On this research project page we present the results of our research about Human Associations in the Semantic Web.
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.
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.
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.
The source-code for our algorithm can be found on GitHub.
In the context of our work the following papers have been published:
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.