DEER: RDF Data Extraction and Enrichment Framework

Over the last years, the Linked Data principles have been used across academia and industry to publish and consume structured data. Thanks to the fourth Linked Data principle, many of the RDF datasets used within these applications contain implicit and explicit references to more data. For example, music datasets such as Jamendo include references to locations of record labels, places where artists were born or have been, etc. Datasets such as Drugbank contain references to drugs from DBpedia, were verbal description of the drugs and their usage is explicitly available. The goal of mapping component, dubbed DEER, is to retrieve this information, make it explicit and integrate it into data sources according to the specifications of the user. To this end, DEER relies on a simple yet powerful pipeline system that consists of two main components: enrichment functions and operators.

Download Issues

Enrichment functions and operators.

Enrichment functions implement functionality for processing the content of a dataset (e.g., applying named entity recognition to a particular property). Thus, they take a dataset as input and return a dataset as output. Enrichment operators work at a higher level of granularity and combine datasets. Thus, they take sets of datasets as input and return sets of datasets.

RDF specification paradigm

In the current version of DEER we introduce our new RDF based specification paradigm. The main idea behind this new paradigm is to enable the processing execution of specifications in an efficient way. To this end, we first decided to use RDF as language for the specification. This has the main advantage of allowing for creating specification repositories which can be queried easily with the aim of retrieving accurate specifications for the use cases at hand. Moreover, extensions of the specification language do not require a change of the specification language due to the intrinsic extensibility of ontologies. The third reason for choosing RDF as language for specifications is that we can easily check the specification for correctness by using a reasoner, as the specification ontology allows for specifying the restrictions that specifications must abide by.

Publications

by (Editors: ) [BibTex of ]

News

Jekyll RDF Tutorial Screencast ( 2018-08-07T11:11:12+02:00 by Natanael Arndt)

2018-08-07T11:11:12+02:00 by Natanael Arndt

Since 2016 we are developing Jekyll-RDF a plugin for the famous Jekyll–static website generator. Read more about "Jekyll RDF Tutorial Screencast"

DBpedia Day @ SEMANTiCS 2018 ( 2018-07-20T14:37:25+02:00 by Johannes Frey)

2018-07-20T14:37:25+02:00 by Johannes Frey

Don’t miss the 12th edition of the DBpedia Community Meeting in Vienna, the city with the highest quality of life in the world. Read more about "DBpedia Day @ SEMANTiCS 2018"

SANSA 0.4 (Semantic Analytics Stack) Released ( 2018-06-26T18:33:38+02:00 by Prof. Dr. Jens Lehmann)

2018-06-26T18:33:38+02:00 by Prof. Dr. Jens Lehmann

We are happy to announce SANSA 0.4 – the fourth release of the Scalable Semantic Analytics Stack. Read more about "SANSA 0.4 (Semantic Analytics Stack) Released"

AKSW is organizing the 6th Leipzig Semantic Web Day (LSWT2018) ( 2018-04-17T14:14:17+02:00 by Natanael Arndt)

2018-04-17T14:14:17+02:00 by Natanael Arndt

On June 18th 2018 we will have the 6th Leipzig Semantic Web Day (LSWT2018). A platform for regional actors to get in touch with each other regarding Semantic Web topics. Read more about "AKSW is organizing the 6th Leipzig Semantic Web Day (LSWT2018)"

SANSA 0.3 (Semantic Analytics Stack) Released ( 2017-12-18T11:15:38+01:00 by Simon Bin)

2017-12-18T11:15:38+01:00 by Simon Bin

Dear all, We are happy to announce SANSA 0.3 – the third release of the Scalable Semantic Analytics Stack. Read more about "SANSA 0.3 (Semantic Analytics Stack) Released"