=Paper= {{Paper |id=None |storemode=property |title=The Manchester OWL Repository: System Description |pdfUrl=https://ceur-ws.org/Vol-1272/paper_57.pdf |volume=Vol-1272 |dblpUrl=https://dblp.org/rec/conf/semweb/MatentzogluTPS14 }} ==The Manchester OWL Repository: System Description== https://ceur-ws.org/Vol-1272/paper_57.pdf

The Manchester OWL Repository: System
Description

Nicolas Matentzoglu, Daniel Tang, Bijan Parsia, and Uli Sattler

The University of Manchester
Oxford Road, Manchester, M13 9PL, UK
{matentzn,bparsia,sattler}@cs.manchester.ac.uk

Abstract. Tool development for and empirical experimentation in OWL
ontology research require a wide variety of suitable ontologies as input for
testing and evaluation purposes and detailed characterisations of real on-
tologies. Findings of surveys and results of benchmarking activities may
be biased, even heavily, towards manually assembled sets of “somehow
suitable” ontologies. We are building the Manchester OWL Repository,
a resource for creating and sharing ontology datasets, to push the qual-
ity frontier of empirical ontology research and provide access to a great
variety of well curated ontologies.

Keywords: Repository, Ontologies, Empirical

1 Introduction
Empirical work with ontologies comes in a wide variety of forms, for example sur-
veys of the modular structure of ontologies[1], surveys of modelling patterns to
inform design decisions of engineering environments [4] and benchmarking activ-
ities for reasoning services such as Description Logic (DL) classification [2]. Since
it is generally difficult to obtain representative datasets, both due to technical
reasons (lack of suitable collections) and conceptual reasons (lack of agreement
on what they should be representative of), it is common practice to manually
select a somewhat arbitrary set of ontologies that usually supports the given
case. On top of that, few authors ever publish the datasets they used, often for
practical reasons (e.g. size, effort), which makes reproducing experiment results
often impossible. The currently best option for ontology related research is the
BioPortal repository [5], which provides a web based interface for browsing on-
tologies in the biomedical domain and a REST web service to programmatically
obtain copies of all (public) versions of a wide range of biomedical ontologies.
There are, however, certain problems with this option. First, the repository is
limited to biomedical ontologies, which makes BioPortal unsuitable for surveys
that require access to ontologies of different domains. The second problem is the
technical barrier of accessing the web service: It requires a good amount of work
to download all interesting ontologies, for example due to a range of ontologies
published in a compressed form or the logistical hurdle of recreating new snap-
shots over and over again. The third problem is due to the fact that there is
BioPortal User Web Google Webscrape
Data Gathering Custom
Webservice submitted crawl Search
Snapshot

Curation Oxford Ontology
BioPortal
Sources Library
MOWLCorp
OWL/XML ORIGINAL OWL/XML ORIGINAL
OWL/XML ORIGINAL

Manchester OWL Repository
Pool
OWL/XML ORIGINAL

Restful Webservice
Interface
Web Frontend API

Fig. 1. The repository architecture.

no shared understanding of what it means to “use BioPortal”. Different authors
have different inclusion and exclusion criteria, for example they only take the
ones that are easily parseable after download, or the ones that were accessible
at a particular point in time. The Manchester OWL Respository aims to bridge
that gap by providing a framework for conveniently retrieving some standard
datasets and allowing users to create, and share, their own.

2 Overall architecture

The Manchester OWL repository can be divided into four layers (see Figure 1).
The first layer represents the data gathering. Through web crawls, web scrapes,
API calls, and user contributions ontologies are collected and stored in their re-
spective collections. The second layer represents the three main data sources of
the repository, each providing ontologies in their original and curated (OWL/XML)
form. The third layer, the pool, represents a virtual layer in which access to the
ontologies is unified, providing some means for de-duplification because of the
possibility of corpora intersection. Lastly, the interface layer provides access to
the repository through a REST service and a web-based front end.

