=Paper=
{{Paper
|id=Vol-1387/paper4
|storemode=property
|title=A Survey of Current, Stand-alone OWL Reasoners
|pdfUrl=https://ceur-ws.org/Vol-1387/paper_4.pdf
|volume=Vol-1387
|dblpUrl=https://dblp.org/rec/conf/ore/MatentzogluLHSP15
}}
==A Survey of Current, Stand-alone OWL Reasoners==
https://ceur-ws.org/Vol-1387/paper_4.pdf
A survey of current, stand-alone OWL Reasoners
Nicolas Matentzoglu, Jared Leo, Valentino Hudhra, Bijan Parsia, and Uli
Sattler
The University of Manchester
Oxford Road, Manchester, M13 9PL, UK
{matentzn,jared.leo,hudhrav,bparsia,sattler}@manchester.ac.uk
Abstract. We present a survey of the current OWL reasoner landscape.
Through literature and web search we have identified 35 OWL reasoners
that are, at least to some degree, actively maintained. We conducted
a survey directly addressing the respective developers, and collected 33
responses. We present an analysis of the survey, characterising all rea-
soners across a wide range of categories such as supported expressiveness
and reasoning services. We will also provide some insight about ongoing
research efforts and a rough categorisation of reasoner calculi.
Keywords: ontologies,reasoning,OWL
1 Introduction
The utility of reasoner surveys is twofold: the main purpose is to inform users
about the systems they can potentially choose from, the secondary purpose is to
give an overview of the reasoner landscape, both to reasoner developers and other
related researchers. In this work, we focus on the second purpose. Instead of pro-
viding a detailed list of reasoners, we group them according to our classification
scheme and report on commonly exhibited features. We discuss features such as
logical services, licensing and supported OWL profiles. Rather than consulting
potentially outdated literature, we decided to directly address reasoner devel-
opers to fill in a detailed survey involving questions about parallelism, mobile
support and use of modules.
Our main contributions are (1) an exhaustive survey of stand-alone, current
reasoners supporting reasoning with OWL or a fragment of it, (2) a discussion of
relevant reasoner characteristics and (3) the foundations for a future knowledge
base of OWL implementations.
2 Preliminaries
The reader should have a general familiarity with description logics and reason-
ing. A shallow understanding of description logic reasoning services, semantics
and language expressivity should suffice.
2 Matentzoglu et al.
3 Related work
There are two main types of reasoner surveys: (1) reasoner benchmarks and (2)
reasoner meta-data surveys. Studies of the first type aim at understanding and
comparing reasoner performance and generally involve a more or less systematic
ontology gathering strategy, but will be, in practice, much more unsystematic
with respect to the choice of reasoners to be evaluated. This choice is usually
driven by practical considerations, such as the implementation of a particular
interface (OWL API, ORE) and intuitions about the importance of the reasoners.
For example, we might believe that Pellet, HermiT, ELK and FaCT++ are more
important than other reasoners, because we have the impression that they are
used more often, cited more often, or simply bundled with Protégé. Reasoner
benchmarks attempt to establish the utility of a particular reasoning system for a
particular set of cases, or test the effect of particular optimisations. The second
type of surveys are reasoner meta-data surveys. Their aim is to list existing
systems, provide an overview of the supported features and, ideally, organise the
landscape. This work is of the second kind.
While reasoner benchmarks have recently received increasing attention, for
example in the ORE reasoner competition [13], exhaustive meta-data surveys
and reasoner listings are sparse and rarely comprehensive or up-to-date [29, 39].
The most comprehensive relatively recent study [22] provides an historical ac-
count of description logic reasoners from the early implementations (1975) until
modern OWL reasoners such as HermiT. The authors analysed each reasoner
with respect to four basic aspects: (1) Inference support (reasoning services,
reasoner type), (2) basic components (supported languages, other features), (3)
algorithm completeness and (4) programming languages. The accounts are rel-
atively unstructured and do not lend themselves to automated comparisons,
aggregations and as a basis for reasoner selections for users. The survey is sup-
posed to provide a brief historical introduction into the subject matter, and
constituted one of the starting points of our own investigation. Another study
focuses on reasoners that the author deemed suitable for either Protégé or the
NeOn toolkit [1]. The survey does describe some axes along which reasoners
could be classified, and provides a comparative account across features such
as expressivity, OWL API support or the ability to perform incremental clas-
sification. However, the study does not go into much detail, is not exhaustive
(many relevant reasoners like JFact, jcel and Konclude are missing), and from
a methodological perspective, there is a lack of explanation on how the data
was obtained and validated. A nice study, focusing on reasoners that are able to
deal with large EL ontologies, lists 8 reasoners and compares them in terms of
supported expressivity, soundness, completeness, rule support and a few other
features [7]. This study is a hybrid between a benchmark and a meta-data study,
as it not only compares reasoner features, but also evaluates their performance
against a handful of important biomedical ontologies. Its main contribution is
the definition of relevant characteristics that guide reasoner choice of users. Our
survey covers most of the dimensions they describe, except for rules and details
with respect to ABox reasoning tasks.
