=Paper= {{Paper |id=Vol-146/paper-14 |storemode=property |title=Situation Modeling and Smart Context Retrieval with Semantic Web Technology and Conflict Resolution |pdfUrl=https://ceur-ws.org/Vol-146/paper13.pdf |volume=Vol-146 |dblpUrl=https://dblp.org/rec/conf/ijcai/Heckmann05 }} ==Situation Modeling and Smart Context Retrieval with Semantic Web Technology and Conflict Resolution== https://ceur-ws.org/Vol-146/paper13.pdf

Situation Modeling and Smart Context Retrieval
with Semantic Web Technology and Conﬂict Resolution

Dominik Heckmann

German Research Center for Artiﬁcial Intelligence
heckmann@dfki.de

Abstract. We present a distributed service to model and control contextual
information in mobile and ubiquitous computing environments. We describe the
general user model and context ontology G UMO for the uniform interpretation
of distributed situational information in intelligent semantic web enriched
environments. Furthermore, we present the relation to the user model and context
markup language U SER ML, that is used to exchange partial models between
different adaptive applications. Our modeling and retrieval approach bases on
semantic web technology and complex conﬂict resolution concepts.

1 Integrated Model for Context-Awareness and User-Adaptivity
The research areas user-adaptivity, context-awareness and ubiquitous computing ﬁnd
their intersection in the concept of context, while semantic web technology could serve
as a mediator between them. In [1] it is pointed out that throughout the different research
communities and disciplines, there are various deﬁnitions of what exactly is contained
in the context model [2], the user model [3], and the situation model [4]. Therefore, it is
necessary to clarify how those terms will be used in our approach. A situation model is
deﬁned in our approach as the combination of a user model and a context model. Fig-
ure 1 presents a diagrammatic answer to the question: What is situated interaction and
how can we conceptualize it? Resource-adaptivity overlaps with user-adaptivity and
context-awareness because the human’s cognitive resources fall into the user model,
while the system’s technical resources can be seen as part of the context model. The
fundamental data structure in our approach is the S ITUATIONAL S TATEMENT, see [5],
that collects apart from the main contextual information also meta data like temporal
and spatial constraints, explanation components and privacy preferences. Distributed
sets of S ITUATION R EPORTS form a coherent, integrated, but still hybrid accretion con-
cept of ubiquitous situation (user and context) models.

2 Context Modeling with UserML and G UMO
Ontologies provide a shared and common understanding of a domain that can be com-
municated between people and heterogeneous and widely spread application systems.
Since ontologies have been developed and investigated in artiﬁcial intelligence to fa-
cilitate knowledge sharing and reuse, they should form the central point of interest

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Fig. 1. Situated interaction and the system’s situation model for mobile computing

for the task of exchanging situation models. The user model & context markup lan-
guage U SER ML is deﬁned as an XML application, see [6]. However, XML is purely
syntactic and structural in nature. Nonetheless, the web ontology language OWL has
more facilities for expressing semantics. OWL can be used to explicitly represent the
meaning of terms in vocabularies and the relationships between those terms. Thus,
OWL is our choice for the representation of user model and context dimension terms
and their interrelationships. This ontology should be available for all user-adaptive
and context-aware systems at the same time, which is perfectly possible via inter-
net and wireless technology. The major advantage would be the simpliﬁcation for
exchanging information between different systems. The current problem of syntacti-
cal and structural differences between existing adaptive systems could be overcome
with such a commonly accepted ontology. G UMO1 collects the user’s dimensions that
are modeled within user-adaptive systems like the user’s heart rate, the user’s age,
the user’s current position, the user’s birthplace or the user’s ability to swim. Sec-
ondly, the contextual dimensions like noise level in the environment, battery status
of the mobile device, or the outside weather conditions are modeled. The main con-
ceptual idea in S ITUATIONAL S TATEMENTS, is the division of user model & context
dimensions into the three parts: auxiliary, predicate and range. Apart from
these mainpart attributes, there are predeﬁned attributes about the situation, the
explanation, the privacy and the administration. Thus, our basic context
modeling is more ﬂexible than simple attribute-value pairs or RDF triples. If one wants
to say something about the user’s interest in football, one could divide this into the
auxiliary=hasInterest, the predicate=football and the range=low-medium-
high. G UMO is designed according to this U SER ML approach. Approximately one
thousand groups of auxiliaries, predicates and ranges have so far been
1
GUMO homepage: http://www.gumo.org

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identiﬁed and inserted into the ontology. However, it turned out that actually everything
can be a predicate for the auxiliary hasInterest or hasKnowledge, what leads
to a problem if work is not modularized. The suggested solution is to identify basic user
model dimensions on the one hand while leaving the more general world knowledge
open for already existing other ontologies on the other hand. Candidates are the general
suggested upper merged ontology SUMO, see [7], and the U BIS O NTOLOGY2 , see [8],
to model intelligent environments. Identiﬁed user model and context auxiliaries
are for example hasKnowledge, hasInterest, hasBelief, hasPlan, hasProperty, hasPlan
and hasLocation. A class deﬁnes a group of individuals that belong together because
they share some properties. Classes can be organized in a specialization hierarchy using
rdfs:subClassOf.

