=Paper=
{{Paper
|id=None
|storemode=property
|title=Multilingual Disambiguation of Named Entities Using Linked Data
|pdfUrl=https://ceur-ws.org/Vol-1272/paper_79.pdf
|volume=Vol-1272
|dblpUrl=https://dblp.org/rec/conf/semweb/UsbeckNLW14
}}
==Multilingual Disambiguation of Named Entities Using Linked Data==
https://ceur-ws.org/Vol-1272/paper_79.pdf
Multilingual Disambiguation of Named Entities
Using Linked Data
Ricardo Usbeck♠♦ , Axel-Cyrille Ngonga Ngomo♦ , Wencan Luo♥ , and Lars
Wesemann♠
♦ University of Leipzig, Germany ,
♠ R & D, Unister GmbH, Leipzig, Germany,
♥ University of Pittsburgh, United States of America
email: {usbeck|ngonga}@informatik.uni-leipzig.de
Abstract. One key step towards extracting structured data from un-
structured data sources is the disambiguation of entities. With AGDIS-
TIS, we provide a time-efficient, state-of-the-art, knowledge-base-agnostic
and multilingual framework for the disambiguation of RDF resources.
The aim of this demo is to present the English, German and Chinese
version of our framework based on DBpedia. We show the results of
the framework on texts pertaining to manifold domains including news,
sports, automobiles and e-commerce. We also summarize the results of
the evaluation of AGDISTIS on several languages.
1 Introduction
A significant portion of the information on the Web is still only available in
textual format. Addressing this information gap between the Document Web
and the Data Web requires amongst others the extraction of entities and rela-
tions between these entities from text. One key step during this processing is
the disambiguation of entities (also known as entity linking). The AGDISTIS
framework [7] (which will also be presented at this conference) addresses two
of the major drawbacks of current entity linking frameworks [1,2,3]: time com-
plexity and accuracy. With AGDISTIS, we have developed a framework that
achieves polynomial time complexity and outperforms the state of the art w.r.t.
accuracy. The framework is knowledge-base-agnostic (i.e., it can be deployed on
any knowledge base) and is also language-independent. In this demo, we will
present AGDISTIS deployed on three different languages (English, German and
Chinese) and three different knowledge bases (DBpedia, the German DBpedia
and the Chinese DBpedia). To the best of our knowledge, we therewith provide
the first Chinese instantiation of entity linking to DBpedia. We will also demon-
strate the AGDISTIS web services endpoints for German, English and Chinese
disambiguation and show how data can be sent to the endpoints. Moreover, the
output format of AGDISTIS will be explained. An online version of the demo is
available at http://agdistis.aksw.org/demo.
2 Demonstration
Within our demonstration, we aim to show how AGDISTIS can be used by
non-expert as well as expert users. For non-experts, we provide a graphical user
interface (GUI). Experts can choose to use the REST interfaces provided by
the tool or use a Java snippet to call the REST interface. The whole of this
functionality, which will be described in more details in the following sections,
will also be demonstrated at the conference.
2.1 AGDISTIS for non-expert users
A screenshot of the AGDISTIS GUI is shown in Figure 1. This GUI supports
the following workflow.
Fig. 1: Screenshot of the demo with an English example which is already anno-
tated.
Entity Recognition After typing or pasting text into the input field, users can
choose between either annotating the entities manually or having the entities
detected automatically. In the first case, the labels of the entities are to be
marked by using square brackets (see central panel of Figure 1). In the case of
an automatic annotation, we send the text to the FOX framework, which has
been shown to outperform the state of the art in [6]. We will demonstrate this
feature by using both manually pre-annotated text and text without annotations
in our examples (see upper bar of Figure 1). Moreover, we will allow the crowd
to enter arbitrary texts that pertain to their domain of interest.
Automatic Language Detection Once the user has set which entities are to
be disambiguated, the marked-up text is sent to the language detection module
based on [5]. We chose this library because it is both precise (> 99% precision)
and time-efficient. If the input is detected to belong to one of the languages
we support (i.e., German, Chinese, English), then we forward the input to a
dedicated AGDISTIS instance for this given language. In all other cases, an error
message is shown to the user, pointing towards the language at hand not being
supported. The main advantage of this approach is that the user does not need
to select the language in which the text is explicated manually, thus leading to
an improved user experience. We will demonstrate this feature by entering text
in different languages (German, English, French, Chinese, etc.) and presenting
the output of the framework for each of these test cases.
Entity Linking This is the most important step of the whole workflow. The
annotated text is forwarded to the corresponding language-specific deployment
of AGDISTIS, of which each relies on a language-specific version of DBpedia 3.9.
The approach underlying AGDISTIS [7] is language-independent and combines
breadth-first search and the well-known HITS algorithm. In addition, string
similarity measures and label expansion heuristics are used to account for typos
and morphological variations in naming. Moreover, Wikipedia-specific surface
forms for resources can be used.
Output Within the demo the annotated text is shown below the input field
where disambiguated entities are colored to highlight them. While hovering a
highlighted entity the disambiguated URI is shown. We will demonstrate the
output of the entity linking by using the examples shown in the upper part of
Figure 1. The output of the system will be shown both in a HTML version
and made available as a download in JSON. Moreover, we will allow interested
participants to enter their own examples and view the output of the tool.
2.2 AGDISTIS for expert users
To support different languages we set up a REST URI for each of the language
versions. Each of these endpoints understands two mandatory parameters: (1)
text which is an UTF-8 and URL encoded string with entities annotated with
XML-tag and (2) type=’agdistis’ to disambiguate with the AGDIS-
TIS algorithm. In the future, several wrappers will be implemented to use differ-
ent entity linking algorithms for comparison. Following, a CURL1 snippet shows
how to address the web service, see also http://agdistis.aksw.org:
curl --data-urlencode "text=’Barack Obama arrives
in Washington, D.C..’" -d type=’agdistis’
{AGDISTIS URL}/AGDISTIS
1
http://curl.haxx.se/
3 Evaluation
English and German Evaluation. AGDISTIS has been evaluated on 8 dif-
ferent datasets from diverse domains such as news, sports or buisiness reports.
For English datasets AGDISTIS is able to outperform the currently best dis-
ambiguation framework, TagMe2, on three out of four datasets by up to 29.5%
F-measure. Considering the only German dataset available for named entity dis-
ambiguation, i.e., news.de [4], we are able to outperform the only competitor
DBpedia Spotlight by 3% F-measure.
Chinese Evaluation. We evaluated the Chinese version of AGDISTIS within
a question answering setting. To this end, we used the multilingual benchmark
provided in QALD-42 . Since the Chinese language is not supported, we extended
the QALD-4 benchmark by translating the English questions to Chinese and
inserted the named entity links manually. The accuracies achieved by AGDISTIS
for the train and test datasets are 65% and 70% respectively.
4 Conclusion
We presented the demo of AGDISTIS for three different languages on three
different DBpedia-based knowledge bases. In future work, we aim to create a
single-server multilingual version of the framework that will intrinsically support
several languages at the same time. To this end, we will use a graph merging
algorithm to combine the different versions of DBpedia to a single graph. The
disambiguation steps will then be carried out on this unique graph.
Acknowledgments This work has been supported by the
ESF and the Free State of Saxony and the FP7 project Geo-
Know (GA No. 318159).
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http://greententacle.techfak.uni-bielefeld.de/~cunger/qald