Yes, you may have noticed an abundance (cornucopia?) of OWL2 RL-related posts on this blog. I’ve done some work in this field, and thought it best to split up the work into more manageable chunks.
I’ve found that when working with OWL2 RL,
- A number of important options for optimization exist;
- Certain rules involving n-ary lists can be supported in multiple ways.
Given these considerations, wouldn’t it be cool if someone can easily create their own OWL2 RL ruleset, geared towards their particular application scenario (availability of a “stable” ontology; need for full OWL2 RL conformance; need for n-ary rules)?
Table of Contents
Regarding optimization, I’ve proposed 3 selections to create OWL2 RL subsets. You can find a summary below.
In , I elaborate on these selections (and their impact on performance) in much more detail.
Equivalent OWL2 RL rule subset
Leaves out logically equivalent rules; replaces a set of specific rules by a single general rule; and may drop rules based on redundancy at the instance level.
Note that this subset focuses in particular on reducing the OWL2 RL ruleset, not necessarily optimizing it for reasoning (e.g., using more general rules has been shown to reduce performance, as shown in ).
Purpose and reference-based subsets
Divides rule subsets via their purpose and referenced data, allowing smaller rulesets to be applied in certain runtime scenarios.
OWL2 RL rules perform either inference or consistency-checking (purpose), and refer to instances and schema or only schema elements (reference). Further, rules that will not yield inferences over a particular ontology can be left out as well, by applying a separate pre-processing step (domain-specific).
Importantly, the applicability of this selection depends on the concrete use case, and whether the ontology can be considered relatively stable (i.e., not prone to change).
First, one can create two separate OWL2 RL rulesets, respectively containing rules that (a) refer to instances & schema assertions, and (b) only refer to schema assertions. While ruleset (a) is relevant whenever new instances are added, ruleset (b) is only relevant at initialization time and whenever the ontology changes. A (rather trivial) proof in  confirms that this process yields the same inferences as the full OWL2 RL ruleset.
Second, one can create a domain-specific ruleset, leaving out rules that will not yield inferences over a given ontology. Although this selection may yield huge performance improvements, note that it is especially brittle in dynamic settings — aside from ontology updates, even new data patterns may render a rule relevant (e.g., reciprocal subclass-of statements). In case of ontology updates, or new data patterns, the domain-specific ruleset will need to be generated again.
Removal of inefficient rules
This selection leaves out rules with a large performance impact.
Currently, it only leaves out the #eq-ref rule, which infers that each resource is equivalent to itself. This rule generates 3 new triples for each triple with unique resources, resulting in a worst-case 4x increase in dataset size (!).
So-called n-ary rules refer to a finite list of elements. A first subset (L1) of these rules enumerate (i.e., list one by one) restrictions on single list elements. For instance, rule #eq-diff2 flags an ontology inconsistency if two equivalent elements of an owl:AllDifferent construct are found. Rules from the second subset (L2) include restrictions referring to all list elements, and a third ruleset (L3) yields inferences for all list elements . E.g., for (L2), rule #cls-int1 infers that y is an instance of an intersection in case it is typed by each intersection member class; regarding (L3), for any union, rule #scm-uni infers that each member class is a subclass of that union.
To support rulesets (L1) and (L3), two list-membership rules can be added that recursively link each element to preceding list cells, eventually linking the first cell to all list elements. Three possible solutions can be applied for (L3), each with their own advantages and drawbacks. These solutions are summarized here and elaborated in more detail in .
I developed a web service that allows anyone to create their own custom OWL2 RL ruleset, by applying one or more optimizations and/or solutions for n-ary lists. It includes an initial OWL2 RL ruleset as a set of SPARQL Constructs (find it separately here). You can find the project on GitHub!
I previously posted about simple Java and Android projects that utilize an OWL2 RL ruleset for ontology reasoning. To convert the OWL2 RL ruleset into different formats (such as the Apache Jena one), there’s a project for that too.
Just plugin your own custom OWL2 RL ruleset, and feel free to share your results/thoughts in the comments!
 W. Van Woensel, S. S. R. Abidi, Benchmarking Semantic Reasoning on Mobile Platforms: Towards Optimization Using OWL2 RL, in: Semantic Web Journal, Forthcoming (2018).