I have a small ontology defined as shown in the following picture:
I created an individual for Dataset and one for Algorithm. I expected that the reasoner would infer the Algorithm individual as Linear_Least_Regression, but this didn't happen.
This is the definition of the Dataset individual. As it can be seen, the individual fulfils the requirements for a Dataset needed by the Linear_Least_Regression
Also, if I add the Linear_Least_Regression as a type for the Algorithm individual, the reasoner does not complain.
I also tried to get the Linear_Least_Regression as a result with a DL Query but this also didn't work.
Did I miss something when modelling my ontology or does the problem lie at the reasoner?
I tried following two reasoners: FaCT++ 1.6.5 and HermiT 1.3.8.413 and Protégé 5
Related
We are using GraphDB 8.4.0 as a triple store for a large data integration project. We want to make use of the reasoning capabilities, and are trying to decide between using HORST (pD*) and OWL2-RL.
However, the literature describing these is quite dense. I think I understand that OWL2-RL is more "powerful", but I am unsure how so. Can anyone provide some examples of the differences between the two reasoners? What kinds of statements are inferred by OWL2-RL, but not HORST (and vice versa)?
Brill, inside there GraphDB there is a single rule engine, which supports different reasoning profiles, depending on the rule-set which was selected. The predefined rule-sets are part of the distribution - look at the PIE files in folder configs/rules. One can also take one of the existing profiles and tailor it to her needs (e.g. remove a rule, which is not necessary).
The fundamental difference between OWL2 RL and what we call OWL-Horst (pD*) is that OWL2RL pushes the limits of which OWL constructs can be supported using this type of entailment rules. OWL Horst is limited to RDFS (subClassOf, subSpropertyOf, domain and range) plus what was popular in the past as OWL Lite: sameAs, equivalentClass, equivalentProperty, SymmetricProperty, TransitiveProperty, inverseOf, FunctionalProperty, InverseFunctionalProperty. There is also partial support for: intersectionOf, someValuesFrom, hasValue, allValuesFrom.
What OWL 2 RL adds on top is support for AsymmetricProperty, IrreflexiveProperty, propertyChainAxiom, AllDisjointProperties, hasKey, unionOf, complementOf, oneOf, differentFrom, AllDisjointClasses and all the property cardinality primitives. It also adds more complete support for intersectionOf, someValuesFrom, hasValue, allValuesFrom. Be aware that there are limitations to the inference supported by OWL 2 RL for some of these properties, e.g. what type of inferences should or should not be done for specific class expressions (OWL restrictions). If you chose OWL 2 RL, check Tables 5-8 in the spec, https://www.w3.org/TR/owl2-profiles/#OWL_2_RL. GraphDB's owl-2-rl data set is fully compliant with it. GraphDB is the only major triplestore with full OWL 2 RL compliance - see the this table (https://www.w3.org/2001/sw/wiki/OWL/Implementations) it appears with its former name OWLIM.
My suggestion would be to go with OWL Horst for a large dataset, as reasoning with OWL 2 RL could be much slower. It depends on your ontology and data patterns, but as a rule of thumb you can expect loading/updates to be 2 times slower with OWL 2 RL, even if you don't use extensively its "expensive" primitives (e.g. property chains). See the difference between loading speeds with RDFS+ and OWL 2 RL benchmarked here: http://graphdb.ontotext.com/documentation/standard/benchmark.html
Finally, I would recommend you to use the "optimized" versions of the pre-defined rule-sets. These versions exclude some RDFS reasoning rules, which are not useful for most of the applications, but add substantial reasoning overheads, e.g. the one that infers that the subject, the predicate and the object of a statement are instances of rdfs:Resource
Id: rdf1_rdfs4a_4b
x a y
-------------------------------
x <rdf:type> <rdfs:Resource>
a <rdf:type> <rdfs:Resource>
y <rdf:type> <rdfs:Resource>
If you want to stay 100% compliant with the W3C spec, you should stay with the non-optimized versions.
If you need further assistance, please, write to support#ontotext.com
In addition to what Atanas (our CEO) wrote and your excellent example, http://graphdb.ontotext.com/documentation/enterprise/rules-optimisations.html provides some ideas how to optimize your rulesets to make them faster.
Two of the ideas are:
ptop:transitiveOver is faster than owl:TransitiveProperty: quadratic vs cubic complexity over the length of transitive chains
ptop:PropChain (a 2-place chain) is faster than general owl:propertyChainAxiom (n-place chain) because it doesn't need to unroll the rdf:List underlying the representation of owl:propertyChainAxiom.
