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A Metadata Model for Semantics-Based Peer-To-Peer Systems

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Abstract and Figures

Peer-to-Peer systems are a new paradigm for information sharing and some systems have successfully been deployed. It has been argued that current Peer-to-Peer systems suffer from the lack of semantics. The SWAP project (Semantic Web and Peer-to-Peer) aims at overcoming this problem by combining the Peer-to-Peer paradigm with Semantic Web technologies. In the course of our investigations it turned out that the nature of Peer-to-Peer systems requires some compromises with respect to the use of semantic knowledge models. In particular, the notion of ontology does not really apply as we often do not find a shared understanding of the domain. In this paper, we propose a data model for encoding semantic information that combines features of ontology (concept hierarchies, relational structures) with a flexible description and rating model that allows us to handle heterogeneous and even contradictory views on the domain of interest. We discuss the role of this model in the SWAP environment and describe the model as well as its creation and access. 1
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A Metadata Model for Semantics-Based
Peer-to-Peer Systems
Jeen Broekstra
, Marc Ehrig
, Peter Haase
, Frank van Harmelen
, Arjohn
, Marta Sabou
, Ronny Siebes
, Steffen Staab
, Heiner
Christoph Tempich
Vrije Universiteit Amsterdam
{jbroeks, frankh, marta, ronny, heiner}
Institute AIFB, University of Karlsruhe, D-76128 Karlsruhe
AIdministrator BV, Ammersfoort
{jeen.broekstra, arjohn.kampman}
Abstract. Peer-to-Peer systems are a new paradigm for information
sharing and some systems have successfully been deployed. It has been
argued that current Peer-to-Peer systems suffer from the lack of seman-
tics. The SWAP project (Semantic Web and Peer-to-Peer) aims at over-
coming this problem by combining the Peer-to-Peer paradigm with Se-
mantic Web technologies. In the course of our investigations it turned
out that the nature of Peer-to-Peer systems requires some compromises
with respect to the use of semantic knowledge models. In particular, the
notion of ontology does not really apply as we often do not find a shared
understanding of the domain. In this paper, we propose a data model for
enco ding semantic information that combines features of ontology (con-
cept hierarchies, relational structures) with a flexible description and
ranking model that allows us to handle heterogeneous and even contra-
dictory views on the domain of interest. We discuss the role of this model
in the SWAP environment and describe the model as well as its creation
and access.
1 Motivation
The essence of Peer-to-Peer (P2P) is that nodes in the network directly exploit
resources present at other nodes of the network without intervention of any
central server. The tremendous success of networks like Napster and Gnutella,
and of highly visible industry initiatives such as Sun’s JXTA, as well as the
Peer-to-Peer Working Group including HP, IBM and Intel, have shown that the
P2P paradigm is a particularly powerful one when it comes to sharing files over
the Internet without any central repository, without centralized administration,
and with file delivery dedicated solely to user needs in a robust, scalable
manner. At the same time, today’s P2P solutions support only limited update,
search and retrieval functionality, e.g. search in Napster is restricted to string
matches involving just two fields: “artist” and “track”. These flaws however
make current P2P systems unsuitable for knowledge sharing purposes.
Metadata plays a central role in the effort of providing search techniques that
go beyond string matching. Ontology-based metadata facilitates the access to
domain knowledge[1]. Furthermore, it enables the construction of exact queries.
Existing approaches of ontology-based information access almost always assume
a setting where information providers share an ontology that is used to access
the information[2]. In a Peer-to-Peer setting, this assumption does not longer
hold. We rather face the situation, where individual peers maintain their own
view of the domain in terms of the organization of the local file system and other
information sources. Enforcing the use of a global ontology in such a setting
would mean to give up the benefits of the Peer-to-Peer approach mentioned
above. Therefore, one has to find a way to deal with the existence of multiple,
distributed and frequently changing views on the domain.
In this paper, we propose a metadata model that combines ontological struc-
tures with information needed to align, evolve and use these for query processing.
In section 2, we explain the requirements for the metadata model that guided its
development in more detail. The third section foc uses on the SWAP environment
in w hich the metadata model is used. The model itself is introduced in section
4. In section 5 we describe the methods supporting the creation, use and update
of the metadata model. We conclude with a discussion of open problems.
2 Requirements
We will illustrate the requirements for the proposed system with the short
scenario described in the following. Virtual organizations or large companies
impose a complex situation, with respect to numb e r of domains, conceptualiza-
tions and documents onto a peer-system for knowledge sharing. Typically their
organizational units are distributed according to expertise or organizational
tasks, such as development and marketing. A small case study of a virtual
organization in the tourism domain is used as real world example. The virtual
organization comprises public authorities, hotels and event organizers. The
public authorities require the number of guests visiting the country to plan
for example public transport and waste management. Event organizers can
customize their offerings according to the number of visitors and the age.
