=Paper=
{{Paper
|id=None
|storemode=property
|title=NoHR: Querying EL with Non-monotonic Rules
|pdfUrl=https://ceur-ws.org/Vol-1035/iswc2013_demo_5.pdf
|volume=Vol-1035
|dblpUrl=https://dblp.org/rec/conf/semweb/IvanovKL13
}}
==NoHR: Querying EL with Non-monotonic Rules==
https://ceur-ws.org/Vol-1035/iswc2013_demo_5.pdf
NoHR: Querying EL with Non-monotonic rules
Vadim Ivanov1,2 , Matthias Knorr1 , and João Leite1
1
CENTRIA & Departamento de Informática, Universidade Nova de Lisboa, Portugal
2
Department of Computing Mathematics and Cybernetics, Ufa State Aviation Technical
University, Russia
Abstract. We present NoHR, a Protégé plug-in that allows the user to take an
EL+ ⊥ ontology, add a set of non-monotonic (logic programming) rules – suitable
e.g. to express defaults and exceptions – and query the combined knowledge base.
Provided the given ontology alone is consistent, the system is capable of dealing
with potential inconsistencies between the ontology and the rules, and, after an
initial brief pre-processing period utilizing OWL 2 EL reasoner ELK, returns
answers to queries at an interactive response time by means of XSB Prolog.
1 Introduction
Ontology languages have become widely used to represent and reason over taxonomic
knowledge, and often such knowledge bases are expressed within the language of the
OWL 2 profile OWL 2 EL.1 For example, the clinical health care terminology SNOMED
CT,2 , arguably the most prominent example in the area of medicine and currently used
for electronic health record systems, clinical decision support systems, or remote inten-
sive care monitoring, to name only a few, builds on a fragment of OWL 2 EL and its
underlying description logic (DL) EL++ [2].
Since OWL and its profiles are based on DLs [3], hence monotonic by nature, which
means that once drawn conclusions persist when adopting new additional information,
the ability to model defaults and exceptions with a closed-world view is frequently
requested as a missing feature. For example, in clinical health care terminology, it would
be advantageous to be able to express directly that normally the heart is on the left side
of the body unless the person is a dextrocardiac, which matters when applying ECG or
defibrillation to a patient.
In recent years, there has been a considerable amount of effort devoted to extending
DLs with non-monotonic features – see, e.g., related work in [8] – many of the exist-
ing approaches focusing on combining DLs and non-monotonic rules. The latter are
one of the most well studied formalisms (in the area of Logic Programming) that ad-
mit expressing defaults, exceptions, and also integrity constraints in a declarative way.
As such, they are part of the RIF,3 the other language for the Semantic Web whose
standardization is driven by the W3C.4
1
http://www.w3.org/TR/owl2-profiles/
2
http://www.ihtsdo.org/snomed-ct/
3
http://www.w3.org/TR/rif-overview/
4
http://www.w3.org
Java Virtual Machine XSB
Protégé
NoHR Plugin
ELK XSB
Knowledge
Base
Translator
Protégé Ontology Query
OWL File
Ontology InterProlog Answering
NM Rules NM Rules Query
Base Processor
Tracer/
Debugger
GUI
NM Rules NoHR NoHR
File Rules Tab Query Tab Tables
Fig. 1. System Architecture of NoHR
Here, we focus on Hybrid MKNF under the well-founded semantics [7] combin-
ing ontologies and such rules, because, as argued for the preceding semantics in [8], the
overall framework is very general and flexible, and unlike [8], [7] has a polynomial data
complexity and admits top-down query-answering based only on the information rele-
vant for the query, and without computing the entire model – no doubt a crucial feature
when dealing with large ontologies such as SNOMED with over 300,000 classes.
