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|pdfUrl=https://ceur-ws.org/Vol-3235/paper13.pdf
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|authors=Aldo Gangemi,Anna Sofia Lippolis,Giorgia Lodi,Andrea Giovanni Nuzzolese
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https://ceur-ws.org/Vol-3235/paper13.pdf
FrODO: Generating Ontologies from Competency
Questions in One Click
Aldo Gangemi1,2 , Anna Sofia Lippolis1,* , Giorgia Lodi1 and
Andrea Giovanni Nuzzolese1,†
1
Institute of Cognitive Sciences and Technologies, Italian National Research Council (ISTC-CNR), Via San Martino della
Battaglia 44, 00184, Rome, Italy
2
Department of Classical and Italian Philology, University of Bologna, Via Zamboni 32, 40126, Bologna, Italy
Abstract
In this paper we demonstrate the potentiality of the Frame-based Ontology Design Outlet (FrODO), a
tool that relies on FRED for generating RDF from the natural language. FrODO exploits frame semantics
for tailoring sound OWL ontologies addressing the given CQs. We envision that FrODO can be used to
make agile ontology design methodologies (e.g. XD, SAMOD, etc.) smoother.
Keywords
Ontology Engineering, Frame semantics, Machine Reading, Knowledge graph, Knowledge representation
1. Introduction
Competency questions [1] (CQs) represent requirements in a number of agile ontology engineer-
ing methodologies, such as the eXtreme Design [2] (XD) or SAMOD [3]. In such methodologies
most effort lies in the design of ontology modules able to address the CQs that have been
previously identified. In this paper we demonstrate the Frame-based Ontology Design Outlet
(FrODO). FrODO is a novel method and Web tool for automatically drafting OWL ontologies
from CQs. FrODO builds on and benefits from FRED [4] a machine reading [5] tool aimed
at gathering RDF from text written in natural language. FRED in fact produces RDF graphs
from text that are (i) domain- and task-independent, and (ii) designed according to the frame
semantics [6] and ontology design patterns [7]. In essence, FrODO extends FRED specifically
on the case of CQs by tailoring the RDF produced by FRED into domain ontologies. This is done
by leveraging its formal representation based on the frame semantics. The domain ontologies
produced by FrODO are drafts that can be used to feed agile ontology design methodologies. In
this paper we demonstrate FrODO as a tool for generating domain ontologies formalised as
OWL from competency questions automatically. This demo paper is associated with [8], which
SEMANTICS 2022 EU: 18th International Conference on Semantic Systems, September 13-15, 2022, Vienna, Austria
*
Corresponding author.
†
The authors are sorted alphabetically as they equally contributed to this paper.
$ aldo.gangemi@cnr.it (A. Gangemi); annasofia.lippolis@istc.cnr.it (A. S. Lippolis); giorgia.lodi@cnr.it (G. Lodi);
andreagiovanni.nuzzolese@cnr.it (A. G. Nuzzolese)
0000-0001-5568-2684 (A. Gangemi); 0000-0002-0266-3452 (A. S. Lippolis); 0000-0001-6020-5874 (G. Lodi);
0000-0003-2928-9496 (A. G. Nuzzolese)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
was accepted for publication to the Research & Innovation track of SEMANTiCS 2022.
2. Related work
Many solutions for generating ontologies from text address an Ontology Learning and Popula-
tion (OL&P) task [9, 10]. Examples of such methods include Text2Onto [11], OwlExporter [12],
and the approach proposed by Tanev and Magnini [13]. Most of these solutions are implemented
on top of machine learning methods. Hence, they are typically data hungry, i.e. they require
large corpora, sometimes manually annotated, in order to learn rules for ontology automatic
construction. Such rules are defined through a training phase that can take a long time. On the
contrary, our solution does not depend on any training and is unsupervised. Other approaches
to OL&P use either lexico-syntactic patterns [14], or hybrid lexical-logical techniques [15].
Finally, there are solutions for generating SPARQL queries from CQs, such as [16]. The latter
can be seen as a similar and complementary task to the generation of OWL ontologies from text.
FrODO differs from all these solutions at the state of the art as it relies on machine reading as
the paradigm for achieving the automatic construction of domain ontologies from competency
questions.