Hotels can publish this information to make the stay m ore pleasant. Today the
exchange of this kind of information is time consuming, not in time and error
prone, although it is often available in electronic form at every level. However,
the different organization have diverse objectives and therefore use different
conceptualizations of their domains.
From a technical point of view the different organizations can be seen as one
or m any independently operating nodes within a “knowledge” network. Nodes
can join or disconnect from the network at any moment and can live or act
independently of the behavior of other nodes in the system. A node may perform
several tasks. Most important is that it acts as a peer in the network, so it can
communicate with other nodes to achieve its goals. But apart from that it may
act as an interface to interact with the human user of the network, or it may
access knowledge sources to accomplish its tasks. One node may have one or more
knowledge sources associated with it. These sources contain the information that
a peer can make available to other peers. Examples are a user’s filesystem and
mail folders or a locally installed database. A node must be designed to meet
the following requirements that arise from the task of sharing information from
the e xternal sources with other peers:
Multiple sources of information
Mostly uniform treatment of internal and external sources
Multiple views on available information
Supp ort for query answering and routing
Distribution of information within the network
The metadata model needs to reflect these requirements. We derive some
main objectives for the metadata model with emphasis on the information me-
Integration Each piece of knowledge requires metadata about its origin. To re-
trieve external information, the metadata needs to capture information about
where the piece of information was obtained from. This information will allow
to identify a peer and locate resources in its repositories.
Information heterogeneity As information is added from a variety of peers, in-
consistencies may occur in a local repository.
Information needs to be assigned a confidence rating, such that the system
will be able to handle heterogeneity and provide useful information. Similarly, a
level of trust can be assigned to peers to model their reliability.
Furthermore, as each peer uses its own local ontology, mappings may be
required to overcome the heterogenous labelling of the same objects.
Security Some information may be of private nature and should not be visible
to other peers. Other information may be restricted to a specific set of peers.
The m etadata model needs to provide means to express these security policies.
Caching Within Peer-to-Peer systems the availability of other peers is not al-
ways guaranteed. Moreover, some peers may have better connectivity, in terms
of bandwidth, to the rest of the network than other peers. Hence, to improve net-
work efficiency, caching of information can be useful. The caching mechanisms
needs to be transparent to the user, but must be captured by the metadata
3 The SWAP environment
The SWAP environment
is a general infrastructure which was designed to meet
the requirements on a knowledge node . The proposed architecture consists of
three representational comp onents: (1) knowledge sources (lower left corner of
figure 1), (2) individual views on these information sources generated by the
user interface (upper right corner of figure 1) and (3) and local node repository
(upp e r left corner of figure 1). Together with additional information provided
by other peers in the network these sources make up the knowledge available
to a peer. The overall architecture of a SWAP node as shown in the figure is
described in [3].
Fig. 1. Abstract Architecture of a SWAP Node
Internal Knowledge Sources: Peers may have local sources of information such as
the local file system, e-mail directories or bookmark lists. These local information
sources represent the peer’s body of knowledge as well as his basic vocabulary.
These sources of information are the place where a peer can physically store
information (documents, web pages) to be shared on the network.
Individual Views: Peers provide different views on the information they have in
their local s ources as well as onto information on the network. The views can be
implemented using different visualization techniques (topic hierarchies, thematic
maps, etc). There are some predefined views that correspond one-to-one to the
different sources of information. Additionally, the user can define more complex
views that range over different sources of information.
Local Node Repository: In order to manage the different information models and
views as well as information acquired from the network, each peer maintains an
internal working model. This model provides the following functionality:
Mediate between views and stored information
Supp ort query formulation and processing
Specify the peer’s interface to the network
Provide the basis for peer ranking and selection
The working model is not an ontology in the classical sense. It is rather a
knowledge structure, which provides an integrated model of the different struc-
tures that coexist in the network and the p e er itself. The different structures
are stored in a single model, individual elements of the model are annotated
with their source and a ranking representing the peer’s belief in their plausi-
bility. The complete model may contain inconsistencies, in order to receive a
consistent model a part of the complete one has to be chosen on the basis of the
plausibility ranking.