OWL Reasoners Survey 3
4 OWL Reasoner Characteristics
Not all reasoners are intended to support the entirety of OWL 2 DL. Reasoners
such as HermiT and FaCT++ support all of OWL 2 DL, while many efficient
reasoners exist that only deal with tractable profiles of OWL and OWL 2; ELK
and CEL are examples of highly efficient reasoners that handle (most of) OWL 2
EL. There are also many reasoners that deal with extensions of classical DLs,
such as fuzzyDL, a reasoner for fuzzy extensions to DLs. Amongst this variation
are the reasoning services that the reasoners offer; some are very efficient at
TBox queries such as classification (computing subsumption relations between
named classes), whereas others are meant for query answering. From a utilitar-
ian perspective, we believe that supported reasoning services, expressivity levels
(including the degree of datatype support) and the completeness of the imple-
mented algorithm are the most important characteristics to categorise a reasoner.
A second way to classify the reasoner is by the primary calculus underlying its
core reasoning service, for example tableau or consequence-based procedures.
This distinction is primarily useful for researchers that are interested in finding
more efficient ways to solve reasoning problems.
4.1 Utility
Modern OWL reasoners support a number of primary logical services such as
consistency, classification, instance checking and entailment checking, and sec-
ondary services such as explanation for entailments and inconsistency, (semantic)
module extraction and ontology based data access (OBDA). Explaining these
services in detail is beyond the scope of this survey. It should suffice to say that
some reasoners are particularly optimised to operate on concept level (TBox)
knowledge, for example offering an efficient classification service, while other
reasoners offer the user services for efficient instance retrieval (ABox level), most
notably through conjunctive query answering. In our work, we attempted to de-
termine key and ancillary reasoning services for all reasoners participating in our
survey.
We define the expressivity of a language as the quality and conceptual breadth
of the constructs which make up the language. The expressivity supported by a
reasoner is the most expressive language for which it can provide a key reasoning
service. As such, expressivity is not an attribute of the reasoner, but of a reason-
ing service: a reasoner might offer different sound reasoning services for varying
levels of expressivity. There are many ways expressivity can be documented and
communicated, for example: (1) The most expressive description logic fragment
supported (such as ALC or SROIQ), (2) the set of OWL profiles supported and
(3) the set of OWL profiles efficiently supported. To simplify things: (1) is usu-
ally communicated as a single language, however, since the expressivity levels of
all known DL fragments can be organised into a partial order, the expressivity of
a reasoner refers to a set of languages, (2) is usually used to indicate the expres-
sivity of a reasoner based on the OWL profiles it is compatible with and (3) is an
extension of (2) where efficiency is the main focus. As an example, consider the
2 reasoners HermiT and ELK. W.r.t (1), HermiT captures the set of DLs below
4 Matentzoglu et al.
and including SROIQ(D) whereas ELK similarly captures (ELRO). W.r.t (2),
the expressivity of HermiT is higher than ELK since HermiT captures OWL 2
DL and ELK only captures OWL 2 EL and finally, w.r.t (3) it is known that
ELK performs better than HermiT w.r.t OWL 2 EL ontologies. In our survey,
we obtained all three kinds of measurements for expressivity, but we will only
report on (1) and (2). For the reasoner classification, we only consider known
fragments of DL and standard OWL profiles respectively. As noted above, many
reasoners are implemented to support extensions to these languages or profiles,
for example probabilistic, temporal or fuzzy extensions. Although these differ
considerably, they still use common calculi and are categorised accordingly.
