=Paper=
{{Paper
|id=Vol-298/paper-4
|storemode=property
|title=The user model and context ontology GUMO revisited for future Web 2.0 extensions
|pdfUrl=https://ceur-ws.org/Vol-298/paper4.pdf
|volume=Vol-298
|dblpUrl=https://dblp.org/rec/conf/context/HeckmannSMDK07
}}
==The user model and context ontology GUMO revisited for future Web 2.0 extensions==
https://ceur-ws.org/Vol-298/paper4.pdf
The User Model and Context Ontology GUMO
revisited for future Web 2.0 Extensions
Dominik Heckmann, Eric Schwarzkopf, Junichiro Mori, Dietmar Dengler,
Alexander Kröner
German Research Center for Artificial Intelligence
D-66041 Saarbrücken, Germany
heckmann@dfki.de
Abstract. We revisit the top-level ontology Gumo for the uniform man-
agement of user and context models in a semantic web environment.
We discuss design decisions, while putting the focus on ontological is-
sues. The structural integration into user model servers, especially into
the U2M-UserModel&ContextService, is also presented. We show ubiq-
uitous applications using the user model ontology Gumo together with
the user model markup language UserML. Finally, we ask how data from
Web 2.0 and especially from a social tagging application like del.icio.us
as a basis for user adaptation and context-awareness could influence the
ontology.
Keywords ubiquitous user modeling, semantic web, ontological engineering,
web 2.0, user model markup language
1 Motivation and Introduction
A commonly accepted top level ontology for user and context models is of great
importance for the user modeling and context research community. This ontology
should be represented in a modern semantic web language like OWL and thus be
available for all user-adaptive systems at the same time via internet. The major
advantage would be the simplification for exchanging user model and context
data between different user-adaptive systems.
However, the current trends of web 2.0 and social computing tell us that the
users like to create their own tag spaces, naming conventions and taxonomies.
The masses of tagging, rating and even blogging define a kind of ”wisdom of
the crowds”. Now the question arises how this new bottom-up approach can be
combined with the more top-down approach of ontology engineering. Does a re-
visiting of a domain ontology like the user model and context ontology GUMO
make sense? There are two directions of mutual influence possible. An existing
ontology could be used in taxonomy learning of tag spaces in a way of seeding,
or the other way round, the taxonomies that are dynamically generated by the
tagging behavior of communities can be used to correct or update existing on-
tologies. Approaches for tag-space mining are presented in [Schmitz et al., 2006],
2
[Heymann and Garcia-Molina, 2006] and [Golder and Huberman, 2006]. And in
[Mika, 2005] a first attempt is shown how to learn ontologies from tag-space
mining. Please notice that we present in this paper only initial thoughts in the
direction of the duality of ontology engineering and tag-space mining. Back to
the ontological approach. The problem of syntactical and structural differences
between existing user modeling and context systems could be overcome with a
commonly accepted taxonomy, specialized for user modeling tasks. Note, that we
are talking about a user model ontology rather than a user modeling ontology,
which would include, the inference techniques, or knowledge about the research
area in general. We are analyzing the user’s dimensions that are modeled within
user-adaptive systems like the user’s heart beat, the user’s age, the user’s current
position or the user’s birthplace.
Ontologies provide a shared and common understanding of a domain that
can be communicated between people and heterogeneous and widely spread ap-
plication systems, as pointed out in [Fensel, 2001]. Since ontologies have been
developed and investigated in artificial intelligence to facilitate knowledge shar-
ing and reuse, they should form the central point of interest for the task of
exchanging user models. The design choices in our approach are described in
the following. The main conceptual idea for the construction of the special-
ized user model ontology Gumo was to divide the descriptions of user model
dimensions into three parts: auxiliary - predicate - range. For example if one
wants to say something about the user’s interest in football, one could divide this
into the auxiliary part: ”interest”, the category part ”football” and the range
part: ”low-medium-high”. If a system wants to express something like the user’s
knowledge about Beethoven’s Symphonies, one could divide this into the triple:
”knowledge” - ”Beethoven’s Symphonies” - ”poor-average-good-excellent”. As
a third example, the user’s hair-color would lead to: ”property” - ”hair-color”
- ”black-red-brown-blonde-white”. First of off all, important groups of auxil-
iaries have to be identified. A list of identified important user model auxiliaries
could be { has Property, has Interest, has Believe, has Knowledge, has Pref-
erence, has Regularity, has Plan, has Goal, has Location }. This listing is not
intended to be complete, but it is a start with which, most of the important
user facts can be realized. Then the user model predicates have to be classi-
fied and analyzed. But it turned out that actually everything can be a category
for the auxiliary ”interest” or ”knowledge”, thus a whole world-ontology would
be needed, what leads to a real problem if one does not work modularized.
