The fact that ontologies are considered shareable and reusable components has always been claimed as one of their main advantages. While reusing ontologies is promised to reduce time and resources invested during ontology development, the experience shows that reusing ontologies also involves a costly efort. In this work, we first analyse how ontologies representing the building domain currently reuse each other. Finally, we detect existing Web Ontology Language (OWL) modelling patterns in the ontologies analysed and generate a graphical pattern library to facilitate reuse of the observed patterns.
During this study, no specific patterns have been found; however, common OWL patterns are detected and a graphical library containing the generalised ontology design patterns has been created. This library compiles the OWL constructs combination that appears in the building domain ontologies.
In conclusion, this study is quite strict in the sense of pattern definition, as the matching is done at the URI level instead of at the concept level, for example by examining the attached labels to an URI. Although the current work has been useful to provide a library of generalised patterns, it could be extended by incorporating ontology alignment techniques to find specific patterns (Section 5).
Some studies have analysed how ontologies are reused in general ontologies [
Regarding the construction domain, Schenider [
Previous works have analysed the use of OWL construct in the Semantic Web [
To carry out this study, the workflow followed in Figure 1 has been followed. As can be observed, the
ifrst step is to collect the ontologies to be analysed. In this case, we have compiled ontologies about
buildings from Linked Open Vocabularies [
Once the ontologies have been selected and downloaded, several processing steps have been carried out. First, in order to know how building ontologies reuse each other, it has been observed in which ontologies hard reuse is present, by means of using owl:imports, and which ontologies implement soft reuse by referencing other ontology elements URIs (note that metadata references are ignored). Second, all ontologies are inspected to identify repeating patterns. Prior to pattern detection, the system identifies structures in the ontologies code stemming from the classes’ rdfs:subClassOf, owl:equivalentClass and owl:disjointWith statements in which the object is a restriction, that is, the object is not a named class. These structures can be specific, considering the exact URIs for the ontology elements (see Figure 2(a) or general structures defined by generalising the particular URIs to the OWL or RDF(S) type of elements (see Figure 2(b)).
To identify patterns, our system counts how many times the exact structure appears in all the ontologies. This is done for the specific and general structures to identify specific and general patterns, respectively.
Finally, taking as input the general patterns identified, a Chowlk library has been created to ease the Identify hard and
soft reuse Hard and soft reuse matrix Remove duplicated and unavailable ontologies 18 ontologies
Detect specific
structures Detect specific
patterns 0 Specific patterns in 5 ontologies workflow line input/output line
Detect structures Detect generic patterns
Generate template library OWL patterns library 35 generic patterns in 5 ontologies Legend
Step
Data source
Document /
result use of common OWL structures in ontologies.3 It should be noted that the generation of this library has been done semi-automatically.
Regarding the hard and soft reuse observed in the analysed ontologies, it can be observed that the analyzed ontologies do not reuse much each other and the most common reuse technique is soft reuse. 3Exception: pattern 32 is not included in the library as it is more expressive than the Chowlk notation at the time of writing this paper. (a) Example of an specific structure (b) Example of a general structure
The following list shows for each analysed ontology which ontologies it imports (hard reuse) and which ontologies are referenced (soft reuse). In the case of soft reuse, the number of ontology elements referenced are included between “()”. The ontologies analyzed in this study are identified by their prefix while ontologies not included in the analysis are identified by their namespaces.
With regard to the identified patterns, we can observe that no specific patterns are repeated. This means that the specific structures identified in the ontologies are not repeated in any other ontology. This result was expected after analysing the soft reuse, as this study seeks the repeated appearance of same URIs in equal structures. For this reason, that exact URIs are matched, we cannot claim that similar concepts are not defined in more than one ontology in a similar way.
Looking now at the general patterns extracted4 we can see that 35 patterns are identified among 5 ontologies. These patterns are only present in 5 ontologies because the rest of ontologies did not include class descriptions using OWL restrictions, boolean combinations or cardinalities. That is, there was no classes’ rdfs:subClassOf, owl:equivalentClass and owl:disjointWith statements whose object was a restriction or a blank node instead of a named class in 13 ontologies.
