Backward inference and pruning for RDF change detection using RDBMS

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)

2015

Many real world RDF collections are large compared with other real world data structures. Such large RDF collections evolve in a distributed environment. Therefore, these changes between RDF versions need to be detected and computed in order to synchronize these changes to the other users. To cope with the evolving nature of the semantic web, it is important to understand the costs and benefits of the different change detection techniques. In this paper, we experimentally provide a detailed analysis of the overall process of RDF change detection techniques namely: explicit change detection, forward-inference change detection, backward-inference change detection and backward-inference and pruning change detection. The results show that pruning is relatively expensive by comparison with inferencing

Section: Introductionmentioning

confidence: 99%

“…The entailment rules infer new RDF statements based on the presence of other statements. Only the rules that play a crucial roles in minimizing the delta size are used [4].…”

Section: Introductionmentioning

confidence: 99%

The cost of reasoning with RDF updates

Azwari

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)

2015

“…insertions or deletions). In addition to the differential functions explained in Section 3, two pruning-based functions as proposed in [7] are also employed. These functions combine the differential functions in [15] with pruning methods to reduce unnecessary computation during the reasoning process.…”

Section: Resultsmentioning

confidence: 99%

Updating OWL2 ontologies using pruned rulesets

Al-Azwari

Proceedings of the 11th International Conference on Semantic Systems

2015

Evolution in Semantic Web content produces difference files (deltas) that track changes between RDF versions. These changes may represent ontology modifications and be expressed in OWL. The deltas can be used to reduce the storage and bandwidth overhead involved in disseminating ontology updates. Minimising the delta size can be achieved by reasoning over the underlying knowledge base. OWL 2 is a development of the OWL 1 standard that incorporates new features to aid application development. Among the sub languages of OWL 2, OWL 2 RL/RDF provides an enriched rule set that extends the semantic capability of the OWL environment. This additional semantic content can be exploited in change detection approaches that strive to minimise the alterations to be made when ontologies are updated. The presence of blank nodes (i.e. nodes that are neither a URI nor a literal) in RDF collections provides a further challenge to ontology change detection because of the practical problems they introduce when comparing data structures before and after update. In the light of OWL 2 RL/RDF, this paper examines the potential for reducing the delta size by pruning the application of unnecessary rules from the reasoning process and using an approach to delta generation that produces the smallest number of updates. It also assesses the impact of alternative approaches to handling blank nodes during the change detection process in ontology structures. The results indicate that pruning the rule set is a potentially expensive process but has the benefit of reducing the joins when carrying out the subsequent inferencing

“…However, although the backward inference method is applied to infer only relevant triples, applying the inference on some of these triples might be unnecessary allowing pruning to be applied before backward inference [4]. The general rule for pruning is that if the subject or object of a triple in this triple cannot be inferred, consequently the triple can be pruned before the inference process begins.…”

Section: Checking the Dense Deltamentioning

confidence: 99%

“…In any particular data collection, changes in the domain that are reflected by evolution of the ontology may require changes in the underlying RDF data. Due to the dynamic and evolving nature of typical Semantic Web structures, RDF data may change on a regular basis, producing successive versions that are available for publication and distribution [4]. In the context of such dynamic RDF data collections, which may be very large structures, it quickly becomes infeasible to store a historic sequence of updates in any accessible form as a consequence of the significant storage space needed.…”

Section: Introductionmentioning

confidence: 99%

Consistent RDF Updates with Correct Dense Deltas

Azwari

2015

Data Science

Abstract. RDF is widely used in the Semantic Web for representing ontology data. Many real world RDF collections are large and contain complex graph relationships that represent knowledge in a particular domain. Such large RDF collections evolve in consequence of their representation of the changing world. Although this data may be distributed over the Internet, it needs to be managed and updated in the face of such evolutionary changes. In view of the size of typical collections, it is important to derive efficient ways of propagating updates to distributed data stores. The contribution of this paper is a detailed analysis of the performance of RDF change detection techniques. In addition the work describes a new approach to maintaining the consistency of RDF by using knowledge embedded in the structure to generate efficient update transactions. The evaluation of this approach indicates that it reduces the overall update size at the cost of increasing the processing time needed to generate the transactions. . . .