections. Currently, Wikidata does not support
using anchors links as a Sitelink, i.e. linking to a specific section of a page.
Fig. 2. A list of Wikidata sitelinks on a Wikipedia page. The French link points to:
https://fr.wikipedia.org/wiki/Analys_de_survie#Fonction_de_survie
SectionLinks: Mapping Orphan Wikidata Entities onto Wikipedia Sections 5
Wikidata Entities
Brexit
Q7888194
European Union
(Withdrawal) Act
2018
Q29582790
exit day
Q59189602
Fig. 3. An example where linking orphan entities from Wikidata (Q29582790 and
Q59189602) to their corresponding section in Wikidata has the potential to greatly
improve both sources by extracting key data from text.
It is worth noting that inter-language Wikipedia can perform this operation,
for example, the Wikidata entity Q2915096 contains a Sitelink to the English
Wikipedia page Survival_function, and all the other Wikidata sitelinks are
listed on this page in the left column (Figure 2). A link to a section can be
added to this list and thus can be mapped to the source Wikidata entity, as is
the case for the French language. Unfortunately, this is done inconsistently
and provides only an indirect mapping to Wikidata, and also assumes that
at least one language has a dedicated Wikipedia page for the entity. Our
proposed resource fills this important gap by building an external resource
to map Wikidata orphans to Wikipedia, without entering a sitelink.
Figure 3 illustrates what we want to achieve. It depicts the Wikipedia
entry for Brexit. While the Brexit entity (Q7888194) from Wikidata cor-
rectly links to that page, two related Wikidata entities are orphans: Euro-
pean Union (Withdrawal) Act 2018 (Q29582790) and exit day (Q59189602).
Linking those two entities to their corresponding sections in Wikipedia, as
6 N. Ostapuk et al.
shown in the figure, would provide important information and context to
Wikidata and greatly improve a number of key downstream tasks such as
ad-hoc object retrieval, joint embeddings, or question answering.
4 The Dataset
We developed two different algorithms to derive mappings from Wikidata
entities to Wikipedia sections. We ran both our algorithms on 15 languages
and obtained tens of thousands of new links in the process (see section 4.2
for details). The two resulting datasets complement each other (i.e. contain
sitelinks for different sets of entities) and are both available as part of our
resource. The rest of this section describes our methods and results in de-
tail, and provides performance numbers and illustrative examples to better
assess the usefulness of our resource.
4.1 Data Generation Pipeline
We consider a bipartite graph G whose vertices consist of two disjoint sub-
sets: D , representing Wikidata entities that are missing a Wikipedia link, and
P , representing Wikipedia page sections. Our goal is to correctly match as
many vertices as possible from D to P (i.e., to create as many correct links
as possible between Wikidata entities and Wikipedia section). To help with
this task, we use existing labels and statements available from each entity,
as well as the section titles that we collect from the 15 Wikis. We proceed in
four steps:
Candidates selection: the first step is to identify candidates, both from
Wikidata (D vertices) and Wikipedia (P vertices), in order to create the
matching graph G .
Key generation: then, we create a key (or a set of keys) to represent each
vertex in D and P .
Matching: at this stage, we create candidate links between by matching
keys in D with keys in P .
Filtering: finally, as the matching step may result in many false positive
links, we consider a postprocesing step where each resulting link is vet-
ted against a set of roles or conditions.
We describe two different instantiations of our data generation pipeline
below: one considering a strict all-to-all matching algorithm, and a second,
graph-based algorithm that takes into account the neighborhood of each
candidate.
SectionLinks: Mapping Orphan Wikidata Entities onto Wikipedia Sections 7
All-to-All Matching Algorithm Our first approach considers a complete
bipartite graph, where each Wikidata entity in D is matched to all Wikipedia
sections in P . Since we do not apply any restriction on the candidate targets
(Wikipedia pages) and since the number of matches grows quadratically,
the key comparison method and the filtering functions both have to be very
strict, otherwise the algorithm would return a lot of false positive matches.
We achieve this with the requirement that a Wikipedia key has to comprise
all tokens from both the page title and the section title; as such, a Wikipedia
key is specific enough to guarantee with a high probability that it refers to
the same object as a corresponding Wikidata entity.
Candidates selection First, we identify all orphan Wikidata entities, i.e. all
entities that have a label in a given language but do not have a sitelink to
a corresponding Wikipedia page or section. Orphans are further filtered by
type to exclude service pages like categories or templates, as well as some
types which have homonymous labels but rarely match any Wikipedia sec-
tion (for example, an entity of the type painting with the label The Crucifix-
ion match the Wikipedia section describing the crucifixion of Jesus, which is
irrelevant to this object).
Key generation We consider a set of keys for each Wikidata candidate in D .
This set of keys consists of its label and all its aliases for a given language.
For example, for an entity Q63854053 the set of keys will be {“spun silk”,
“noil”, “silk noil”}. To generate keys for Wikipedia page section in P , we con-
catenate the page title with the section title. After all keys are generated, we
split each key into tokens, remove punctuation and stop-words, sort tokens
in alphabetic order and concatenate them back together. We used stop word
lists provided by the NLTK package6 in this context.
