REX: Web-Scale Extension of RDF Knowledge Bases

REX is an RDF extraction framework for Web data that can learn XPath wrappers from unlabelled Web pages using knowledge from the Linked Open Data Cloud.

API Documentation Issues Source Code Wiki

Introduction

The Web RDF Extraction Framework, REX, addresses the problem of extracting RDF data from templated websites. To this end, REX provide a generic architecture that allows learning XPath wrappers from unlabelled Web pages using knowledge from the Linked Open Data Cloud. REX is to be regarded as a skeleton that is to be fleshed out for your purposes. Still, REX is also a running system as it provides running implementations for all of its interfaces.

In contrast to existing frameworks to RDF extraction using XPath wrappers, REX provides a consistency layer which ensure that the new knowledge extracted is logically consistent with the knowledge already available in the input knowledge base. This website gives an overview of the framework. All technical details can be found on the Github page's wiki. There you will also find:

  • The Java documentation for the coders out there.
  • A manual to help you run the framework before you customize it for your purposes.
  • A ticket system in case you find some bugs or have some feature request.

Architecture

The REX Architecture

To facilitate the implementation of extraction processes, the framework provides the four layer-architecture shown in Figure 1. The data for the extraction is first to be gathered from the Web (or any other source of your choice). To this end, interfaces are provided. Each of the modules in each of the layers is provided as an interface. Moreover, an initial implementation of each interface is provided (see Java Docs).

  • The extraction layer allows for gathering data from the Web and consists of two modules: The crawler gathers website content from the Web while the domain identifier helps detecting web site domains that contain information pertaining to a given property.
  • The storage layer provides interfaces for managing and storing structured data as well as unstructured data.
  • The induction layer contains all modules that allow to learn XPath expressions. The core module here is the XPath Learner.
  • The generation layer allows integration approaches for generating and validating RDF data. The default generator relies on AGDISTIS and ORE.

Evaluation

With REX, we also aimed to provide a baseline system for the extraction of RDF from templated websites. Thus, in addition to providing at least one implementation for all the interfaces, we also evaluated the basic REX. The data we used for the evaluation can be found here.

What next?

There are several things you can do.

  1. Run REX: Simply follow the steps in the manual.
  2. Extend REX: Please check out the installation instructured.
  3. Point out bugs: Please use the issue tracker.

Now you're on. Please extend REX and help improving the extraction of RDF from the Web.

Project Team

Former Members

Publications

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News

SANSA 0.7.1 (Semantic Analytics Stack) Released ( 2020-01-17T09:52:41+01:00 by Prof. Dr. Jens Lehmann)

2020-01-17T09:52:41+01:00 by Prof. Dr. Jens Lehmann

We are happy to announce SANSA 0.7.1 – the seventh release of the Scalable Semantic Analytics Stack. SANSA employs distributed computing via Apache Spark and Flink in order to allow scalable machine learning, inference and querying capabilities for large knowledge graphs. Read more about "SANSA 0.7.1 (Semantic Analytics Stack) Released"

More Complete Resultset Retrieval from Large Heterogeneous RDF Sources ( 2019-12-05T15:46:09+01:00 Andre Valdestilhas)

2019-12-05T15:46:09+01:00 Andre Valdestilhas

Over recent years, the Web of Data has grown significantly. Various interfaces such as LOD Stats, LOD Laundromat and SPARQL endpoints provide access to hundreds of thousands of RDF datasets, representing billions of facts. Read more about "More Complete Resultset Retrieval from Large Heterogeneous RDF Sources"

DL-Learner 1.4 (Supervised Structured Machine Learning Framework) Released ( 2019-09-24T22:41:46+02:00 by Simon Bin)

2019-09-24T22:41:46+02:00 by Simon Bin

Dear all, The Smart Data Analytics group [1] and the E.T.-db-MOLE sub-group located at the InfAI Leipzig [2] is happy to announce DL-Learner 1.4. DL-Learner is a framework containing algorithms for supervised machine learning in RDF and OWL. Read more about "DL-Learner 1.4 (Supervised Structured Machine Learning Framework) Released"

DBpedia Day @ SEMANTiCS 2019 ( 2019-08-01T10:35:05+02:00 Sandra Bartsch)

2019-08-01T10:35:05+02:00 Sandra Bartsch

 We are happy to announce that SEMANTiCS 2019 will host the 14th DBpedia Community Meeting at the last day of the conference on September 12, 2019. Read more about "DBpedia Day @ SEMANTiCS 2019"

LDK conference @ University of Leipzig ( 2019-03-22T09:21:41+01:00 by Julia Holze)

2019-03-22T09:21:41+01:00 by Julia Holze

With the advent of digital technologies, an ever-increasing amount of language data is now available across various application areas and industry sectors, thus making language data more and more valuable. Read more about "LDK conference @ University of Leipzig"