=Paper=
{{Paper
|id=Vol-1320/paper_31
|storemode=property
|title=Querying Life Science Ontologies with SemFacet
|pdfUrl=https://ceur-ws.org/Vol-1320/paper_31.pdf
|volume=Vol-1320
|dblpUrl=https://dblp.org/rec/conf/swat4ls/GrauKMZZ14
}}
==Querying Life Science Ontologies with SemFacet==
https://ceur-ws.org/Vol-1320/paper_31.pdf
Querying Life Science Ontologies with SemFacet?
Bernardo Cuenca Grau, Evgeny Kharlamov, Šarūnas Marciuška,
Dmitriy Zheleznyakov, and Yujiao Zhou
Department of Computer Science, University of Oxford
first.middle.lastname@cs.ox.ac.uk
Abstract. Faceted search is the de facto query paradigm in e-commerce and it
has been recently adapted for the Semantic Web. In this demonstration we present
our faceted search system SemFacet and show how it can enhance access to RDF
and OWL 2 datasets and OWL 2 ontologies in the domain of life sciences. Sem-
Facet combines keyword and faceted search and it is based on a solid theory,
in particular it employs novel ontology projection techniques to enable faceted
navigation for OWL 2. SemFacet relies on PAGOdA and HermiT for logical rea-
soning and on JRDFox and Sesame for storing and querying RDF triples.
1 Introduction
In the last decade numerous RDF datasets and OWL ontologies in the life sciences
domain have become available [4–6]. Accessing the required information, however, re-
mains a challenging task for end users and often requires proficiency in SPARQL. In
order to make data and ontological knowledge more human accessible numerous query
formulation, data exploration, and browsing tools have been developed. Many such in-
terfaces have beed tailored for specific life science datasets [6, 7]. More generic systems
typically rely on controlled natural language, [8, 9] diagrammatic query constructors
[10, 11], or exploratory search [12].
Faceted search is the de facto query paradigm in e-commerce applications [13]. A
facet typically consists of a property (e.g., ‘gender’ or ‘occupation’ when querying doc-
uments about people) and a set of possible string values (e.g., ‘female’ or ‘research’),
and documents in the collection are annotated with property-value pairs. During faceted
search, users iteratively select facet values and the documents annotated according to
the selection are returned as the search result.
Several authors have proposed faceted search for querying RDF, and a number of
systems have been developed [14–18]. Existing systems, however, have been designed
for plain RDF data, and do not take into account ontological axioms other than sub-
sumption statements between atomic classes and properties [19, 20], with reasoning
playing little or no role. In stark constrast to other domains, life sciences applications
tend to require a great deal of the expressive power available in OWL 2; in particular,
data often involves complex class or property assertions (e.g., see FlyBase [6]) and on-
tologies largely consist of complex axioms which encapsulate highly valuable informa-
tion for faceted search. As a result, existing faceted search systems are not well-suited
for typical life sciences applications.
?
Work supported by the Royal Society, the EPSRC projects Score!, Exoda, and MaSI3 , and the
FP7 project OPTIQUE [1–3] under the grant agreement 318338.
In [21] we developed a faceted search approach for RDF data enhanced with OWL 2
ontologies. Our solution is based on a solid theoretical framework and it addresses
many of the limitations of existing techniques. To put our ideas into practice we de-
veloped SemFacet [21, 22]: a faceted search system that relies on state-of-the-art triple
stores and OWL 2 reasoners to generate and update faceted query interfaces, as well
as for computing search results. For demonstration purposes our platform integrates
JRDFox [23] and Sesame [24] as RDF triple stores, as well as PAGOdA [25] and Her-
miT [26] as fully-fledged OWL 2 reasoners. Our system is fully generic and can be
used to query arbitrary data and ontologies. In this demonstration we will show how
SemFacet can be used to access several datasets and ontologies from the domain of life
sciences and illustrate the main advantages of our approach over existing techniques
designed for plain RDF.
2 The SemFacet System
SemFacet [27] combines keyword search and faceted navigation to query arbitrary
ontology-enhanced RDF datasets. Our system offers the following main functionality.
– Keyword search. Seach in SemFacet typically starts with a set of keywords, which
are matched against the annotations in the ontology and data.