Matching The output of the key generation step consist of two key-value
tables: one for Wikidata entities, where the keys are as described above
and each value is an entity id, and another for Wikipedia sections, with
(page_title−section_title) pairs as values. These two tables are then joined by
key and grouped by QID (Wikidata entity id). This operation was performed
on a Hadoop cluster.
Filtering The last step of the pipeline is result filtering. As mentioned above,
this approach considers all possible matches and hence may bring up a lot of
false positives, therefore the filtering function we use is strict also: we keep
only those QIDs for which exactly one Wikipedia section was found. In more
formal terms, this step of the algorithm checks the output of the groupBy
operation and filters out records which grouped more than one value per
QID. Figure 4 outlines the overall pipeline of our first approach.
6
https://www.nltk.org
8 N. Ostapuk et al.
Fig. 4. All-to-all matching pipeline
Neighbors Matching Algorithm Although the above algorithm demon-
strates a good performance (over 80% precision for English Wikipedia), a
manual analysis reveals that it has a relatively low recall. The reason is that
in many cases, when a Wikipedia section describes an object its title is self-
sufficient, i.e. it stands on its own and does not depend on the title page to
identify the object. Hence, matching a Wikidata label strictly with a combina-
tion of a page and section title results in a lot of false negatives as the page
title introduces redundancy. On the other hands, matching with section titles
only will significantly drop the precision in general. To tackle this problem,
we restrict the set of candidate Wikipedia sections for each Wikidata entity
by leveraging the Wikidata graph structure.
Candidates selection Our candidate selection algorithm in this case is based
on the assumption that a Wikipedia page that is “semantically” related (e.g.,
to a subclass relation) to a Wikidata entity in D is more likely to contain a
section relevant to that entity.
We introduce a second condition to further restrict the candidates as fol-
lows: a candidate in P should be related to one and only one source entity
in D for a particular edge type. For example, consider an orphan entity bad-
minton racket and a triple [(badminton), (uses), (badminton racket)]. Here,
(badminton) is a good candidate, because it is linked to (badminton racket)
with the relation [uses]. On the other hands, in the triple [(Sofia Shinas),
(occupation), (singer)], (singer) is not an interesting candidate for (Sofia
Shinas), as many entities have an occupation singer.
SectionLinks: Mapping Orphan Wikidata Entities onto Wikipedia Sections 9
As such, we developed the following algorithm pipeline for selecting can-
didate Wikipedia sections using a graph-based approach:
– Identify an orphan Wikidata entity;
– Collect its neighbor entities following all incoming and outgoing edges;
– Filter out neighbors that do not have a Wikipedia sitelink;
– Filter out neighbors with non-unique edges;
– Extract Wikipedia sitelinks from the remaining neighbors;
– Consider sections of the resulting Wikipedia pages as candidates for
matching.
This algorithms yields excellent results in practice as we describe below
in Section 4.3.
Key generation As we significantly limited the set of candidate Wikipedia
sections in P , we consider a different way of constructing the keys. First, we
do not always consider the tokens from the page title for the keys in P (al-
though they may be included). Second, in addition to removing stop words
and punctuation, we consider a third postprocessing step by stemming the
key tokens, i.e. we remove all affixes that mostly carry morphological in-
formation and only keep just their root (e.g. words works, worked, working
are all reduced to work ). Finally, we remove disambiguation tokens from
Wikipedia page titles: when a title is ambiguous, a disambiguation word or
phrase can be added in parenthesis. For example, titles Mercury (element),
Mercury (planet) and Mercury (mythology) are all reduced to Mercury.
Matching The matching step is similar to the one in our first algorithm, but
instead of running a join of two tables we process each Wikidata entity in D
individually. If one of the Wikidata keys exactly match a Wikipedia section
key, we consider the section as a potential sitelink for this entity.
Filtering Due to the various manipulations we consider on the keys, we may
end up with situations where different Wikipedia sections have the same
key that matches a Wikidata key. For a example, an article Rotterdam Metro
includes two sections: Line D and Lines. After stemming and stop words
removal, both section titles are reduced to line. If we consider a Wikidata
entity with a label Line D (which is also reduced to line), we get two potential
matches. In that case, we consider the edit distance between the Wikidata
label and the section title (in their original forms) and pick the closest match
to break the tie.
10 N. Ostapuk et al.
Table 1. Datasets size per language
All-to-all Neighbors Final
matching matching
Arabic 672 1752 1792
Chinese 38 848 882
Dutch 2699 5230 5644
English 9834 25469 30351
French 6573 10436 13098
German 3923 9140 10443
Italian 8512 27775 32262
Japanese 215 4105 4229
Polish 663 678 1271
Portuguese 399 1188 1407
Russian 2844 3404 4675
Spanish 3081 6754 7939
Swedish 2552 3035 3343
Ukrainian 309 290 571
Vietnamese 8164 108 8244
Total 50478 100212 126151
4.2 Resource Description
We ran our methods using the dumps of 15 Wikipedias obtained from April
2020.7 While the Wikidata graph dump was obtained from February 2020.8
The final resource contains 126,151 sitelinks for 109,734 unique entities
for 15 languages obtained with the two methods described above. The subset
of languages we initially considered were chosen according to the following
criteria:
1. Number of articles in the corresponding Wikipedia (over 1 million)9
2. Number of Wikipedia active users (over 1000)
We plan to run algorithms on more languages in the future. We report
below the full list of languages we considered as well as detailed statistics
on the datasets (see Table 1).
