Associations & Linked Data


A research project to connect Human Associations and the Semantic Web

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What's this all about?


On this research project page we present the results of our research about Human Associations in the Semantic Web.

Human Associations

Associations are one of the building blocks of human intelligence, thinking, context forming and everyday communication. They have been the focus of psychological research for a long time and there are excellent datasets such as the Edinburgh Associative Thesaurus (EAT).

Semantic Web

The Semantic Web is a vision of a World Wide Web for interlinked data, not only web pages for humans. Following this vision, the Linked Data movement has brought us many cool interlinked datasets. A prominent example of such a dataset is DBpedia, which is extracted from Wikipedia.

Datasets

With the help of an Association Vocabulary we were able to transform EAT into RDF. We already mapped the strongest associations to DBpedia entities. You can interact with the resulting semantic associations between DBpedia entities above or access the generated datasets below.

Graph Patterns

We also extracted graph patterns for these semantic associations from DBpedia. We developed and applied an evolutionary graph pattern learner which is able to learn SPARQL patterns for a given list of source-target-pairs. You can find a visualisation of its results below.

Association Vocabulary


The generated association vocabulary ( RDF | OWL | TTL ) has its own doc page.

It mainly consists of the classes Term, for a stimulus or response, and Association which connects them to their count and frequency. It also defines classes Mapping and VerifiedMapping to map associations from one dataset to another with the mapped to property.

An example for all this can be found in the following drawing. In the middle you can see the stimulus "pupil", which can lead to the responses "eye" (left) and "school" (right). Both associations are mapped to a corresponding association node which also contains information about ther counts and frequencies. The one on the left further was mapped to a semantic association between the two DBpedia entities dbr:Pupil and dbr:Eye. As you can see in this process the ambiguous term "pupil" was disambiguated to the right semantic entity dbr:Pupil.

See our papers in the publications section for more details.

Datasets


Further information in the publications section.

EAT as RDF

As the transformation of all EAT associations into RDF creates nearly 1.7 M triples, we only provide, at the moment, a dump of this dataset.

(eat.nt.gz (gzipped N-Triples))

Mapping EAT to DBpedia

The 790 manually verified mappings of strong EAT associations to semantic associations between DBpedia entities are provided as dereferenceable RDF, as TSV and dump file as well.

Graph Pattern Learner


Evolutionary Algorithm

In order to find graph patterns for associations in DBpedia, we developed an evolutionary graph pattern learner (see paper). It takes an input list of source-target-pairs (such as the stimulus-response-pairs of the semantic associations above) and learns SPARQL queries for them from a given SPARQL endpoint.

The source-code for our algorithm can be found on GitHub.

Resulting Patterns

We ran our pattern learner against a local Linked Data endpoint filled with 7.9 G triples from the heart of the LOD-Cloud.

The resulting learned graph patterns are available in an interactive visualisation:

Predicting Associations

Given a source node, the learned SPARQL patterns can be used to predict a list of target nodes and by this simulate human associating based on DBpedia.

Try it out

Publications


In the context of our work the following papers have been published:

EAT as RDF and DBpedia Mapping

J. Hees, R. Bauer, J. Folz, D. Borth & A. Dengel -

Edinburgh Associative Thesaurus as RDF and DBpedia Mapping.

Proceedings of ESWC 2016 SE, Herarklion, Crete.

LNCS, Springer.

( PDF: Local | ESWC | Springer | Poster | Extended Paper )

Evolutionary Graph Pattern Learner

J. Hees, R. Bauer, J. Folz, D. Borth & A. Dengel

An Evolutionary Algorithm to Learn SPARQL Queries for Source-Target-Pairs - Finding Patterns for Human Associations in DBpedia.

Proceedings of EKAW 2016, Bologna, Italy.

LNCS, Springer.

( PDF: Local | Springer | arXiv | Slides )

Talks


  • Human Associations in the Semantic Web and DBpedia at 7th DBpedia Community Meeting in Leipzig co-located with Semantics 2016 (PDF)
  • An Evolutionary Algorithm to Learn SPARQL Queries for Source-Target-Pairs - Finding Patterns for Human Associations in DBpedia at EKAW 2016 in Bologna (PDF)

Get In Touch!


Have a question, some idea, feedback or want to collaborate?

Just drop us a line, we don't bite ;)

Jörn Hees
(main contact)

DFKI Knowledge Management
(our research group)