3. FrODO
FrODO is a web-based application1 that generates ontologies from CQs. This is done by
extending FRED that uses frame semantics [6] for organising domain independent RDF graphs
generated from text. Hence, FrODO builds on top of FRED for refactoring its RDF graph by
means of graph traversal strategies that exploit the frame semantics. In this we assume that
frames and frame arguments are the key tools to leverage on for drawing domain-relevant
boundaries around the RDF graph produced by FRED, thus enabling the generation of domain
ontology drafts. Two refactoring strategies are implemented by FrODO in order to cope with
the possible frame representation patterns available in FRED, i.e. (i) 𝑛-ary relations and (ii)
periphrastic relations. We refer the interested readers to [4] for more details about FRED and [8]
for more details about FrODO. In this paper we aim at demonstrating how FrODO can support
ontology engineers and practitioners to generate an ontology from CQs automatically. Figure 1
shows the user interface provided by FrODO. It consists of a main text area meant for entering a
CQ to be processed by FrODO and an input text for specifying the IRI to be used as the identifier
of the resulting ontology. The former input is mandatory, while the latter is optional, i.e. if no
ontology identifier is specified then a default one is used2 .
To demonstrate FrODO we use the CQ “Who commissioned a component of a system?” as a
running example. The CQ is about the design on an ontology related to the commissioning of
systems and their components. Such a CQ comes from a real world set of CQs we defined in
the context of the WHOW project3 for representing the requirements of an ontology network
about systems for environmental monitoring. When a user provides the previous CQ and clicks
1
FrODO is available online at https://w3id.org/stlab/frodo.
2
The ontology identifier used as default by FrODO is https://w3id.org/stlab/ontology/.
3
https://whowproject.eu/
Figure 1: The user interface of FrODO.
on the button labelled “Get OWL!”, FrODO starts its processing, which is summarised by the
UML activity diagram depicted in Figure 2.
Figure 2: Process implemented by FrODO. In this UML activity diagrams the yellow boxes represent
activities, while the black circle and encircled black circle represent the initial and final nodes, respectively.
The arrows among activities represent the direction of the workflow execution. Finally, the light-gray
and dark-gray boxes pinned on the activities identifies the objects required by the activities as input
and output, respectively.
The first step of the worflow requires FrODO to query FRED in order to get an RDF graph
from the text of the CQ. Then the frames are recognised in the graph either in the form of 𝑛-ary
or periphrastic relations. Once the frame occurrences are recognised the frame arguments with
their associated roles are used for generating an ontology draft with domain terminology. This
draft is finally enriched with annotations (i.e. RDFS labels), inverse properties, and restriction
axioms. Listing 1 reports the ontology returned by FrODO to a user who provided the CQ
expressed as our running example. The ontology is serialised according to the Manchester
syntax.
Listing 1: OWL ontology produced for the CQ “Who commissioned a component of a system?”.
ObjectProperty: involvesComponent
Annotations: rdfs:label "involves component"
Range: Component
InverseOf: isComponentInvolvedIn
ObjectProperty: involvesPerson
Annotations: rdfs:label "involves person"
Range: Person
InverseOf: isPersonInvolvedIn
ObjectProperty: isComponentInvolvedIn
Annotations rdfs:label "is component involved in"
Domain: Component
ObjectProperty: isPersonInvolvedIn
Annotations: rdfs:label "is person involved in"
Domain: Person
ObjectProperty: componentOfSystem
Annotations: rdfs:label "component of system"
Range: System
InverseOf: isComponentOfSystemOf
ObjectProperty: isComponentOfSystemOf
Annotations: rdfs:label "is component of system of"
Domain: System
Class: System
Annotations: rdfs:label "System"@en
Class: Commissioning
Annotations: rdfs:label "Commissioning"@en
SubClassOf: componentOfSystem some System
Class: Component
Annotations: rdfs:label "Component"
Class: ComponentCommissioning
Annotations: rdfs:label "Component commissioning"@en
SubClassOf: Commissioning, involvesComponent some Component, involvesPerson some Person
Class: Person
Annotations: rdfs:label "Person"
4. Conclusions
In this work we demonstrate the Frame-based Ontology Design Outlet (FrODO), which is a tool
able to draft ontologies from competency questions (CQs) automatically. FrODO extends FRED
by leveraging frame semantics to gather domain knowledge from the RDF graph produced by
FRED from textual CQs. FrODO can be used by ontology engineers to make agile ontology
design methodologies (e.g. XD, SAMOD, etc.) smoother.
Acknowledgments
This work was partially supported by the European Commission (EC) through the Connecting
Europe Facility (CEF) programme under the WHOW project WHOW (grant agreement no.
INEA/CEF/ICT/A2019/2063229).
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