The identified general patterns are depicted in Figure 3, Figure 4 and Figure 5 following the Chowlk notation. In such figures there are three numbers on the left of each pattern; the top number represents the pattern identifier, according to the numeration in the supplementary material; the middle number indicates in how many ontologies appears the pattern; and the bottom number indicates how many repetitions of the pattern have been observed considering all ontologies. 4The data resulting from the study is available at https://doi.org/10.5281/zenodo.10997320. sal (denoted with “(all)”) or existential (denoted with “(some)”) restrictions for object and datatype properties. For example, pattern 4 represents a rdfs:subClassOf restriction involving a existential constraint over a given object property. These patterns can involve property characteristics such as owl:FunctionalProperty (denoted with “(F)”) or owl:TransitivelProperty (denoted with “(T)”), as for example patterns 6, 9, 14 or 26 for object properties and 1 and 7 for datatype properties. Finally, we can observe that some patterns, 2 and 26, combine the restrictions with a union of classes instead of named classes. qualified cardinality (denoted with “(X..Y)”) or qualified cardinality (denoted with “[X..Y]”) restrictions for object and datatype properties. For example, pattern 10 represents a rdfs:subClassOf restriction involving a minimum non-qualified cardinality constraint over a given object property and pattern 27 represents a rdfs:subClassOf restriction involving an exact qualified cardinality constraint over a given object property. Equally to the previous case, these patterns can involve property characteristics, ns:Class1 (some) ns:ObjectProperty1 ns:Class2 ns:Class1 (all) ns:ObjectProperty1 ns:Class2 ns:Class1 (some) (F) ns:ObjectProperty1 ns:Class2 ns:Class1 as, for example, pattern 31 that represents an exact non-qualified cardinality over a given datatype property.
Figure 5 depicts the patterns stemming from a rdfs:subClassOf declaration followed by nested universal or existential restrictions for object properties. In all the cases the properties involved in the restrictions are functional properties and it can be observed that for patterns 19 and 21 the last nested ns:Class1 (all) (T) ns:ObjectProperty1
ns:Class2 ns:Class1 (all) (T) ns:ObjectProperty1 ⨆ ns:Class2 ns:Class3 ns:Class1 ns:Class1 ns:Class1
This work has shown the degree of reuse between ontologies in the building domain, which is quite low; however, the reasons for this are not explored in detail. In addition, regarding the observed reuse, it should be analysed whether it corresponds to references to other domains.
On the one hand, and in line with the low level of soft reuse between ontologies, we have observed that there are no specific domain patterns between the ontologies. However, this conclusion could be due to the fact that this study compares URIs rather than labels of concepts. In this sense, future lines of work include reproducing the study considering labels and annotations or even running ontology matching algorithms before searching for patterns.
On the other hand, we have extracted 35 general patterns that answer, in a graphical way, how much OWL knowledge is needed to understand the analysed ontologies. In addition, a patterns library for Chowlk has been developed to facilitate the reuse of such patterns.
During this process two valuable lessons have been learnt about pattern generation using the Chowlk notation. First, we have detected one case of an OWL structure (pattern 32) that is currently not supported by the notation, which implies an impact in the notation that should be extended. Second, we observed that the process of extracting patterns does not take into account whether the involved object and datatype properties have domain or range defined, information that is needed to generate the Chowlk visualisations. At this point, we arbitrarily decided to draw the patterns using the notation for domain and range definition for this version; however, it is planned for the next version to keep track of that information at the structure level.
This work has been supported by Horizon 2020 research and innovation programme under grant agreements no. 958310 (COGITO) and 101016854 (AURORAL) and by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with the Universidad Politécnica de Madrid in the Excellence Programme for University Teaching Staf, in the context of the V PRICIT (Regional Programme of Research and Technological Innovation).