In general, a sound algorithm will never answer Y ES if N O would be true
and a complete algorithm will never answer N O if Y ES would be true. For
example, given an algorithm P , an ontology O, and an axiom α over O of the
form A v B where O |= α, P (O, α) says YES iff O |= α, where soundness is the
→ direction and completeness is the ← direction. Both can be seen as important
and required characteristics of a reasoner. To show why, a trivial implementation
of a sound and incomplete algorithm answers N O to every question, where as
a trivial implementation of a unsound but complete algorithm answers Y ES to
every question - both can be seen as irrelevant if not taken together. While it
would make sense to classify each reasoning service individually on whether the
underlying algorithms are sound and complete, we simplify our measurement to
determine what the algorithm underlying the key reasoning service is. In our
survey, we distinguish broad categories of completeness, but assume that all
procedures are generally sound.
4.2 Calculus
We identified 3 main categories of calculi: consequence-, model construction- and
rewriting-based. In general, consequence-based reasoners rely on adding logical
consequences (entailments) to a knowledge base (KB) without the need to check
possible consequences that are not entailed by the knowledge base. This cate-
gory covers similar techniques such as resolution-based techniques, rule-based
procedures and completing algorithms. These techniques are most commonly
employed for reasoners that deal only with the fragments of OWL 2 DL such
as EL+ and EL++, and are used by some popular reasoners such as ELK and
CEL. Model construction techniques are based on building models based on the
KB and checking for KB consistency. These are generally utilised for quite highly
expressive fragments of OWL 2 DL, i.e. those extending ALC. This category in-
cludes those reasoners that use automata-based approaches, tableau and hyper-
tableau techniques. Rewriting is a technique used to expand a KB by rewriting
the facts of the KB, to be used for a specific task, e.g. query answering, mak-
ing the reasoner less reliant on the terminological aspect and more involved in
the data aspect. This technique is most commonly used for query rewriting and
catalogue rewriting but can also be used for structural transformation of a KB.
While we attempted to determine which calculus was primarily employed
by the reasoner developers, there seems to be a trend, at least for the general
OWL Reasoners Survey 5
purpose reasoners that cover the entirety of OWL 2 DL reasoning, to employ
hybrid techniques, for example combining saturation-based or consequence-based
techniques with tableau-style techniques (Pellet, Konclude, WSClassifier and
others).
5 Materials and Methods
The subjects of this survey are current, stand-alone reasoners that can deal
with OWL. We defined current as either being updated or published over since
January 2012. Being able to deal with OWL means that at least one of the stan-
dard reasoning services above was supported on a defined fragment of the OWL.
Stand-alone means that the reasoner is meant to be used by itself, rather than
being a dependent component of a bigger system, such as the various inference
engines as part of triple stores.
This survey emerged as a by-product of a systematic review on DL reason-
ing system evaluation quality. It is however by itself not strictly systematic.
The initial list of reasoners was created from publicly maintained lists [29, 39].
More reasoners were found through respective Google searches. This knowledge
directly informed the search strategy for the systematic review mentioned be-
fore, which again yielded a large range of system description papers that lead
to amending the full list of reasoners. After the search was completed, our list
included 68 description logic and OWL reasoners, with another two being added
later as part of a feedback round from reasoner developers.
As a first step, we attempted to find evidence of use of each reasoner by
searching Google and Google Scholar for web pages that led to version con-
trol systems or other kinds of download pages as well as academic papers. We
recorded the most up to date evidence we could find (for example, the lat-
est commit in a version control, the publication date of the most recent pa-
per). If a reasonable attempt at finding evidence failed (repeated searches with
varying keyword combinations, references in perhaps older system description
papers), we excluded the reasoner from the survey. Apart from the 35 reason-
ers we mention in this survey, the original list contained the following reason-
ers: COROR, RacerPro, *SAT, BACK, CB, Cerebra Engine, CICLOP, CLAS-
SIC, Condor, CRACK, DLP, Fact, FLEX, HAM-ALC, K-REP, Bossam, KRIS,
LOOM, MSPASS, QuOnto, SHER, YAK, OWLGres, Pronto, DLEJena, F-OWL,
Fresg, OWLer, OntoMinD, Screech, REQUIEM, YARR!, Kaon2, Elly and Soft-
Facts. Most of these reasoners are quite old description logic reasoners, some
reasoners, such as RacerPro, are superseded by other reasoners (Racer). For
none of these reasoners we were able to find proof that they were used or pub-
lished about since January 2012. The YARR! reasoner did have a workshop
paper in 2013 [30], but we decided to exclude it from the survey because we
were unable to find a more substantial proof of existence (like a web page). We
sent our survey to the developers of the remaining 35 reasoners.