3 Smart Situation Retrieval with Semantic Conﬂict Resolution
The architectural diagram in ﬁgure 2 shows the S MART S ITUATION R ETRIEVAL or
smart context retrieval process. The focus is set on the semantic conﬂict resolution part.
The oval numbers indicate the reading direction. Item (1) shows the request in UserQL

Fig. 2. Smart Situation Retrieval with Focus on Semantic Conﬂict Resolution

that has to be parsed ﬁrst. Item (2) points to the distributed retrieval of S ITUATIONAL -
S TATEMENTS. Item (3) summarizes the three macro-steps select, match and
filter and presents the F ILTERING R ESULT as input to the conﬂict resolution
process. Item (4) stands for the three syntactical procedures VARIATION M APPING,
REMOVE E XPIRED and REMOVE R EPLACED. Item (5) shows the three semantical pro-
cedures G ROUP M EMBER M APPING, S EMANTIC P ROPERTY M APPING and S EMANTI -
C R ANGE M APPING that base on knowledge in the ontologies G UMO , UbisWorldOntol-
ogy, SUMO/MILO and the knowledge base WorldNet. Item (6) shows the detection of
2
UbisWorld homepage: http://www.ubisworld.org

130
syntactic and semantic conﬂicts and the construction of S ∗ , A∗ , P ∗ , R∗ nonExpired
nonReplaced
conﬂict sets. Item (7) points to the post-processing of ranking, format, naming
and function that control the output format. Item (8) forms the resulting UserML
report, that is send via HTTP to the requestor. The matching procedure compares all
given match attributes with the corresponding statement attributes. Furthermore it in-
tegrates semantic functionality like ontological extension and spatial inclusion. The ﬁl-
tering procedure operates on the M ATCHING R ESULT. Each statement is individually
checked if it passes the privacy ﬁlter, the conﬁdence ﬁlter and the temporal ﬁlter. The
privacy ﬁlter checks if the statement.access is either set to public, or if it is
set to friends, that the friends relation holds between the query.requestor and
the statement.owner, or if it is set to private that the query.requestor is
the same as statement.owner. As every user and every system is allowed to enter
statements into repositories, some of this information might be contradictory. Conﬂicts
among S ITUATIONAL S TATEMENTS like for example a contradiction caused by differ-
ent opinions of different creators or changed values over time are loosely categorized
in the following listing.

1. ON THE SEMANTICAL LEVEL : the systems are not forced to use the same vocabulary, to say
the same ontology, to represent the meaning of the concepts, which leads to the user model
integration problem number one: ontology merging and semantic web integration.
2. ON THE OBSERVATION AND INFERENCE LEVEL : several sensors can see same things dif-
ferently and claim to be right, measurement errors can occur, systems may have preferred
information sources
3. ON THE TEMPORAL AND SPATIAL LEVEL : information can be out of date or out of spatial
range, a degree of expiry can hold. Reasoning on temporal and spatial meta data is necessary
4. ON THE PRIVACY AND TRUST LEVEL : information can be hidden, incomplete, secret or
falsiﬁed on purpose, a system of trustworthiness could be applied

Conﬂict Resolvers are a special kind of ﬁlters that control the conﬂict resolution
process. An ordered list of these resolvers deﬁne the conﬂict resolution strategy. They
are modeled in the query.strategy attribute. These resolvers are needed if the
match process and ﬁlter process leave several conﬂicting statements as possible an-
swers. Three kinds of conﬂict resolvers can be identiﬁed: the most(n)-resolvers that use
meta data for their decision, the add-resolvers that add expired or replaced statements
to the conﬂict sets, and the return-resolvers that don’t use any data for their selection.

mostRecent(n) Especially where sensors send new statements on a frequent basis, values tend
to change more quickly as they expire. This leads to conﬂicting non-expired statements. The
mostRecent(n) resolver returns the n newest non-expired statements, where n is a natural
number between 1 and the number of remaining statements.
mostNamed(n) If there are many statements that claim A and only a few claim B or something
else, than n of the ”most named” statements are returned. Of course it is not sure that the
majority necessarily tells the truth but it could be a reasonable rule of thumb for some cases.
mostConﬁdent(n) If the conﬁdence values of several conﬂicting statements can be compared
with each other, it seems to be an obvious decision to return the n statements with the highest
conﬁdence value.
mostSpeciﬁc(n) If the range or the object of a statement is more speciﬁc than in oth-
ers, the n ”most speciﬁc” statements are returned by this resolver. For example if:

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auxiliary=hasKnowledge, predicate=chess and ﬁrst range=yesNo while the sec-
ond range=Novice-Occasional-Professional-Expert-Grandmaster, the statement with the
second range contains a more speciﬁc information. Another speciﬁcity range ordering is for
example: yesNo < lowMediumHigh < 0%-100%
mostPersonal(n) If the creator of the statement is the same as the statement’s subject (a
self-reﬂecting statement), this statement is preferred by the mostPersonal(n) resolver. Fur-
thermore, if an is-friend-of relation is deﬁned, statements by friends could be preferred to
statements by others.
These conﬂict resolver rules are based on common sense heuristics, however they need
not to be true for speciﬁc sets of statements. An important issue to keep in mind is the
problem that resolvers and strategies imply uncertainty. To contribute to this fact, the
confidence value of the resulting statement is appropriately changed, furthermore
the conﬂict situation is added to the evidence attribute.

Summary and Acknowledgements
We have introduced an integrated architecture for Situation Modeling and Smart Context
Retrieval. We have clariﬁed a model for situated interaction and context-awareness.
The context exchange language UserML has been presented as well as the general user
model & context ontology G UMO. Our approach bases on semantic web technology
and a complex conﬂict resolution and query concept, in order to be ﬂexible enough
to support adaptation in human-computer interaction in ubiquitous computing. This
work has been supported by the German Ministry of Education and Research within the
project SPECTER at the German Research Center for Artiﬁcial Intelligence (DFKI).

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