4 The SWAP Metadata Model
The SWAP environment aims at providing a general view on the knowledge
each peer has. It should facilitate the access to different information sources
and enable the user to take advantage from other peers’ knowledge. Therefore a
metadata model was designed which provides semantics to annotate external as
well as internal data. Information from different information sources and from
other peers can be integrated with this metadata model to enable a later retrieval
of the underlying information items. If the information source is a file system, the
information item , viz. file, must be retrievable. Furthermore, it allows to cache
information to make the entire network work more efficiently. Another purp os e
of the metadata model is to deal with the information heterogeneity which is
inherent in Peer-to-Peer systems.
4.1 Detailed description
As a response to the objectives, we define a SWAP specific metadata model in
RDF(S)[4]. An overview of the model is given in figure 2. The complete definition
of the model is available at:
The model consists of two RDFS classes namely the “Swabbi”-class and a
“Peer”-class. Each of these objects is augmented by several properties which
allow for the above described objectives.
Fig. 2. The SWAP metadata model
Swabbi: Each piece of content information links to a “Swabbi”-object. This
object contains the meta-information or links to it.
Peer: For each information we have to save from which peer it originates from.
Therefore the local repository stores different information about each known
peer. The information is grouped in the Peer object. The “Swabbi”-object links
to the corresponding peer.
peerID: This is the first attribute stored within the peer object. Each peer has
a unique ID to be identified. For our purposes this will be the JXTA UID.
peerLabel: The second peer attribute gives the peer label, which is a human
readable and understandable description of the peer. Natural text is used for
this. An example could be: “Marc Ehrig Notebook”.
peerTrust: The last peer attribute is a measure to include trust. Some peers
might be more reliable than others. The faith in responses of peers changes with
every message containing true or false information. It can be defined manually
for specific peers. To control the peerTrust it is defined to have a value between
0 and 1, with zero meaning having no faith at all and one the peer being very
uri: Each piece of information was originally create d on one peer. To keep track
of the origin of the information and its primary URI this is explicitly saved among
the metadata. With this information it is possible to unambiguously address an
object across the network. The URI-attribute is also required for mapping which
will be explained at another point in this document.
location: Whereas the URI is an identifier within the ontology of the local repos-
itory, the location-attribute is a physical identifier. If we want to access a doc-
ument we do not only need the document-object in the ontology but also the
address where the document can be accessed e.g. file://c:/Projects/myfile.txt.
Only the peer where the information is physically stored needs to be able to
interpret the expression. The location information is also required when doing
updates of the local repository.
label: The label saves how the specific information is called on the peer it orig-
inates from. The label-attribute is formulated in natural language, so users can
also understand it (e.g. bank, financial institution). While URIs are only machine
readable the label is human readable, but the meaning and uses are equivalent.
As one concept can have different names on different peers, this property is
added to each “Swabbi”-object and not only to the original object.
confidence: Trust is used to measure the reliability of a specific peer. The
confidence-attribute returns a figure describing a specific statement. Again it
is s tored as a number between 0 and 1.
additionDate: This attribute keeps track of the date it was added to the local
repository. This could be used to determine confidence; old information might
become less reliable. The main reason for this will be to make the right updates.
security: Security issues and access rights are important in enterprizes. In the
wide open Peer-to-Peer environment some access control is required to ensure
prop e r usage of the information. The security mechanism is not yet specified in
detail, but is provisioned with this attribute.
visibility: For editing purposes some objects will have to be hidden, instead of
being completely removed. This can be achieved by this attribute. Furthermore,
the c ached information is annotated with this property and set to “false”.
cache: To increase the network efficiency caching of information will be neces-
sary. The cached information is annotated with this property and set to date of
the inclusion.
5 Working with the Model
In order to use the model described above for semantics-based information ex-
change, we have to provide a set of methods for constructing the repository
according to the metadata model and to access the knowledge that is stored
therein. In the following, we describe methods that have been developed to (1)
create repository content mostly automatically from local information source,
to (2) rank information in a repository based on the confidence we have in its
reliability and to (3) access parts of the content in the repository and present it
to the user in a comprehensive way.
5.1 Creation
Building up the Model The creation of the metadata comprises the extraction
mechanism, the metadata integration and the ontology creation.
Extraction As mentioned ab ove, the SWAP-system provides an integrated m odel
of different structures that coexist in the network and the peer itself. These
structures are file systems, emails, databases , ontologies and others. In order to
include the different information sources into our model we e xtract an RDF(S)
representation from them. This extraction is just on a syntactical level, i.e. the
existing structure is translated into an RDF(S) representation. The single infor-
mation items are annotated according to an information source s pecific metadata
model. This metadata model has to be built for each information source manu-
ally. However, the metadata model does not model information which could not
be extracted automatically from the information source.