The survey itself was implemented as a series of questions in SurveyMonkey1 ,
as a joint effort of a team of experts including reasoner developers, logicians and
1
https://www.surveymonkey.com/r/6F9K89X
6 Matentzoglu et al.
empirical researchers. The results were used to update the popular reasoner
listing2 at Manchester.
6 Survey Results and Discussion
Out of the 35 surveys sent out to reasoner developers, we collected 33 complete
answers. Surveys for OwlOntDB [10] and Deslog [40] were not completed at
the time of this writing. An overview of all participating systems can be found
in Table 1 (see appendix). As discussed in the introduction, we do not aim to
provide a discussion of the individual reasoners. Additional information can be
found on the Manchester University pages mentioned in the previous section.
6.1 General information
As can be seen in Figure 1, the majority of reasoners were developed in the
last five years (23 out of 33). However, 3 reasoners have been around for more
than ten years, all of which have been updated during the last two years. 23 are
actively developed, 8 are merely maintained (bugfixes etc.) and two reasoners,
CEL and HermiT, are not maintained at all anymore. In terms of implemen-
tation, most reasoners are based on the Java programming language. This is
not surprising, given that the dominant frameworks supporting OWL, such as
the OWL API [16] and Jena, are implemented in Java. As can be seen in Fig-
ure 2 there are, interestingly enough, a good number or reasoners implemented
in other languages though, such as the more efficient C/C++ or Prolog. More
than two thirds of the reasoners in the set implement the OWLReasoner inter-
face of the OWL API. Like other questions in the survey this one was optional,
thus merely establishing a lower bound, but it already demonstrates the ubiq-
uity of the framework. The majority of reasoners are accessible via the command
line, or are readily available for integration with the Protégé editor, also Fig-
ure 2. The dominant underlying primary calculus is tableau (10 out of 33), but
only just. It is inherently difficult to classify modern reasoners accurately. Kon-
clude for example uses a hybrid approach that involves tableau and some nested
saturation-based techniques. Many full fledged OWL 2 reasoners come with an
efficient consequence-based delegate reasoner for EL ontologies, such as Pellet,
FaCT++ or WSClassifier. For this survey, we grouped the different techniques
into the categories described in Section 4.2. As can be see in Figure 2, most
reasoners are primarily based on an approach that involves model construction.
Few reasoner developers report to make use of modularity, perhaps sur-
prisingly. Only four reasoners, including full fledged modular reasoners such as
MORe and Chainsaw, report to make use of modularity for classification, and
only three reasoners (Pellet, FaCT++ and DistEL) are reported to make use of
modularity for incremental reasoning. Only 6 reasoners are known to draw on
parallelism to improve classification performance, and only 4 mobilise it during
pre-processing. Up until today, little is known whether parallel techniques like
2
http://owl.cs.manchester.ac.uk/tools/list-of-reasoners/
OWL Reasoners Survey 7
2015
2010
Years Last Update
2005 Prototype Launch
2000
0 10 20 30
Reasoners (ordered by prototype launch date)
Fig. 1. Timeline of OWL reasoners.
parallel or-branching or parallel module classification actually do much good,
especially for tableau-style reasoning. Three reasoners, CEL, snorocket and WS-
Classifier have been reported to be tuned to particular ontologies (SNOMED
CT the two former, a version of FMA the latter). Given the growing interest for
mobile technology, we also asked whether the reasoner has been run on a mobile
device. 5 reasoners where run on Android (ELK, JFact, jcel, LIFr, BaseVIsor),
24 where never run on a mobile device and for 4 reasoners the developers did
not give an answer.
20
15 10
20
8
10
6
5 10 4
2
0
e a r I k e 0
Lin Je
n
Othe LA
P
LLin ote
g
.
a nd OW OW Pr CB Co lar itin
g
C + va P g n l du wr
mm C+ Ja LIS olo tho de Mo Re
Co Pr Py Mo
Interfaces Programming Lang. Calculus
Fig. 2. Main programming language the reasoner is written in, interfaces provided and
the main category of calculi underpinning the reasoner.
6.2 Expressivity and services
Some reasoners do not merely provide reasoning services for standard descrip-
tion logics. A minimal support for the most important extension, datatypes, is
provided by many reasoners. In our survey however, 16 reasoner developers did
not give any indication for datatype support. Full support of all datatypes on
the OWL 2 datatype map is reported for 6 reasoners. The question was optional,
however, so the numbers have to be interpreted cautiously. Other prominent ex-
tensions are fuzzy with 3 reasoners, probabilistic with 4 and distributed reasoning
with 2 reasoners in the survey. Multiple extensions to standard description logic
(for example distributed-fuzzy) reasoning do not seem to occur at all.