In our ISWC 2013 Research Track paper [5], we describe a system, realized as a
plug-in for the ontology editor Protégé 4.X,5 that allows the user to query combinations
of EL+ ⊥ ontologies and non-monotonic rules in a top-down manner. To the best of our
knowledge, it is the first Protégé plug-in to integrate non-monotonic rules and top-down
queries. Our approach is theoretically founded on the abstract procedure SLG(O) [1]
and developed upon the usage of the consequence-driven, concurrent EL reasoner ELK
[6] to classify the ontology part, whose result is translated into rules which, together
with the non-monotonic rules, subsequently serve as input for the top-down query en-
gine XSB Prolog.6 Additional features of the plug-in include: the possibility to load
and edit rule bases, and define predicates with arbitrary arity; guaranteed termination
of query answering, with a choice between one/many answers; robustness w.r.t. po-
tential inconsistencies between the ontology and the rules in case the EL+ ⊥ ontology
contains DisjointW ith axioms; leveraging of XSB tabling mechanisms to improve
performance, and trace/debug features, e.g., to provide explanations.
2 System Description
In this Section, we briefly describe the architecture of NoHR, our plug-in for Protégé,
as shown in Fig. 1 and discuss some features of our implementation and querying in
XSB. For the technical details and the evaluation of our approach, we refer to [5].
The input for our plug-in consists of an OWL file, which can be manipulated as
usual in Protégé, and a rule file. For the latter, we provide a tab called NoHR Rules that
5
http://protege.stanford.edu
6
http://xsb.sourceforge.net
Fig. 2. NoHR Query Tab with a query interestingCity(X), onSea(X, Y )
allows the user to load, save and edit rule files in a text panel. The syntax follows Prolog
conventions, so that one rule from Ex. 2 in [5] can be represented, e.g., by
SeaSideCity(X) :- PortCity(X), not NonSeaSideCity(X).
The NoHR Query tab as shown in Fig. 2 also allows for the visualization of the rules
(in the lower left corner), but its main purpose is to provide an interface for querying the
combined KB. Whenever the first query is posed by pushing “Execute”, the translator
is started, initiating the ELK reasoner to classify the ontology and return the result to
the translator. It is verified whether DisjointW ith axioms appear in O which deter-
mines whether the transformation into rules has to contain means to check for potential
inconsistencies or not. Then, accordingly, a joint (non-monotonic) rule set is created in
which predicates and constants, i.e., all terms, are encoded using MD5. This requires
the user to write case-sensitive rules (w.r.t. to the ontology), but ensures full compati-
bility with XSB Prolog’s more restrictive admitted input syntax. The resulting program
is transfered to XSB via InterProlog [4], which is an open-source Java front-end that
provides the ability to communicate between Java and a Prolog engine.
Next, the query can be sent via InterProlog to XSB, and answers are returned to the
query processor, which collects them and sets up a table showing for which variable
substitutions we obtain true, undefined, or inconsistent valuations (or just shows the
truth value for a ground query). The table itself is shown in the Result tab of the Output
panel (see Fig. 2), while the Log tab shows measured times and system messages, in-
cluding those from XSB via InterProlog. XSB not only answers queries very efficiently
in a top-down manner, with tabling, it also avoids infinite loops.
Once the query has been answered, the user may pose other queries, and the sys-
tem will simply send them to XSB directly without any repeated preprocessing. If the
user changes data in the ontology or in the rules, then the system offers the option to
recompile, but always restricted to the part that actually changed.
During the demo exhibition, we take a given/chosen ontology loaded into Protégé,
and we show, first how to edit, load, and save rules, and subsequently, run queries on the
combined knowledge base. In particular, we interactively demonstrate how changing the
ontology and the rules affects the query results, also in the presence of inconsistencies
between the ontology and the rule set. For that purpose, we use data sets of two kinds,
namely toy examples for which the query result can be verified right away and some of
the real world ontologies, utilized already during testing in [5] (cf. Fig.3), to which we
add non-monotonic rules.
Our plug-in is under active development and the most recent version is available at
https://code.google.com/p/nohr-reasoner/. The example file sets for
testing can also be found on this web page.
Acknowledgments. We would like to thank Miguel Calejo for his help with Inter-
Prolog, Pavel Klinov for his help with ELK, Terry Swift for his help with XSB, and
Gonca Güllü for her collaboration. Vadim Ivanov was partially supported by a MUL-
TIC – Erasmus Mundus Action 2 grant. Matthias Knorr and João Leite were par-
tially supported by FCT funded project ERRO – Efficient Reasoning with Rules and
Ontologies (PTDC/EIA-CCO/121823/2010) and Matthias Knorr also by FCT grant
SFRH/BPD/86970/2012.
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