3 Data Gathering

The main component of the data gathering layer is a web crawl based on
crawler4j, a java-based framework for custom web crawling and daily calls to
the Google Custom Search API that fills the MOWLCorp, which makes up
the bulk of the repository’s data. An ongoing BioPortal downloader creates a
snapshot of BioPortal once per month using the BioPortal web services, whilst
retaining copies of all available versions so far. The third (minor) component of
the repository is a web scrape of the Oxford Ontology Library (OOL), a hand
curated set of ontologies which features some particularly difficult, and thus in-
teresting to reasoner developers, ontologies. Ontologies are downloaded in their
raw form and thrown in the curation pipeline.

4 Data curation

Ontology candidates from all three sources undergo a mild form of repair (un-
declared entity injection, rewrite of non absolute IRIs) and are exported into
OWL/XML, with their imports closure merged into a single ontology, while
retaining information about the axiom source ontology through respective an-
notations. Metrics and files for both the original and the curated versions of
the ontologies are retained and form part of the repository. The data curation
looks slightly different for all three data sources, especially with respect to filter-
ing. Apart from the criterion of OWL API [3] parse-ability, BioPortal and the
OOL are left unfiltered because they are already deemed curated. This means
that some ontologies in the corpus may not contain any logical axioms at all.
In MOWLCorp, on the other hand, we filter out ontologies that 1) have an
empty TBox (root ontology) and 2) have byte-identical duplicates after seriali-
sation into OWL/XML. The reason for the first step is our focus on ontologies
(which excludes pure collections of RDF instance data) and the fact that the
imports closure is part of the repository, i.e., they are downloaded and evaluated
independently of the root ontology.

5 Accessing the repository

There are currently three different means to access the repository: 1) A web
frontend1 provides access to preconstructed datasets and their descriptions, 2)
an experimental data set creator allows users to create custom datasets based
on a wide range of metrics and 3) an experimental REST-based web service that
allows users to create a dataset using the REST API. Since 2) is based on 3), we
now describe the query language that allows users to create their own datasets
and access the web service.
The query language allows the user to construct statements that represent
filter criteria for ontologies based on some essential metrics such as axiom and
entity counts, or profile membership. It roughly conforms to the following gram-
mar:
q = comp {(”&&”|”||”) comp}
comp = metric (“>=” | “<=” | “=”) n
metric = “axiom count” | “class count” | ...
where “metric” should be a valid metadata element. The query language parser
was built with open-source parser generator Yacc and Lex.
The repository web services are built using the PHP framework Laravel.
Laravel is an advanced framework which implements the REST protocol, so that
users can get access to the services using a REST client, or simply using a web
1
http://mowlrepo.cs.manchester.ac.uk/
Table 1. The REST service parameters.

service url method param return

query /api/ POST query JSON array with fields:status, count,size,
message, progress
check status /api/checkStatus/ GET id JSON array with fields: status, progress
download /api/resource GET id file stream

browser and web-based tools such as Curl. For now, we have implemented three
services: query, checkStatus and download. The query service accepts a query
string that complies to the query language and returns an id string. Afterwards,
users can use the id string to check the status of their query, and to download
the final dataset using checkStatus and download services.
The usage of the services are listed in the Table 1; note that urls should be
appended to mowlrepo.cs.manchester.ac.uk which has been omitted.

6 Next steps
We have presented the Manchester OWL Repository and a range of prototype
interfaces to access pre-constructed datasets and create custom ones. We believe
that the repository will help pushing the quality frontier of empirical ontology-
related research by providing access to shareable, well curated datasets. We
are currently working on the REST services, the dataset creator and improved
dataset descriptions. In the near future, we are aiming to 1) integrate the repos-
itory with Zenodo, a service that allows hosting large datasets that are citable
via DOIs, 2) extend our metadata to capture even more ontology properties (in
particular consistency and coherence) and 3) improving the curation pipeline by
implementing extended yet save fixes for OWL DL profile violations.

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