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 Fmeasure than state-of-the-art named entity recognition frameworks by combining the results of several approaches through ensemble learning. Moreover, it disambiguates 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.
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 addition, we will show how to configure and train FOX after it has been enhanced with a
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 [
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.
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.
Entity Recognition Our approach relies on four state-of-the-art NER tools so far: (1)
the Stanford Named Entity Recognizer (Stanford) [
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
We performed a thorough evaluation of FOX by using five different datasets and
comparing 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 [
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.