As was discussed in Section 4.1, we will report the supported language expres-
sivity in two ways: Supported OWL profile and the main underlying description
logic family. Both can be seen in Figure 3. For readability, we have grouped the
DL languages into 3 rough categories: Horn-style languages includes EL, RL,
Horn-SHIQ and similar. Horn logics are those that provide no means to express
8 Matentzoglu et al.
any form of disjunction, either directly or indirectly. They do not require any
form of “reasoning by case”, often have nice canonical models, and are generally
suited for consequence-based reasoning. To the right of the figure, we summarize
the degree of completeness of the underlying algorithms. SC stands for sound-
ness/completeness, SI stands for sound and incomplete, SC/Profile means that
the reasoner is sound and complete for one of the profiles, SC/OWL1 sound and
complete for OWL 1 and so on. Only six reasoners in the set are incomplete,
and the majority of reasoners are complete at least for one of the polytime OWL
profiles. This is also consistent with the supported profiles: The majority of rea-
soners support OWL 2 EL, and at least 12 reasoners are directly recommended
for the OWL 2 EL profile (see questionnaire).
16 14
14 12
15 12 10
10
10 8
8
6
6
5 4 4
2 2
0
DL EL QL RL DL Lit
e 0 rn rn of 1 L2 SI
2 2 2 2 L L AC Ho Ho /Pr WL OW
L L L L OW OW n− SC /O /
OW OW OW OW No SC SC
Profiles Expressivity Cat. Soundness/Completeness
Fig. 3. Supported levels of expressivity and completeness.
Most reasoners support simple satisfiability checking (22 out of 33), knowl-
edge base consistency (24), entailments checking (19) and classification (21).
Fewer reasoners are asserted to support ABox related tasks, such as realisation
(15) and conjunctive query answering (11).
6.3 Ongoing Research and future directions
Beyond the obvious need for improved evaluations and support for increased
expressive languages, many current developments focus on optimisations related
to parallelism and concurrency. At least 6 reasoner developers are working to
integrate or improve some aspect of the reasoning with parallel techniques. An-
other working topic at the moment appears to be incremental reasoning. 4 rea-
soner developers report to be actively working on integrating support for in-
cremental reasoning. Other topics of interest include, but are not limited to:
Reasoning with large scale datasets, meta-modeling and meta-reasoning, condi-
tional completeness-guaranteed of approximation results, proof-based explana-
tions, integration of a novel approach to module extraction and support of extra
interfaces to allow client code to access the tableaux structure.
7 Conclusions and Future Work
We hope that our work will inform reasoner developers of other relevant projects
in their field, and support them to get a sense of the coverage and directions of
current reasoner development trends. We tried to get an idea of (intended) use for
OWL Reasoners Survey 9
concurrent or parallel technologies, as well as modules. The survey we conducted
covers many more aspects than we could report on within the limits of this paper.
We asked for supported datatypes individually, employed reasoner optimisations
and recommended usage. The resulting data will form the starting point for a
knowledge base about reasoning systems and ontologies. We have already started
working on an OWL reasoner ontology (ORO) that attempts to model language
expressivity, calculi and reasoner optimisations in a fine grained fashion, to serve
as a schema for the knowledge base. ORO is directly informed by the outcomes
of this survey and is, in its very prototypical first version, available online3 .
References
1. S. Abburu. A Survey on Ontology Reasoners and Comparison. International
Journal of Computer Applications, 57(17):33–39, Nov. 2012. Full text available.
2. F. Baader, C. Lutz, and B. Suntisrivaraporn. CEL - A Polynomial-Time Reasoner
for Life Science Ontologies. In Automated Reasoning, Third International Joint
Conference, IJCAR 2006, Seattle, WA, USA, August 17-20, 2006, Proceedings,
pages 287–291, 2006.
3. J. Bak, M. Nowak, and C. Jedrzejek. RuQAR: Reasoning Framework for OWL
2 RL Ontologies. In The Semantic Web: ESWC 2014 Satellite Events - ESWC
2014 Satellite Events, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected
Papers, pages 195–198, 2014.
4. F. Bobillo, M. Delgado, and J. Gmez-Romero. DeLorean: A reasoner for fuzzy
OWL 2. Expert Syst. Appl., 39(1):258–272, 2012.
