PURO is a primarily graphical language for capturing ontological conceptualizations at the level of interconnected example entities and their types. The PURO Modeler tool allows users to create the PURO models and transform them to skeletons of models in other languages: OWL and OntoUML. In real-world scenarios, a single PURO model quickly becomes too large to be managed due to its graphical nature. We demonstrate a solution consisting of modularization of the models and a semiautomated way of merging the modules before they are transformed into OWL or OntoUML.
Modularization is an obvious approach to handling large knowledge bases and ontologies. While the size of ontologies represents a challenge for multiple tasks, including reasoning, it is probably most harmful when it comes to human interaction. Therefore, ontological modeling languages that emphasize visual browsing and editing by human users are particularly sensitive in this respect.
An example of such a language is PURO [
The original tool for authoring PURO models, PURO Modeler, only allowed users to create monolithic models, as no module-handling support was available. This paper presents a new addition to PURO Modeler, which allows merging independently developed International Workshop on Knowledge Graph Generation from Text (TEXT2KG 2022) and Modular models (modules), thus enabling drafting larger skeletons of ontologies within a single project.
PURO was primarily developed as a graphical language for kick-starting the development
of OWL ontologies [
An alternative use case identified for PURO is to kick-start the development of
ontological conceptual models in a semantically rich language such as OntoUML [
Both use cases are supported by the prototype PURO editing tool, PURO Modeler.1 However, a weakness of the original PURO Modeler was the monolithic nature of the developed PURO models, which often became unmanageable before reaching the size of a useful ontology skeleton.
The manageable size of PURO models can be achieved through their modularization.
Since each model is meant to represent an example real-world situation, it is natural
to confine each model to some small domain area, for example, to create one module
(a partial model) per competency question; e.g., one module may cover the notion of
a person and its relationships, and another one may cover the topic of organizations.
Since the goal is to create a single, coherent ontology skeleton, the modules need to be
merged into a single PURO model before the transformation. Some automation and
visualization techniques (since we still need to show the whole merged model to enable
1Available at http://protegeserver.cz/purom5/
checking for errors and manual editing) can be employed to make the merging easier.
Namely, we can match identical entities present in diferent modules 2 automatically in a
way similar to ontology matching [
The heuristic matching process is applied to 2 modules at a time (called source and target, where the target module stores the result of the merging) in the following way: (1) A set of pairs of B-types, and of B-objects, respectively, is found, such that either the string similarity of their names is above a user-controlled threshold or they are synonyms in WordNet [6]. (2) is shown to the user, who can modify and add further pairs to it. (3) Pairs of entities (, ) having identical types and names are found, such that there exists a pair (, ) ∈ such that is linked to and is linked to ; each such pair (, ) is added to .3 (4) Entities not present in are copied from the source module to the target module. (5) Links between entities are copied from the source module to the target module, according to the matched pairs from .
Diferent modules might include example instance-level entities of the same type involved in diferent relationships. For example, we can have a module with a person instance John having hobbies, and another module with Bob being employed. To decrease the number of visible nodes and links, we can merge John and Bob into a single entity named, e.g., ’some person,’ involved both in the ’hasHobby’ and ’isEmployed’ relationships. Such merging can be optionally turned on by the user and is very straightforward: each group of B-objects that are instances of the same B-type is merged into a single placeholder B-object that bears the name of the parent B-type.
Any part of the model can be selected and grouped in the visualization. The group can then be collapsed into a single node, with relationships to the nodes inside the group preserved in the visualization. 2E.g., the person module will probably include an organization entity that can be matched to the organization module. 3Since entities other than B-types and B-objects are always dependent on some B-type or B-object, we can only match them when they are linked to an already matched pair of B-types or B-objects.
As a proof of concept, a prototype PURO Joiner was implemented as a Javascript web app.4 It allows for authoring and merging pairs of PURO models. When the merging is initiated, the modules are displayed side by side, and a list of automatically matched entities is shown (see example in Figure 1). The user can check and edit the mapped pairs, as well as add the mappings manually. After the merge operation has been performed, a single PURO model is displayed where the mapped entities are merged into a single node. The user can optionally ask for the automatic merging of instances. When the respective button is clicked, all instances of the same B-type are merged into a single node named ’general name of the B-type.’ Figure 2 then shows the collapsing of a group of entities into a single node (in the visualization).
The task addressed by our new tool consists in supporting interactive merging of ontology modules for a particular modeling language, namely, PURO.
Automation of ontology module merging has so far received smaller attention than
ontology alignment [
A well-known, early tool suite similar to ours (but having more functionality) is PROMPT [7]. It enabled users to both align and merge (frame-based) ontological modules in an interactive mode. The main diference from our approach is in the substantial dissimilarity of the underlying modeling language (esp. the presence of instances, meta-types, and n-ary relationships in PURO), in the visual interface (PURO Modeler/Joiner uses a node-link view, while PROMPT relied on an indented list display), and possibly also in the overall scenario: PURO Joiner is primarily meant for combining modules that may possibly overlap in a few entities, rather than for merging multiple ontologies describing the same domain, as shown in the demonstration of PROMPT [7].
A more recent representative of interactive OWL ontology alignment tools is the Alignment tool [8]. It ofers both a list-based and graph-based view of the two to-bealigned ontologies. However, it does not support merging.
The proposed extension of PURO Modeler is likely to significantly improve its scalability for larger ontology skeletons, thus potentially leading to its broader adoption by ontology engineers.
We envision several improvements. The tool could automatically search for existing related entities in other modules and suggest them to the user. This would encourage the reuse of existing entities whenever possible, making the subsequent merging possibly fully automated. To enable collaboration between multiple users, a git-inspired versioning system could be implemented. The layout of the merged model could be arranged automatically for more clarity, e.g., collapsing parts of it based on some heuristics.
An important next step will be testing with real users. The first experiment will be qualitative. Several users will create partial modules, and then the group will collaborate on merging them, while we analyze the process and results. Future experiments will compare performance of users working on large models with and without the modularization.
Supported by CHIST-ERA within the CIMPLE project (CHIST-ERA-19-XAI-003). [6] C. Fellbaum (Ed.), WordNet An Electronic Lexical Database, The MIT Press, Cambridge, MA, 1998. [7] N. F. Noy, M. A. Musen, The PROMPT suite: interactive tools for ontology merging and mapping, Int. J. Hum. Comput. Stud. 59 (2003) 983–1024. [8] S. Karampatakis, C. Bratsas, O. Zamazal, P. Filippidis, I. Antoniou, Alignment: A hybrid, interactive and collaborative ontology and entity matching service, Inf. 9 (2018) 281.