4.3 Evaluation Results
To estimate the precision of our resource, we randomly sampled several
hundreds matches from each dataset and manually evaluated them as either
true or false. For instance, one algorithm matched the entity Q49001814
7
https://dumps.wikimedia.org/
8
https://dumps.wikimedia.org/wikidatawiki/
9
https://meta.wikimedia.org/wiki/List_of_Wikipedias
SectionLinks: Mapping Orphan Wikidata Entities onto Wikipedia Sections 11
Table 2. Datasets precision
All-to-all Neighbors Final
matching matching
Arabic 0.99 1.0 0.99
English 0.82 0.81 0.82
French 0.85 0.92 0.89
Russian 0.88 0.85 0.87
(Timber Dam, the name of the dam in Montana, USA) to the section Timber
dams of the Wikipedia page Dam, which describes a type of dams made of
timber. This match was labelled as a false positive. An example of a true
positive match is a mapping of the entity Q334415 (security camera) onto
the page Surveillance, section Cameras. We labelled each sample this way
and then divided the number of true positive matches by the sample size to
get a precision value. We then generalized from the sample observations to
the whole dataset using linear extrapolation in order to estimate the dataset
precision. Table 2 reports our results.
We evaluated 12 samples – one sample per algorithm plus the joint re-
sults, for 4 different languages (Arabic, English, French, Russian). Each sam-
ple contains around 200 mappings. This number was chosen empirically, as
we observed that 200 random examples were enough to stabilize the metric,
and increasing the sample size did not change the resulting value signifi-
cantly. Overall we manually labelled 2400 mappings.
Our evaluation aims to demonstrate that the overall accuracy of the re-
source is high enough that it can be used for many tasks that do not require
a perfect dataset (for example, most deep learning algorithms are robust
to errors in the training set). We cannot provide evaluation results for all
languages unfortunately, as we decided to focus on those languages we felt
comfortable with only.
5 Availability and Reusability
Our resource is available in JSON and RDF formats and comply with the
Wikibase data model.10 To keep the resource compact and as easy to process
as possible, we only publish the sitelinks discover using our methods.
In the JSON representation, an entity contains two fields: id (the unique
identifier of an entity) and sitelinks (links to Wikipedia pages). Each sitelink
record comprises three fields: site, title and url. A section title is appended
to the page title separated with # symbol. Such a compound title is then
URL-encoded and added to the URL path. Following the Wikidata guidelines,
each entity is encoded as a single line.
10
https://www.mediawiki.org/wiki/Wikibase/DataModel
12 N. Ostapuk et al.
The RDF dump is serialized using the Turtle format and stores nodes
describing Wikipedia links. Section titles are added in the same manner as
described above . 11
The resource is published on the Zenodo platform under CC BY 4.0 li-
cense.12 . The canonical citation is available on the Zenodo page. The source
code is also available on our github repository to help maintain and generate
newer releases in the future.13 .
6 Conclusion and Future Work
We presented a dataset that extends Wikidata orphan entities with Sitelinks
referencing Wikipedia sections for the 15 most prominent languages in Wiki-
pedia. To generate this resource, we employed string matching and graph
processing methods that leverage multilingual labels and the graph struc-
ture to find corresponding sections in Wikipedia. Since our methods use
heuristics, we compute the accuracy of a subset of the data using manual
judgment. This piece of information can be useful to inform downstream ap-
plication on how to use the data. For instance, for entities with an English
label, we identified 9,834 links with 82% accuracy when using exact label
matching, and 25,469 links when using graph-based method alone with 81%
accuracy.
We believe that using this resource can improve both sources in terms
of completeness and freshness, as well as diminish the information gap that
persists between Wikipedia-based entities and tail-entities. For example, one
could build targeted information extraction tools and automatically curate
entities that do not have a dedicated Wikipedia article using our resource.
As future work, we plan to incorporate embedding-based similarity scores
into our mapping method and perform a comprehensive evaluation of the ob-
tained results in terms of both precision and recall. We also envision building
a section recommendation system that can be offered to Wikidata editors for
relevance judgment.
Acknowledgements
This project has received funding from the European Research Council (ERC)
under the European Union’s Horizon 2020 research and innovation pro-
gramme (grant agreement 683253/GraphInt).
11
For the detailed description of the Wikidata RDF format refer to: https://www.
mediawiki.org/wiki/Wikibase/Indexing/RDF_Dump_Format
12
http://doi.org/10.5281/zenodo.3840622
13
https://github.com/eXascaleInfolab/WikidataSectionLinks
SectionLinks: Mapping Orphan Wikidata Entities onto Wikipedia Sections 13
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