Under some conditions you can translate the standard OWL constructs to these custom constructs, to have both standards compliance and faster speed:
use rule TransitiveUsingStep; if every TransitiveProperty p (eg skos:broaderTransitive) is defined over a step property s (eg skos:broader) and you don't insert p directly
if you use only 2-step owl:propertyChainAxiom then translate them to custom using the following rule, and infer using rule ptop_PropChain:
Id: ptop_PropChain_from_propertyChainAxiom
q <owl:propertyChainAxiom> l1
l1 <rdf:first> p1
l1 <rdf:rest> l2
l2 <rdf:first> p2
l2 <rdf:rest> <rdf:nil>
----------------------
t <ptop:premise1> p1
t <ptop:premise2> p2
t <ptop:conclusion> q
t <rdf:type> <ptop:PropChain>
http://rawgit2.com/VladimirAlexiev/my/master/pubs/extending-owl2/index.html describes further ideas for extended property constructs, and has illustrations.
Let us know if we can help further.
After thinking this for bit, I came up with a concrete example. The Oral Health and Disease Ontology (http://purl.obolibrary.org/obo/ohd.owl) contains three interrelated entities:
a restored tooth
a restored tooth surface that is part of the restored tooth
a tooth restoration procedure that creates the restored tooth surface (e.g., when you have a filling placed in your tooth)
The axioms that define these entities are (using pseudo Manchester syntax):
restored tooth equivalent to Tooth and has part some dental restoration material
restored tooth surface subclass of part of restored tooth
filling procedure has specified output some restored tooth surface
The has specified output relation is a subproperty of the has participant relation, and the has participant relation contains the property chain:
has_specified_input o 'is part of'
The reason for this property chain is for reasoner to infer that if a tooth surface participates in a procedure, then the whole tooth that the surface is part of participates in the procedure, and, furthermore, the patient that the tooth is part of also participates in the procedure (due to the transitivity of part of).
As a concrete example, let define some individuals (using pseudo rdf):
:patient#1 a :patient .
:tooth#1 a :tooth; :part-of :patient#1
:restored-occlusal#1 a :restored-occlusal-surface; :part-of tooth#1 .
:procedure#1 :has-specified-output :restored-occlusal#1 .
Suppose you want to query for all restored teeth:
select ?tooth where {?tooth a :restored-tooth}
RDFS-Plus reasoning will not find any restored teeth b/c it doesn't reason over equivalent classes. But, OWL-Horst and OWL2-RL will find such teeth.
Now, suppose you want to find all patients that underwent (i.e. participated in) a tooth restoration procedure:
select ?patient where {
?patient a patient: .
?proc a tooth_restoration_procedure:; has_participant: ?patient .
}
Again, RDFS-Plus will not find any patients, and neither will OWL-Horst b/c this inference requires reasoning over the has participant property chain. OWL2-RL is needed in order to make this inference.
I hope this example is helpful for the interested reader :).
I welcome comments to improve the example. Please keep any comments within the scope of trying to make the example clearer. Its purpose is to give insight into what these different levels of reasoning do and not to give advice about which reasoner is most appropriate.
I have a background in frame-based ontologies, in which classes represent concepts and there's no restriction against assertion of class:class relationships.
I am now working with an OWL2 ontology and trying to determine the best/recommended way to represent "canonical part-of" relationships - conceptually, these are relationships that are true, by definition, of the things represented by each class (i.e., all instances). The part-of relationship is transitive, and I want to take advantage of that so that I'd be able to query the ontology for "all parts of (a canonical) X".
For example, I might want to represent:
"engine" is a part of "car", and
"piston" is a part of "engine"
and then query transitively, using SPARQL, for parts of cars, getting back both engine and piston. Note that I want to be able to represent individual cars later (and be able to deduce their parts by reference to their rdf:type), and of course I want to be able to represent sub-classes of cars as well, so I can't model the above-described classes as individuals - they must be classes.
It seems that I have 3 options using OWL, none ideal. Is one of these recommended (or are any discouraged), and am I missing any?
OWL restriction axioms:
rdfs:subClassOf(engine, someValuesFrom(partOf, car))
rdfs:subClassOf(piston, someValuesFrom(partOf, engine))
The major drawback of the above is that there's no way in SPARQL to query transitively over the partOf relationship, since it's embedded in an OWL restriction. I would need some kind of generalized recursion feature in SPARQL - or I would need the following rule, which is not part of any standard OWL profile as far as I can tell:
antecedent (body):
subClassOf(B, (P some A) ^
subClassOf(C, (P some B) ^
transitiveProperty(P)
consequent (head):
subClassOf(C, (P some A))
OWL2 punning: I could effectively represent the partOf relationships on canonical instances of the classes, by asserting the object-property directly on the classes. I'm not sure that that'd work transparently with SPARQL though, since the partOf relationships would be asserted on instances (via punning) and any subClassOf relationships would be asserted on classes. So if I had, for example, a subclass six_cylinder_engine, the following SPARQL snippet would not bind six_cylinder_engine:
?part (rdfs:subClassOf*/partOf*)+ car
Annotation property: I could assert partOf as an annotation property on the classes, with values that are also classes. I think that would work (minus transitivity, but I could recover that easily enough with SPARQL paths as in the query above), but it seems like an abuse of the intended use of annotation properties.