5. F. Bobillo and U. Straccia. fuzzyDL: An expressive fuzzy description logic reasoner.
In FUZZ-IEEE 2008, IEEE International Conference on Fuzzy Systems, Hong
Kong, China, 1-6 June, 2008, Proceedings, pages 923–930, 2008.
6. D. Calvanese, G. D. Giacomo, D. Lembo, M. Lenzerini, A. Poggi, M. Rodriguez-
Muro, R. Rosati, M. Ruzzi, and D. F. Savo. The MASTRO system for ontology-
based data access. Semantic Web, 2(1):43–53, 2011.
7. K. Dentler, R. Cornet, A. t. Teije, and N. d. Keizer. Comparison of reasoners for
large ontologies in the OWL 2 EL profile. Semantic Web, 2(2):71–87, 2011.
8. C. L. Duc, M. Lamolle, A. Zimmermann, and O. Cur. DRAOn: A Distributed
Reasoner for Aligned Ontologies. In Informal Proceedings of the 2nd International
Workshop on OWL Reasoner Evaluation (ORE-2013), Ulm, Germany, July 22,
2013, pages 81–86, 2013.
9. T. Eiter, M. Ortiz, M. Simkus, T.-K. Tran, and G. Xiao. Query Rewriting for
Horn-SHIQ Plus Rules. In Proceedings of the Twenty-Sixth AAAI Conference on
Artificial Intelligence, July 22-26, 2012, Toronto, Ontario, Canada., 2012.
10. R. U. Faruqui and W. MacCaull. O wl O nt DB: A Scalable Reasoning System for
OWL 2 RL Ontologies with Large ABoxes. In Foundations of Health Information
Engineering and Systems - Second International Symposium, FHIES 2012, Paris,
France, August 27-28, 2012. Revised Selected Papers, pages 105–123, 2012.
11. M. d. M. R. Garca and J. F. A. Montes. Evaluating DBOWL: A Non-materializing
OWL Reasoner based on Relational Database Technology. In Proceedings of the 1st
International Workshop on OWL Reasoner Evaluation (ORE-2012), Manchester,
UK, July 1st, 2012, 2012.
3
https://github.com/matentzn/owlreasonerontology
10 Matentzoglu et al.
12. B. Glimm, I. Horrocks, B. Motik, G. Stoilos, and Z. Wang. HermiT: An OWL 2
Reasoner. J. Autom. Reasoning, 53(3):245–269, 2014.
13. R. S. Gonalves, S. Bail, E. Jimnez-Ruiz, N. Matentzoglu, B. Parsia, B. Glimm,
and Y. Kazakov. OWL Reasoner Evaluation (ORE) Workshop 2013 Results: Short
Report. In Informal Proceedings of the 2nd International Workshop on OWL Rea-
soner Evaluation (ORE-2013), Ulm, Germany, July 22, 2013, pages 1–18, 2013.
14. A. V. Grigorev and A. G. Ivashko. TReasoner: System Description. In Infor-
mal Proceedings of the 2nd International Workshop on OWL Reasoner Evaluation
(ORE-2013), Ulm, Germany, July 22, 2013, pages 26–31, 2013.
15. V. Haarslev, K. Hidde, R. Mller, and M. Wessel. The RacerPro knowledge repre-
sentation and reasoning system. Semantic Web, 3(3):267–277, 2012.
16. M. Horridge and S. Bechhofer. The OWL API: A Java API for OWL ontologies.
Semantic Web, 2(1):11–21, 2011.
17. Y. Kazakov, M. Krtzsch, and F. Simancik. The Incredible ELK - From Polyno-
mial Procedures to Efficient Reasoning with EL Ontologies. J. Autom. Reasoning,
53(1):1–61, 2014.
18. R. Kontchakov, M. Rezk, M. Rodriguez-Muro, G. Xiao, and M. Zakharyaschev.
Answering SPARQL Queries over Databases under OWL 2 QL Entailment Regime.
In The Semantic Web - ISWC 2014 - 13th International Semantic Web Conference,
Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part I, pages 552–567,
2014.
19. C. J. Matheus, K. Baclawski, and M. M. Kokar. BaseVISor: A Triples-Based Infer-
ence Engine Outfitted to Process RuleML and R-Entailment Rules. In Rules and
Rule Markup Languages for the Semantic Web, Second International Conference,
RuleML 2006, Athens, Georgia, USA, November 10-11, 2006, Proceedings, pages
67–74, 2006.
20. J. Mendez. jcel: A Modular Rule-based Reasoner. In Proceedings of the 1st Inter-
national Workshop on OWL Reasoner Evaluation (ORE-2012), Manchester, UK,
July 1st, 2012, 2012.
