RDF Priming is a technique that selects a subset of available statements for use as the input to query answering. It is based upon the concept of 'spreading activation' as developed in cognitive science. RDF Priming is a scalable and customisable implementation of the popular connectionist method on top of RDF graphs that allows for the "priming" of large datasets with respect to concepts relevant to the context and to the query. It is controlled using SPARQL ASK queries. This section provides an overview of the mechanism and explains the necessary SPARQL queries used to manage and set up RDF Priming.
To enable RDF Priming over the repository, the repository-type configuration parameter should be set to weighted-file-repository.
RDF Priming is controlled using SPARQL ASK queries, which allows all the parameters and default values to be set. These queries use special system predicates, which are described below:
The following example uses data from DBPEDIA http://dbpedia.org/About and was imported into OWLIM-SE with the RDF Priming mode enabled. The management queries are evaluated through the Sesame console application for convenience. The initial step is to evaluate a demo query that retrieves all the instances of the dbpedia:V8 concept:
The above query returns the following results:
As can be seen, the query returns many engines from different manufacturers. The RDF Priming module can be used to reduce the number of results returned by this query by targeting the query to specific parts of the global RDF graph, i.e. the parts of the graph that have been activated.
Change the default decay factor:
Change the firing threshold parameter:
Change the filter threshold:
The initial Activation Level is changed to reflect the specifics of the data set:
Adjust the Weight factors for a specific predicate so that it activates the relevant sub-set of the RDF graph, in this case the rdfs:subClassOf predicate:
The next step alters the Weight Factor of the rdf:type predicate so that it does not propagate activations to the classes from the activated instances. This is a useful technique when there are a lot of instances and a very large classification taxonomy which should not be broadly activated (as is the case with the DBpedia dataset).
If the example query is executed at this stage, it will return no results, because the RDF graph has no activated nodes at all. Therefore the next step is to activate two particular nodes, the Ford Motor Company dbpedia3:Ford_Motor_Company and one of the cars they build dbpedia3:1955_Ford, which came out of the factory with a very nice V8 engine:
Finally, tell the RDF Priming module to spread the activations from these two nodes:
This will normally take 8-10 seconds after which the example query can be re-evaluated with the following results:
As can be seen, the result set is smaller and most of the engines retrieved are made by Ford. However, there is an engine made by Jaguar which is most probably there because Ford owned Jaguar for some time in the past, so both manufacturers are somehow related to each other. This might also be the case for the other non-Ford engines returned, since BMW also owned Jaguar for some time. Of course, these remarks are a free interpretation of the results.
to return to the normal operating mode.
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