– Faceted interface generation and update. SemFacet implements dedicated infras-
tructure for automatically generating a faceted interface from the result of a key-
word search as well as for updating an interface in response to users’ actions. A
distinguishing aspect of our algorithms for interface generation and update is that
they are ‘guided’ by both explicit and implicit information in the ontology and data
(see [21] for details).
– Query answering. User selections of facet values in an interface are compiled into
SPARQL queries, which are then evaluated against the ontology and data using
a reasoner. Our system allows for both disjunctive facets (i.e., those where mul-
tiple value selections are interpreted disjunctively) and conjunctive facets. Thus,
the SPARQL graph patters relevant to our approach can be captured by the A ND-
U NION fragment of SPARQL 1.1. The current version of SemFacet integrates the
following reasoners: Sesame [24] (a widely used system for RDF(S) reasoning),
JRDFox [23] (a parallel in-memory RDF triple store supporting sound and com-
plete reasoning for OWL 2 RL), HermiT [26] (a standard fully-fledged OWL 2
reasoner), and PAGOdA [25] (a pay-as-you-go reasoner for OWL 2 that combines
JRDFox and HermiT for increased efficiency).
– Refocusing. SemFacet provides functionality for changing the focus of the search
from one type of object to another. For instance, if the system is displaying as search
results neurons that develop from cells, where “develops from” is a facet name and
“cell” is a facet value, we can refocus the search and display as search results the
particular cells that are related to the selected neurons.
– Customisation. Our system is generic and highly customisable for different datasets
and applications. Users can upload arbitrary ontologies and datasets, select the rea-
soner to be exploited for faceted navigation and query answering, customise the
kinds of annotations relevant for keyword search, select which facets should be in-
terpreted disjunctively or conjunctively as well as which facets should be excluded
textual description Keyword Based Faceted Query Answers as GUI
is missing
Search Interface Snippets
textual description
is missing
KBS Facet Query Snippet Backend
Engine Generator Converter Generator
textual description
is missing
textual description
is missing Triple Store:
Data and
Inverted Index Reasoning
e.g. DBpedia Ontology
textual description
is missing Abstracts Data
RDFOX, PAGOdA, Hermit, Sesame
Fig. 1. Left: screenshot of SemFacet over FlyBase OWL 2 data, Right: architecture of SemFacet
from the search process, or select what properties are relevant for image thumbnails
and snippets (if any).
On the left-hand-side of Figure 1 we can see a screenshot of SemFacet with a search
over the Adult Brain Anatomy dataset [4]. The navigation map in the interface enables
refocusing, the filter by section displays the relevant facet names and values, and search
results (i.e., query answers) are displayed on the rightmost part of the interface. The gen-
eral architecture of SemFacet including its main software components is summarised
on the right-hand-side of Figure 1.
3 Demonstration Scenarios
During the demonstration we will show how to explore and query OWL 2 life science
datasets and ontologies with SemFacet. To this end, we will preconfigure the system
for several test cases, including fragments of FlyBase [6], SNOMED CT [5], as well
as a selection of Bio2RDF [4] datasets. In all cases the input for the search will be a
dataset and an ontology. We will demonstrate the following variants of our algorithms
for interface generation and update.
– Data driven, where only the data is exploited for interface generation and update.
This configuration simulates existing approaches to faceted search over RDF.
– Ontology driven, where only the axioms in the ontology are considered. In this
configuration, facet names and values in an interface reflect semantic relationships
between entities in the input ontology.
– Both data and ontology driven, where both the data and ontology are exploited
in interface generation and update. This is the default configuation of SemFacet,
and the aim here is to show how reasoning and ontologies can improve data driven
faceted interfaces and allow for enhanced data exploration.
Besides querying preconfigured scenarios, the demo attendees will be able to try Sem-
Facet end-to-end. This would require to load a data set and ontology, to customise the
system parameters, and to query the uploaded ontology and data with the selected pa-
rameters. For the end-to-end test of SemFacet the demo attendees will be able to use
datasets and ontologies either from the preconfigured scenarios or the ones they provide
(of reasonable size), e.g., by downloading them from the Web.
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