The crucial idea is to leave this part open for existing other ontologies like the
general CYC ontology (see [Lenat, 1995] for example), the UbisWorld ontology
(see [Stahl and Heckmann, 2004]), or any other. This insight leads to a modular
approach which forms a key feature rather than a disadvantage. Nevertheless
the problem of finding a commonly accepted, specialized top level ontology for
the user modeling research group is moved into the user’s property section:
Which classes of user dimensions can be identified? In [Jameson, 2001] and in
[Kobsa, 2001] rough classifications for such categories can be found. However,
no top level user model ontology has been proposed so far.
3
Fig. 1. Several User Model Property Dimensions: Emotional States, Characteristics
and Personality with included sub models like the ”Five Factor Model”
2 Representation of Gumo in OWL
In this section we discuss, why we have chosen the web ontology language OWL.
We present three concept definitions, namely the class ”Physiological State”, the
user model dimension ”Happiness” and the auxiliary ”has Knowledge”.
2.1 Three example concept definitions from Gumo
Figure 2 presents as a first example the concept of the user model dimension
class Physiological State which is realized as a owl:Class. A class defines a group
of individuals that belong together because they share some properties. Classes
can be organized in a specialization hierarchy using subClassOf. There is a
built-in most general class named Thing that is the class of all individuals and
a superclass of all OWL classes. The Physiological State is defined as subclass
of Basic User Dimensions.
Every new concept has a unique rdf:ID, that can be resolved into a com-
plete URI. Since the handling of these URIs could become very unhandy, a short
identification number was introduced, the so called u2m:identifier. The iden-
tification number in this case is 700016, it has been chosen arbitrarily but seen
4
Physiological State
700016
the state of the body or bodily functions
Fig. 2. The OWL class definition of ”Physiological State”
under its namespace, it is unique. It has the advantage of freeing the textual
part in the rdf:ID from the need of being semantically unique. The term mouse
for example, could be read as the animal mouse, or as the computing device
mouse. Apart from solving the problem of conceptual ambiguity, this number
facilitates the work within relational databases, which is important from the
implementation point of view.
Figure 2 also defines the lexical entry u2m:lexicon of the concept of Physi-
ological State as ”the state of the body or bodily functions”, while this textual
definition could also be realized through a link to an external lexicon. The at-
tribute u2m:website points towards a web site, that has its purpose in present-
ing this ontology concept, to a human reader. The abbreviation &UserOL; is a
shortcut for the complete URL to the Gumo ontology.
Happiness
800616
Hour.520060
Fig. 3. GUMO definition of ”Happiness”
Figure 3 defines the user model dimension Happiness as an rdf:Description.
It contains a rdfs:label, a u2m:identifier and a u2m:website attribute. Ad-
ditionally it provides a default value of the average durability u2m:durability.
