Focused categorization power (FCP) has been recently introduced as a way of measuring the utility of an ontology by the count of concept expressions expressible using the ontology and subsumed by the given (focus) class/es. OReCaP is an ontology search interface with an integrated ontology ranking method based on the FCP value. The choice of ontologies for reuse is supported by the listing of di erent types of categories provided by the ontology for a particular focus class.
When reusing existing OWL ontologies for publishing a dataset in RDF or
developing a new ontology, preference may be given to those providing extensive
subcategorization for the classes deemed important in the new dataset schema
or ontology (focus classes). The reused set of categories may not only consist of
named classes but also of some compound concept expressions viewed as
meaningful categories by the knowledge engineer and possibly later transformed to
a named class, too, in a local setting. In our previous work [
As an example, let us consider the case of ontology reuse for describing the dataset of a used car retailer. For the focus class Vehicle in a particular ontology, we can consider its named subclasses such as Motorcycle, but also anonymous concept expressions1 such as Vehicle and hadAccident some Thing (vehicle that underwent an accident), Vehicle and hasSeller some Company (vehicle sold by a company, not by a person), or Vehicle and hasFuel value CNG (vehicle that uses CNG as fuel). These three examples belong each to a di erent concept expression type; however, all are a part of the `existential restriction family'. Their Tbox templates are, in turn: 9P:>, 9P:C, and 9P:fig. Exhaustively enumerating all such expressions that can be constructed from the signature of the given ontology would of course have little relevance. However, we assume that the expressions can be ltered using syntactic patterns over the Tbox axioms; most prominent role is played by the domain/range axioms, in this respect. For example, 9P:> is more likely to be a meaningful subcategory for a focus class F C if there is an axiom in the form
P rdfs:domain F C
in the (inferential closure of the) ontology. The heuristic patterns for other
axioms types [
In this demo paper we present the rst operationalization of the notion of
FCP in its main target context: ontology recommendation for dataset
description. Recommendation of ontologies and of individual terms from them have
recently been an active eld of research. The mainstream approach consists in
various kinds of term/ontology popularity computation. For example,
Atemezing & Troncy [
We believe that the FCP is yet another relatively orthogonal feature to be
considered: while some existing approaches include a similar notion of class
`importance' within the ontology [
The survey on ontology reuse strategies by Schaible et al. [
OReCaP is a web application2 that aims to demonstrate the calculation of FCP scores for ontologies in the context of an ontology search (for reuse) scenario. 1 Here written in the human-readable Manchester syntax, see https://www:w3:org/
TR/owl2-manchester-syntax/. 2 Available as demo at https://fcp:vse:cz/orecap.
The interaction starts with a keyword-based search where the input consists of at least one focused class keyword and of optional additional keywords. The intuition is that the focused class keyword/s denotes the high- or medium-level type/s of entities whose instances are to be further sub-categorized using concepts from the ontology; the additional keywords, on the other hand, correspond to whatever domain terms. Imagine, for example, that the data is currently stored in a relational database. The focused class keyword might then often be the name of the top-level table (which can be, e.g., `Client', `Patient', `Vehicle', `Account', or the like); the additional keywords can be taken, e.g., from the names of subordinate tables, table columns, or prede ned values for the elds.
The search returns a sorted list of ontologies whose classes match one or more of the provided keywords by their IRI, name or description; classes with a match of focused class keyword are listed rst. The matched classes are listed for each ontology. Classes that match the focused class keywords are preselected (i.e., checked) by default; classes that matches the additional keywords are not preselected but can be selected (checked) manually by the user.
The next step is to execute the FCP calculation for a chosen ontology, given
the selected classes as focus classes, by clicking on the `Calculate FCP' button. In
a pop-up window, metadata about the ontology including its URI and namespace
is displayed, along with the total FCP score, which is calculated based on the
FCP weight values and the categorizations listed at the bottom. This score is
the sum of all partial scores for each focus class. The weight values can be
adjusted for each calculated ontology according to the user's assessment of each
category type, and the resulting FCP score will change accordingly. The global
FCP weights can be changed in the settings section, so that every new FCP
calculation would use them as the default weight values. The calculated FCP
score is then saved to a comparison list, which shows the FCP-based ranking of
the ontologies. Furthermore, the details of the calculations and categorizations
can be inspected, where for each focus class, its categories are displayed. There
are 4 types of categories considered, conforming to the earlier formulated [
OReCaP makes use of the Linked Open Vocabulary API3 for the
keywordbased search and for retrieving the ontology metadata. The FCP calculation
itself [
This scenario addresses sports event data publishing. For the focus class
keywords competition, round, and match, and additional keywords game, medal,
player, team, and sport, OReCaP lists the BBC Sport Ontology as one of the top
matches. The FCP calculation for this particular ontology with 3 selected classes,
sport:Competition, sport:Match, and sport:Round, yields, with previously
empirically estimated default category type weights [
Focus class sport:Competition sport:Match sport:Competition sport:Round sport:Match sport:Competition sport:Round sport:Match sport:Competition sport:Round Total FCP score
Of course, not all categories are equally meaningful. To re ect that, the user can adjust the weight values as described in Section 2. At the moment, OReCaP only allows to change the weight values at the level of the whole category types (we also plan to add the option of altering the weight of the individual categories).
Another result retrieved for the same keyword setting is the The DBpedia Ontology. Even if it has more (additional) keyword matches, it only matches a single focus class keyword (with dbpedia-owl:Competition), and its (default) FCP score is only 5.00. A partial screenshot with these two results is in Fig. 1. Focused class keywords * competition round match
CALCULATE FCP
Additional keywords game medal player team unitsport event dbpedia-owl: The DBpedia Ontology http://dbpedia.org/ontology/ Matched keywords game (1) medal (1) player (1) team (1) competition (1) match (1) (Un)select your focus classes dbpedia-owl:Competition dbpedia-owl:Game dbpedia-owl:GolfPlayer dbpedia-owl:Netbal Player dbpedia-owl:SquashPlayer dbpedia-owl:Handbal Team dbpedia-owl:Basebal Team dbpedia-owl:NCAATeamSeason dbpedia-owl:AustralianFootbal Team dbpedia-owl:SportsTeamSeason dbpedia-owl:CyclingCompetition dbpedia-owl:SportCompetitionResult dbpedia-owl:WrestlingEvent dbpedia-owl:Footbal Match
The notion of FCP is fundamentally novel within the family of content-based ontology recommendation approaches. The current demo is meant to demonstrate its contribution. In the future we plan to compare the results obtained through this measure with those obtained by popularity, credibility, and other existing measures, to see how they could support the dataset publisher in a complementary way, within a coherent methodology. We would also like to perform experiments with the tool in various domains, in order to devise novel heuristics for setting parameters on the onset of the ontology search and reuse sessions. Acknowledgment This research is being supported by project no. 18-23964S of the Czech Science Foundation, \Focused categorization power of web ontologies ".