=Paper=
{{Paper
|id=None
|storemode=property
|title=Named Entity Recognition using FOX
|pdfUrl=https://ceur-ws.org/Vol-1272/paper_70.pdf
|volume=Vol-1272
|dblpUrl=https://dblp.org/rec/conf/semweb/SpeckN14a
}}
==Named Entity Recognition using FOX==
https://ceur-ws.org/Vol-1272/paper_70.pdf
Named Entity Recognition using FOX
René Speck and Axel-Cyrille Ngonga Ngomo
AKSW, Department of Computer Science, University of Leipzig, Germany
{speck,ngonga}@informatik.uni-leipzig.de
Abstract. Unstructured data still makes up an important portion of the Web.
One key task towards transforming this unstructured data into structured data is
named entity recognition. We demo FOX, the Federated knOwledge eXtraction
framework, a highly accurate open-source framework that implements RESTful
web services for named entity recognition. Our framework achieves a higher F-
measure than state-of-the-art named entity recognition frameworks by combining
the results of several approaches through ensemble learning. Moreover, it disam-
biguates and links named entities against DBpedia by relying on the AGDISTIS
framework. As a result, FOX provides users with accurately disambiguated and
linked named entities in several RDF serialization formats. We demonstrate the
different interfaces implemented by FOX within use cases pertaining to extracting
entities from news texts.
1 Introduction
The Semantic Web vision requires the data on the Web to be represented in a machine-
readable format. Given that a significant percentage of the data available on the Web
is unstructured, tools for transforming text into RDF are of central importance. In this
demo paper, we present FOX, the federated knowledge extraction framework.1 It inte-
grates state-of-the-art named entity recognition (NER) frameworks by using ensemble
learning (EL). By these means, FOX can achieve up to 95.23% F-measure where the
best of the current state-of-the-art system (Stanford NER) achieves 91.68% F-measure.
In this paper, we aim to demonstrate several of the features of FOX, including the large
number of input and output formats it supports, different bindings with which FOX
can be integrated into Java and Python code and the easy extension model underly-
ing the framework. Our framework is already being used in several systems, including
SCMS [5], ConTEXT [3] and IR frameworks [8]. The approach underlying FOX is
presented in [7], which will be presented at the same conference. All features presented
herein will be part of the demonstration.
2 Demonstration
The goal of the demonstration will be to show the whole of the FOX workflow from
the gathering and preprocessing of input data to the generation of RDF data. In addi-
tion, we will show how to configure and train FOX after it has been enhanced with a
1
FOX online demo:http://fox-demo.aksw.org
FOX project page:http://fox.aksw.org.
Source code, evaluation data and evaluation results:http://github.com/AKSW/FOX.
novel NER tool or EL algorithm. Further, we will present FOX’s feedback RESTful
service to improve the training and test datasets. In the demonstration, we also go over
the Python2 and Java bindings3 for an easy use of FOX’s RESTful service within an
application. At the end we will explain how to use the FOX Java interfaces to integrate
future algorithms.
2.1 Workflow
The workflow underlying FOX consists of four main steps: (1) preprocessing of the
unstructured input data, (2) recognizing the Named Entities (NE), (3) linking the NE to
resources using AGDISTIS [9] and (4) converting the results to an RDF serialization
format.
Preprocessing FOX allows users to use a URL, text with HTML tags or plain text as
input data (see the top left part of Figure 1). The input can be carried out in a form
(see the center of Figure 1) or via FOX’s web service. In case of a URL, FOX sends
a request to the given URL to receive the input data. Then, for all input formats, FOX
removes HTML tags and detects sentences and tokens.
Fig. 1. Request form of the FOX online demo.
We will use text examples, URLs and text with HTML tags to show how FOX
gathers or cleans them for the sake of entity recognition.
2
https://pypi.python.org/pypi/foxpy
3
https://github.com/renespeck/fox-java
Entity Recognition Our approach relies on four state-of-the-art NER tools so far: (1)
the Stanford Named Entity Recognizer (Stanford) [2], (2) the Illinois Named Entity
Tagger (Illinois) [6], (3) the Ottawa Baseline Information Extraction (Balie) [4] and
(4) the Apache OpenNLP Name Finder (OpenNLP) [1]. FOX allows using a particu-
lar NER approach which is integrated in it (see bottom right of Figure 1). To this end,
FOX light has to be set to the absolute path to the class of the tool to use. If FOX light
is off, then FOX utilizes these four NER tools in parallel and stores the received NEs
for further processing. It maps the entity types of each of the NER tools to the classes
Location, Organization and Person. Finally, the results of all tools are merged
by using FOX’s EL layer as discussed in [7]. We will show the named entities recog-
nized by FOX and contrast these with those recognized by the other tools. Moreover,
we will show the runtime log that FOX generates to point to FOX’s scalability.
Entity Linking FOX makes use of AGDISTIS [9], an open-source named entity dis-
ambiguation framework able to link entities against every linked data knowledge base,
to disambiguate entities and to link them against DBpedia. In contrast to lookup-based
approaches, our framework can also detect resources that are not in DBpedia. In this
case, these are assigned their own URIs. Moreover, FOX provides a Java interface and
a configuration file for easy integration of other entity linking tools. We will show the
messages that FOX generates and sends to AGDISTIS as well as the answers it receives
and serializes.
Serialization Formats FOX is designed to support a large number of use cases. To
this end, our framework can serialize its results into the following formats: JSON-LD4 ,
N-Triples5 , RDF/JSON6 , RDF/XML7 , Turtle8 , TriG9 , N-Quads10 . FOX allows the user
to choose between these formats (see bottom left part of Figure 1). We will show how
the out of FOX looks like in the different formats and point to how they can be parsed.
3 Evaluation and Results
We performed a thorough evaluation of FOX by using five different datasets and com-
paring it with state-of-the-art NER frameworks (see Table 1). Our evaluation shows that
FOX clearly outperforms the state of the art. The details of the complete evaluation are
presented in [7]. The evaluation code and datasets are also available at FOX’s Github
page, i.e., http://github.com/AKSW/FOX.
4
http://www.w3.org/TR/json-ld
5
http://www.w3.org/TR/n-triples/
6
http://www.w3.org/TR/rdf-json
7
http://www.w3.org/TR/REC-rdf-syntax
8
http://www.w3.org/TR/turtle
9
http://www.w3.org/TR/trig
10
http://www.w3.org/TR/n-quads
Table 1. Comparison of the F-measure of FOX with the included NER tools. Best results are
marked in bold font.
token-based entity-based
News News∗ Web Reuters All News News∗ Web Reuters All
FOX 92.73 95.23 68.81 87.55 90.99 90.70 93.09 63.36 81.98 90.28
Stanford 90.34 91.68 65.81 82.85 89.21 87.66 89.72 62.83 79.68 88.05
Illinois 80.20 84.95 64.44 85.35 79.54 76.71 83.34 54.25 83.74 76.25
OpenNLP 73.71 79.57 49.18 73.96 72.65 67.89 75.78 43.99 72.89 67.66
Balie 71.54 79.80 40.15 64.78 69.40 69.66 80.48 35.07 68.71 67.82
4 Conclusion
We will present FOX, a NER framework which relies on EL and demonstrate how it
can be used. In future work, we will extend the number of tools integrated in FOX.
Moreover, we will extend the tasks supported by the framework. In particular, we aim
to integrate tagging, keyword extraction as well as relation extraction in the near future.
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