It carries the qualitative time span of how long the statement is expected to be
valid (like minutes, hours, days, years). In most cases when user model dimen-
sions or context dimensions are measured, one has a rough idea about the ex-
pected durability, for instance, emotional states change normally within hours,
however personality traits won‘t change within months. Since this qualitative
time span is dependent from every user model dimension, a definition mechanism
5
is prepared within the Gumo. Some examples of rough durability-classifications,
without any attempt of proven correctness, are:
– physiologicalState.heartbeat - can change within seconds
– mentalState.timePressure - can change within minutes
– emotionalState.happiness - can change within hours
– characteristics.inventive - can change within months
– personality.introvert - can change within years
– demographics.birthplace - can’t normally change at all
Another important point that is shown in the definition of happiness in figure
3 is the ability in OWL of multiple-inheritance. In detail, happiness is defined
as rdf:type of the class Emotional State as well as rdf:type of the class Five
Basic Emotions. Thus OWL allows to construct complex, graph like hierarchies
of user model concepts, which is especially important for ontology integration.
Figure 4 defines the auxiliary has Knowledge as rdfs:subPropertyOf of the
has Knowledge
600120
Fig. 4. GUMO Property hasKnowledge as example for general auxiliaries
resource user model auxiliary with the rdf:domain #Person, which is not part
of the user model ontology itself, but which is part of the general UbisWorld
Ontology, see [Stahl and Heckmann, 2004]. The acronym u2m stands for ubiqui-
tous user modeling and forms a collection of standards, that are available on-
line at http://www.u2m.org/. The new vocabulary for the user model ontology
language consists of u2m:identifier, u2m:durability, u2m:image, u2m:website
u2m:lexicon . The main User Model Dimension that we identified so far are
MentalState PhysicalState, Demographics, ContactInformation, Role, Emotion-
alState, Personality, Characteristics, Ability, Proficience and Motion.
To support the distributed construction and refinement of the top level user
model ontology, we developed a specialized online editor, that helps with in-
troducing new concepts, adding their definitions and transform the information
automatically into the required semantic web ontology language. Currently sup-
ported are RDF and OWL.
3 The U2M-UserModelServer
A user model server manages information about users or individuals in general.
The U2M-UserModel&ContextService, see [Heckmann, 2003a] is an application-
6
independent server with a distributed approach for accessing and storing user
information, while the focus lies on the possibility to exchange and understand
the data between different applications, as well as adding privacy and trans-
parency to the statements about the user itself. The semantics for all concepts
is mapped to the Gumo ontology.
Applications can retrieve or add information to the server by simple HTTP
requests, alternatively, by the ”UserML” WebService. UserML, see for example
[Heckmann and Krüger, 2003], is an XML application which is based on the con-
cept of ”situational statements”, as introduced in [Heckmann, 2003b]. A request
could look like:
http://www.u2m.org/UbisWorld/UserModelServer.php?
subject=Joerg.210006&auxiliary=hasProperty&predicate=Age.800302
Mentionable is the optional naming convention for disambiguation, like ”Jo-
erg.210006” or ”Age.800302”. These names are unique identifiers for the particu-
lar, intended concepts. A general problem when one wants to talk about objects,
individuals or concepts is the non-uniqueness of names, as seen before, especially
in an open web-based system. In the Semantic Web approach, each resource is
mapped to a (hopefully) unique URI. But the URIs have the disadvantage that
they are rather long and uneasy to read. The used naming-format ”Name.Id” can
be seen as a shortcut for such a unique URI. Those unique resource identifiers,
for the area of user modeling, are established in the Gumo.
The user model server ”u2m.org” can be used by every user adaptive system
to manage user related data, but also by the modeled user himself. A specialized
UserModelEditor is provided which displays the information in a web-browser
form that allows the change and privacy control, see http://www.u2m.org. The
access, the purpose and the retention of every situational statement can be con-
trolled in the ”editor view modus”. Each statement can contain meta information
like creator, method, evidence or confidence. Figure 5 shows the overall archi-
tecture of the UserModelServer with its input and output information flows
Query, Answer and Add that are represented as arrows. The main block of the
illustration contains four piled, dotted rectangles. The lowest one indicates the
distributed storage of the so called SituationalStatements, which are ex-
plained in detail in [Heckmann and Krüger, 2003]. The second rectangle shows
the filter, ranking and conflict resolution strategies that are applied to the set
of Situational Statements. The User Model Server itself, which is responsible for
communication, handling requests and responses, is based on both introduced
rectangles as well as the rectangle on the top for distributed knowledge bases in
form of semantic web ontologies. A query or request, that is received in the so
called UserQL query language will be handled by the user model server in the
following way: first all matching situational statements are retrieved, then the
filter and resolution strategies are applied and finally the semantics is given by
referencing to web ontologies.