21. A. Metke-Jimenez and M. Lawley. Snorocket 2.0: Concrete Domains and Con-
current Classification. In Informal Proceedings of the 2nd International Workshop
on OWL Reasoner Evaluation (ORE-2013), Ulm, Germany, July 22, 2013, pages
32–38, 2013.
22. R. B. Mishra and S. Kumar. Semantic web reasoners and languages. Artif. Intell.
Rev., 35(4):339–368, 2011.
23. B. Motik, Y. Nenov, R. Piro, I. Horrocks, and D. Olteanu. Parallel Materialisation
of Datalog Programs in Centralised, Main-Memory RDF Systems. In Proceedings of
the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27 -31, 2014,
Qubec City, Qubec, Canada., pages 129–137, 2014.
24. R. Mutharaju, P. Hitzler, P. Mateti, and F. Lcu. Distributed and Scalable OWL
EL Reasoning. In Proceedings of the 12th Extended Semantic Web Conference,
Portoroz, Slovenia, To Appear, 2015.
25. M. Niepert, J. Noessner, and H. Stuckenschmidt. Log-Linear Description Logics. In
IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial
Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011, pages 2153–2158, 2011.
26. I. Palmisano. JFact repository, 2015.
27. F. Riguzzi, E. Bellodi, E. Lamma, and R. Zese. BUNDLE: A Reasoner for Proba-
bilistic Ontologies. In Web Reasoning and Rule Systems - 7th International Con-
ference, RR 2013, Mannheim, Germany, July 27-29, 2013. Proceedings, pages 183–
197, 2013.
OWL Reasoners Survey 11
28. A. A. Romero, B. C. Grau, and I. Horrocks. MORe: Modular Combination of
OWL Reasoners for Ontology Classification. In The Semantic Web - ISWC 2012 -
11th International Semantic Web Conference, Boston, MA, USA, November 11-15,
2012, Proceedings, Part I, pages 1–16, 2012.
29. U. Sattler and N. Matentzoglu. List of Reasoners (owl.cs). Modified: 01/09/2014.
30. J. Schoenfisch and J. Ortmann. Yarr!: Yet another rewriting reasoner. In Infor-
mal Proceedings of the 2nd International Workshop on OWL Reasoner Evaluation
(ORE-2013), Ulm, Germany, July 22, 2013, pages 19–25, 2013.
31. B. Sertkaya. The ELepHant Reasoner System Description. In Informal Proceedings
of the 2nd International Workshop on OWL Reasoner Evaluation (ORE-2013),
Ulm, Germany, July 22, 2013, pages 87–93, 2013.
32. E. Sirin, B. Parsia, B. C. Grau, A. Kalyanpur, and Y. Katz. Pellet: A practical
OWL-DL reasoner. J. Web Sem., 5(2):51–53, 2007.
33. W. Song, B. Spencer, and W. Du. WSReasoner: A Prototype Hybrid Reasoner for
ALCHOI Ontology Classification using a Weakening and Strengthening Approach.
In Proceedings of the 1st International Workshop on OWL Reasoner Evaluation
(ORE-2012), Manchester, UK, July 1st, 2012, 2012.
34. A. Steigmiller, T. Liebig, and B. Glimm. Konclude: System description. J. Web
Sem., 27:78–85, 2014.
35. E. Thomas, J. Z. Pan, and Y. Ren. TrOWL: Tractable OWL 2 Reasoning Infras-
tructure. In The Semantic Web: Research and Applications, 7th Extended Semantic
Web Conference, ESWC 2010, Heraklion, Crete, Greece, May 30 - June 3, 2010,
Proceedings, Part II, pages 431–435, 2010.
36. D. Tsarkov and I. Horrocks. FaCT++ Description Logic Reasoner: System De-
scription. In Automated Reasoning, Third International Joint Conference, IJCAR
2006, Seattle, WA, USA, August 17-20, 2006, Proceedings, pages 292–297, 2006.
37. D. Tsarkov and I. Palmisano. Chainsaw: a Metareasoner for Large Ontologies.
In Proceedings of the 1st International Workshop on OWL Reasoner Evaluation
(ORE-2012), Manchester, UK, July 1st, 2012, 2012.
38. D. Tsatsou, S. Dasiopoulou, I. Kompatsiaris, and V. Mezaris. LiFR: A Lightweight
Fuzzy DL Reasoner. In The Semantic Web: ESWC 2014 Satellite Events - ESWC
2014 Satellite Events, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected
Papers, pages 263–267, 2014.
