=Paper=
{{Paper
|id=Vol-1081/invited2
|storemode=property
|title=Combining ontologies in settings with multiple agents
|pdfUrl=https://ceur-ws.org/Vol-1081/womo2013_invited_paper_2.pdf
|volume=Vol-1081
|dblpUrl=https://dblp.org/rec/conf/womo/Vouros13
}}
==Combining ontologies in settings with multiple agents==
https://ceur-ws.org/Vol-1081/womo2013_invited_paper_2.pdf
Combining Ontologies in
Settings with Multiple Agents
George A. Vouros
Department of Digital Systems,
University of Piraeus, Greece
georgev@unipi.gr
Abstract. Combining knowledge and beliefs of autonomous peers in
distributed settings, is a major challenge. In this talk we consider agents
that combine their ontologies and reason jointly with their coupled knowl-
edge using the E-SHIQ representation framework. We motivate the need
for a representation framework that allows agents to combine their knowl-
edge in different ways, maintaining the subjectivity of their own knowl-
edge and beliefs, and to reason collaboratively, constructing a tableau
that is distributed among them. The talk presents the E − SHIQ repre-
sentation framework and the tableau reasoning algorithm. It presents the
implications to the modularization of ontologies for efficient reasoning,
implications to coordinating agents’ subjective beliefs, as well as chal-
lenges for reasoning with ontologies in open and dynamic multi-agent
systems.
1 Combining Ontologies with E − SHIQ
To combine knowledge and beliefs of autonomous agents in open and inher-
ently distributed settings, we need special formalisms that take into account
the complementarity and heterogeneity of knowledge in multiple interconnected
contexts. Agents may have different, subjective beliefs concerning “bridging”
heterogeneity and coupling their knowledge with the knowledge of others. The
subjectivity of beliefs plays an important role in such a setting, as agents may
inherently (i.e. due to restrictions of their task environment) have different views
of the knowledge possessed by others, or they may not agree on the way they
may jointly shape knowledge.
On the other hand, large ontologies need to be dismantled so as to be evolved,
engineered and used effectively during reasoning. The process of taking an ontol-
ogy to possibly interdependent ontology units is called ontology modularization,
and specifically, ontology partitioning. Each such unit, or module, provides a
specific context for performing ontology maintenance, evolution and reasoning
tasks, at scales and complexity that are smaller than that of the initial ontol-
ogy. Therefore, in open and inherently distributed settings (for performing either
ontology maintenance, evolution or reasoning tasks), several such ontology mod-
ules may co-exist in connection with each other. Formally, it is required that
any axiom that is expressed using terms in the signature of a module and it is
entailed by the ontology must be entailed by the module, and vise-versa. The
partitioning task requires that the union of all the modules, together with the
set of correspondences/relations between modules, is semantically equivalent to
the original ontology. This later property imposes hard restrictions to the mod-
ularization task: Indeed, to maintain it, a method must do this with respect to
the expressiveness of the language used for specifying correspondences/relations
between modules’ elements, to the local (per ontology module) interpretation
of constructs, and to the restrictions imposed by the setting where modules are
deployed.
The expressivity of knowledge representation frameworks for combining knowl-
edge in multiple contexts, and the efficiency of distributed reasoning processes,
depend on the language(s) used for expressing local knowledge and on the lan-
guage used for connecting different contexts.
While our main goal is to provide a rich representation framework for com-
bining and reasoning with distinct ontology units in open, heterogeneous and
inherently distributed settings, we propose the E − SHIQ representation frame-
work and a distributed tableau algorithm [1] [2].
The representation framework E − SHIQ:
– Supports subjective concept-to-concept correspondences between concepts
in different ontology units.
– In conjunction to subjective concept-to-concept correspondences, E −SHIQ
supports relating individuals in different units via link relations, as well
as via subjective individual correspondence relations. While correspondence
relations represent equalities between individuals, from the subjective point
of view of a specific unit, link relations may relate individuals in different
units via domain-specific relations.
– Supports distributed reasoning by combining local reasoning chunks in a
peer-to-peer fashion. Each reasoning peer with a specific ontology unit holds
a part of a distributed tableau, which corresponds to a distributed model.
– Finally, E − SHIQ inherently supports subsumption propagation between
ontologies, supporting reasoning with concept-to-concept correspondences in
conjunction to link relations between ontologies.
2 Constructing E − SHIQ distributed knowledge bases
via modularization
To distribute knowledge among different agents, we need to partition monolithic
ontologies to possibly interconnected modules. In this part of the talk we describe
efforts towards constructing E − SHIQ distributed knowledge bases by mod-
ularizing ontologies: Our aim is to make ontology units as much self-contained
and independent from others as possible, so as to increase the efficiency of the
reasoning process. We discuss the flexibility offered by E − SHIQ itself, and
different modularization options available (a first attempt towards this problem
has been reported in [3]).
3 Challenges towards reasoning with multiple ontologies
Towards reasoning with ontology units in open and dynamic settings with mul-
tiple agents, this talk presents and discusses the following major challenges:
Reaching Agreements to correspondences: Agents in inherently distributed
and open settings can not be assumed to share an agreed ontology of their com-
mon task environment. To interact effectively, these agents need to establish
semantic correspondences between their ontology elements. As already pointed
out, the correspondences computed by two agents may differ due to (a) differ-
ent mapping methods used, to (b) different information one makes available to
the other, or (c) restrictions imposed by their task environment. Although se-
mantic coordination methods have already been proposed for the computation
of subjective correspondences between agents, we need methods for communi-
ties, groups and arbitrarily formed networks of interconnected agents to reach
semantic agreements on subjective ontology elements’ correspondences [4].
Exploitation of ontology units in open and dynamic settings: In open settings
where agents may enter or leave the system at will, we need agents to dynami-
cally combine their knowledge and re-organize themselves, so as to form groups
that can serve specific information needs successfully. There are several issues
that need to be addressed here: Agents (a) must share information about their
potential partners and must learn the capabilities, effectiveness, trustworthiness
etc. of their peers, (b) must locate the potential partners, and (c) must decide
for the ”best” groups to be formed in an ad-hoc manner, towards serving the
specific information needs. Reaching complete and optimal solutions in such a
setting is a hard problem: we discuss the computation of approximate solutions
[5].
Acknowledgements Thanks to Georgios Santipantakis for his contributions
to various parts of this work, especially the one concerning E − SHIQ. The
major part of the research work referenced in this talk is being supported by
the project IRAKLITOS II” of the O.P.E.L.L. 2007 - 2013 of the NSRF (2007 -
2013), co-funded by the European Union and National Resources of Greece.
References
1. Vouros, G.A., Santipantakis, G.M.: Distributed reasoning with eDDL HQ+ SHIQ. In:
Modular Ontologies: Proc. of the 6th International Workshop (WoMo 2012). (July
2012)
2. Santipantakis, G.M., Vouros, G.A.: The e-shiq contextual logic framework. In: AT.
(2012) 300–301
3. Santipantakis, G.M., Vouros, G.A.: Modularizing owl ontologies using eDDL
HQ+ SHIQ.
In: ICTAI. (2012) 411–418
4. Vouros, G.A.: Decentralized semantic coordination via belief propagation. In: AA-
MAS. (2013) 1207–1208
5. Karagiannis, P., Vouros, G.A., Stergiou, K., Samaras, N.: Overlay networks for
task allocation and coordination in large-scale networks of cooperative agents. Au-
tonomous Agents and Multi-Agent Systems 24(1) (2012) 26–68