7
Fig. 5. Architecture of the UserModelServer
4 How to further develop GUMO in the era of Web2.0?
The Semantic Web is based on the content-oriented description of digital doc-
uments with standardized vocabularies that provide machine understandable
semantics. The result is the transformation from a Web of Links into a Web
of Meaning / Semantic Web, (see arrow A in Fig. 6). On the other hand, the
traditional Web 1.0 has recently been orthogonally shifted into a Web of Peo-
ple / Web 2.0 where the focus is set on folksonomies, collective intelligence and
the wisdom of crowds (see arrow B in Fig. 6). Only the combined muscle of
semantic web technologies and broad user participation will ultimately lead to
a Web 3.0 with completely new business opportunities in all segments of the
ITC market. Without Web 2.0 technologies and without activating the power of
community-based semantic tagging, the emerging semantic web cannot be scaled
and broadened to the level, that is needed for a complete transformation of the
current syntactic web. On the other hand, current Web 2.0 technologies cannot
be used for automatic service composition and open domain query answering
without adding machine-understandable content descriptions based on seman-
tic web technologies. The ultimate world-wide knowledge infrastructure cannot
be produced fully automatically, but needs massive user participation based on
open semantic platforms and standards.
The interesting and urging question that arises is: what happens when the
emerging Semantic Web and Web 2.0 meet with their full potential power?
There are no new technologies introduced by Web 2.0, but the role and value
of the user has been changed significantly. We focus in this paper on tagging.
8
Fig. 6. Joining Semantic Web and Web 2.0
However, a social rating system could also be of interest in order to improve the
ontologies.
Tag spaces are an obvious source of data for user modeling. The user of
a social tagging tool could provide access to his personal tag space to an e-
commerce site which could use the data to tailor its structure and presentation
to the user. For example, a music store could attempt to assess where a user
lives given data from a social bookmarking site. Then, if the user is interested
in an album by an artist who will give a concert in the vicinity of the user’s
home town, the store could offer him tickets for the event. How can we use a
tag space and a user’s tagging data to create a user model and adapt a system?
Furthermore, how can we use the already developed general user model and
context Ontology Gumo to improve the tagging taxonomy and the generated user
model and context rules? Figure 7 shows the possible connection of Gumo and
Web 2.0.
The approach we are proposing starts with automatically learning a structure
of the tag space, then manually defining adaptation rules based on that structure,
and finally automatically mapping a user’s data into the structure in order to
decide what adaptation rules to apply. This implies that the set of possible
adaptation rules depends on the learned structure. For instance, creating a rule
with a precondition on the home town of a user is sensible only if this information
is part of the structure. Not all tag spaces are suitable for this type of user
modeling. Because we want to learn something about the user’s interests, we
require tagging data used by the user for himself (as in del.ico.us) and not for
others (as in flickr).
We are aiming for a taxonomy of tags, where subtags of a tag tag (for ex-
ample, pop-music should be a subtag of music). For the designer of an adaptive
system, identifying the semantics of a tag (by using its predecessors and succes-
sors its generality (the higher it is in the taxonomy, the more users will Hence,
we think a taxonomy is a good underlying structure for the a taxonomy from a
9
Fig. 7. A possible connection of GUMO and Web 2.0
tag space is the main subject of this paper. See [Schwarzkopf et al., 2007] for a
detailed description of this approach.
Summary We have revisited the user model and context ontology Gumo in
the semantic web ontology language OWL together with the exchange language
UserML and the U2M UserModel&ContextServer. This work is highly under
progress and the future goal is to find out the influence of social computing in
Web 2.0 to the so far only semantic web approach in order to determine the
possible advantages of combining tag-space mining and ontology engineering.
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