I think you have performed a good analysis of the problem and the advantages/disadvantages of different approaches. I don't know if any one is discouraged or encouraged. IMHO this problem has not received sufficient attention, and is a bigger problem in some domains than others (I work in bio-ontologies which frequently use partonomies, and hence this is very important).
For 1, your rule is valid and justified by OWL semantics. There are other ways to implement this using OWL reasoners, as well as RDF-level reasoners. For example, using the ROBOT command line wrapper to the OWLAPI, you can run the reason command using an Expression Materializing Reasoner. E.g
robot reason --exclude-tautologies true --include-indirect true -r emr -i engine.owl -o engine-reasoned.owl
This will give you an axiom piston subClassOf partOf some car that can be queried using a non-transitive SPARQL query.
The --exclude-tautologies removes inferences to owl:Thing, and --include-indirect will include transitive inferences.
For your option 2, you have to be careful in that you may introduce incorrect inferences. For example, assume there are some engines without pistons, i.e. engine SubClassOf inverse(part_of) some piston does not hold. However, in your punned shadow world, this would be entailed. This may or may not be a problem depending on your use case.
A variant of your 2 is to introduce different mapping rules for layering OWL T-Tboxes onto RDF, such as described in my OWLStar proposal. With this proposal, existentials would be mapped to direct triples, but there is another mechanism (e.g. reification) to indicate the intended quantification. This allows writing rules that are both safe (no undesired inferences) and complete (for anything expressible in OWL-RL). Here there is no punning (under the alternative RDF to OWL interpretation). You can also use the exact same transitive SPARQL query you wrote to get the desired results.
So, here is something with OWL / Protégé I can't quite understand:
Let's say I have a class Clazz which is an enumerated class containing only the individuals I1 and I2. I then create a third individual I3 and declare it to be of type Clazz.
If I now start a reasoner, I would expect it to infer a sameIndividualAs between all (or at least some) of the indidivuals. This is not the case, I tested with both Hermit and Pellet reasoners.
If I explicitly state the three individuals to be different from each other, the ontology becomes inconsistent. Can anyone tell me why the individuals are not showing up to be sameIndividualAs in Protégé in the first case?
As there is no unique name assumption in OWL, the ontology is consistent until it is explicitly asserted that the manually typed individual is owl:differentFrom all of the individuals defining the class (the set restricted with owl:oneOf). If that's not asserted, in case there is more than one individual, the only inference that can be made is that, in your case, I1and I2 are members of the class Clazz. I3 should be the same as one of the individuals, but there is no information to decide as which. You can remove this ambiguity by making Clazz defined as owl:oneOf :I1. Then there will be no ambiguity and sufficient information to infer that :I3 owl:sameAs :I1.
I am currently working on a ontology.
I created a bunch of individuals (every individual should be sorted to a different optimization problem like TSP, VRP and VRPTW).
But I think the open world assumption creates some problems.
In Individuals I want the reasoner to realize that Auftrag 1 is a TSP, Auftrag 2 is a VRP and Auftrag 3 a VRPTW. At the moment it works
but only because for 1 and 2 I use the "exactly" cardinality in types.
I have to do this because if not the reasoner may think that there could be more than one of this kind (in this example warehouse or simpleVehicle).
Because of that I created this "exactly 1" cardinality on types so that the reasoner can only sort it right. But as soon as I am right the closure axiom could help me so to prevent the open world assumption at this point?
I hope you understand my point.
I attached the file, best regards!
File:
https://www2.zippyshare.com/v/loP7YJNP/file.html
I wanna get the total number of relationships between classes (just classes without taking into account individuals) in an ontology, I'm not sure if I can get it through the metrics that provides protege. I'll put an example below to show you what I'm looking for.
This picture represents an excerpt of people ontology. For me the total number of relationshiphs between classes is 11 (8 subclass relationships and 3 other relationships ).
By visualizing the ontology metrics provided by protege, this is what I get (in the picture below): As you can see I have just 5 subclassOf axioms instead of 8. And I don't know if it's possible to get the total number of relationships from only those metrics. I wanna get the total number using java code based on OWL-API. I use Protege just to have an idea of numbers of metrics.
Please if you have any idea that may help me to get the total number, I would be grateful
Thank you
You can count axioms of a specific type with OWLOntology::getAxioms(AxiomType) - I believe that's what Protege is doing - but I don't think that will be enough for your objective. EquivalentClasses axioms with more than two elements in it will count for more than one link, for example.