39. W3C. OWL/Implementations. Modified: 11 December 2013.
40. K. Wu and V. Haarslev. A Parallel Reasoner for the Description Logic ALC. In
Proceedings of the 2012 International Workshop on Description Logics, DL-2012,
Rome, Italy, June 7-10, 2012, 2012.
41. G. Xiao and T. Eiter. Inline Evaluation of Hybrid Knowledge Bases. In Web
Reasoning and Rule Systems - 5th International Conference, RR 2011, Galway,
Ireland, August 29-30, 2011. Proceedings, pages 300–305, 2011.
42. R. Zese, E. Bellodi, E. Lamma, and F. Riguzzi. A Description Logics Tableau
Reasoner in Prolog. In Proceedings of the 28th Italian Conference on Computational
Logic, Catania, Italy, September 25-27, 2013., pages 33–47, 2013.
43. R. Zese, E. Bellodi, E. Lamma, F. Riguzzi, and F. Aguiari. Semantics and Inference
for Probabilistic Description Logics. In Uncertainty Reasoning for the Semantic
Web III - ISWC International Workshops, URSW 2011-2013, Revised Selected
Papers, pages 79–99, 2014.
12 Matentzoglu et al.
Table 1. Overview of the participating reasoning systems. SC stands for sound-
ness/completeness, P for profile, O1 for OWL 1 and O2 for OWL 2. CALC is the
main underlying calculus, EXP the highest expressive language supported. ACT indi-
cates whether there is active development (B=Bugfixes, D=Active development, N=No
development)
Name Institution SC ACT CALC EXP
BaseVISor[19] VIStology, Inc. P B Rete Network NA
BUNDLE[27] Univ. of Ferrara O2 D Tableaux SROIQ
CEL[2] Technische Universitt Dres- P N Consequence-based EL+
den
Chainsaw[37] Univ. of Manchester O2 D Modular Reasoner SROIQ
Clipper[9] Vienna Univ. of Technology P B Query Rewriting Horn-SHIQ
DBOWL[11] Univ. of Malaga O1 D Relational Alge- SHOIN
bra and fixed-point
iterations
DeLorean[4] Not given O2 D Fuzzy NA
DistEL[24] Wright State Univ. P D Consequence-based NA
DRAOn[8] Univ. of Paris 8, IUT of O1 D Compressed models NA
Montreuil
DReW[41] Vienna Univ. of Technology P B Datalog Rewriting EL++
ELepHant[31] Not given I D Consequence-based EL++
ELK[17] Univ. of Ulm, Germany I D Consequence-based EL+
ELOG[25] Not given P B Integer Linear Pro- NA
gramming
FaCT++[36] Univ. of Manchester O2 D Tableaux SROIQ
fuzzyDL[5] ISTI - CNR I D Tableaux SHIF
HermiT[12] Univ. of Oxford O2 N Hypertableaux SROIQ
jcel[20] Technische Universitt Dres- P B Consequence-based EL+
den
JFact[26] Univ. of Manchester I D Tableaux SROIQ
Konclude[34] Univ. of Ulm, derivo GmbH O2 D Hybrid SROIQV
LiFR[38] Centre for Research P D Hypertableaux OWL DLP
and Technology Hellas
(CERTH)
Mastro[6] Sapienza Univ. of Rome P D Query Rewriting DL-LiteA
MORe[28] Univ. of Oxford O2 D Modular Reasoner SROIQ
ontop[18] Free Univ. of Bozen-Bolzano P D Query Rewriting OWL 2 QL
Pellet[32] Clark & Parsia, LLC O2 D Tableaux SROIQ
Racer[15] Concordia Univ., Montreal, P B Tableaux SRIQ
Canada; Univ. of Luebeck,
Germany;
RDFox[23] Univ. of Oxford P D Datalog Rewriting OWL 2 RL
RuQAR[3] Poznan Univ. of Technology P D Datalog Rewriting
Snorocket[21] CSIRO I D Consequence-based EL++
TReasoner[14] Tyumen State Univ. O2 B Tableaux SROIQ
TRILL[42] Univ. of Ferrara O1 D Tableaux SHOQ
TRILLP[43] Univ. of Ferrara O1 D Tableaux ALC
TrOWL[35] Univ. of Aberdeen P D Consequence-based SROIQ
WSClassifier[33] Univ. of New Brunswick, I B Hybrid ALCHOI
Canada