Note, that the use of the SWAP-system is not restricted to SWAP-pe ers
which have used the swap-common namespace. It represents the peer’s knowl-
edge about the network. As long as the other peer understands the query lan-
guage it can participate as an information provider and seeker.
<rdf:RDF xmlns:rdf=’’
<rdfs:label xml:lang="en">Project</rdfs:label>
Metadata integration Having built an RDF(S) representation of the information
sources, in the second processing step the “Swabbi”-objects are added. Adding
the metadata to the extracted structures is straightforward. A new object is
created for each resource or statement. To link a “Swabbi”-object to a statement
we use the construct of reification.
The properties (as presented in 4.1) are filled accordingly and a reference
between the resource and the “Swabbi”-object is established with a “hasSwabbi”
relation. Some of the “Swabbi”-object’s prop e rties are now examined in more
detail. The “additionDate” property is set to the date when the statement was
added or updated. The “visibility” property is set to “true” if the user included
the s tatement or resource and wants it to be displayed otherwise “false”.
One major source of information are other peers. Query results are returned
as an RDF-graph. The user selects the information to keep. This information is
added to the local repository in the same way as any other information from the
peer itself. Of course the “hasPeer” property of the “Swabbi”-object changes.
The “peerTrust” property might alter when receiving queries. This mechanism
is des cribed in the next section.
The example shows a reified statement with its corresponding “Swabbi” in-
<!-- peer1 -->
<rdf:subject rdf:resource="swap://1234567890.jxta#project"/>
<rdf:predicate rdf:resource="rdfs:subclassOf"/>
<rdf:object rdf:resource="swap://1234567890.jxta#thing"/>
<swap:hasSwabbi rdf:resource="swap://1234567890.jxta#swabbiObjectNo01"/>
<swap:hasPeer rdf:resource="swap://1234567890.jxta#knownPeers0001" />
<swap:uri rdf:resource="swap://1234567890.jxta#project" />
<swap:Peer rdf:about="swap://1234567890.jxta#knownPeers0001">
Ontology creation The extracted structures are a starting point for the ontology
creation process. We suppose that the extracted information and background
knowledge like e.g. WordNet
can be used to create these richer structures. This
would mean adding formerly implicit semantics explicitly to the structure.
We assume for the m oment, that we can determine the type of a certain
information item in an ontological sense and can describe it as either concept,
instance or property. If we extract for example a folder with the label “Project”
we assert it is a concept while the label “SWAP”
would rather correspond to an
Just differences are shown
3 wn/
SWAP is a project of the EU. Contract no. EU IST-2001-34103.
instance of this concept. To represent this kind of statem ents in RDF we use the
multiple inheritance mechanism of the language. In this manner we just have to
add a new “rdf:type” statement to the already existing resource.
Automatically made assertions will probably lead to contradictions within
the model. With the “confidence” we can adjust our believe in the particular
In the following example just the changes in the structure are shown:
<!-- peer1 -->
<rdf:type rdf:resource=’swap://1234567890.jxta#project’/>
Merging One goal of the SWAP system is to have one knowledge representation.
Through addition of resources and statements from other peers the same object
might be present in the local repository, but under different names. The system
has to identify these two objects through similarity measures and merge them
accordingly. Whereas in RDF(S) no explicit relation as “equals” is defined, we
use the equality relation defined in OWL[5].
Rating Model Content Statements (subclassOf and instanceOf) made by
peers can be incomplete, vague or even false. For this reason, statements are
not accepted by a peer as an absolute truth, but is judged based on the previ-
ous experience with the sender [6]. For example, if the sender tells the receiver
something about gorillas and the receiver knows that the sender is an expert
on orangutans, then it can derive from the fact that both concepts (gorilla and
orangutan) have a small semantic distance that the sender probably also knows
more than an average user about gorillas. To formalize the expertise we intro-
duce confidence ratings that are meta-statements placed in the ’Swabbi’ that
indicate the confidence in a certain statement. The rating methodology involves
the following aspects: (1) assigning confidence ratings to s tatements received by
different peers. (2) calculating the confidence in the total statement itself (3)
updating these ratings when new information from these peers is received. (4)
using these ratings to determine a set of peers that have a high probability of
correctly answering an incoming query and (5) applying an aging mechanism on
ratings and removing statements that have a rating below a certain value. We
describe these aspects in more detail:
Assigning confidence ratings to statements from a peer In this case we have to
distinguish between derived statements from the extraction algorithm described
in the previous paragraph and statements received from external peers. In the
first case we assume that the user is confident in the statements that are derived
and therefore assigned a high confidence rating. The exact value has to be deter-
mined by experiments. In the second case the confidence ratings are calculated
from the previous statements from the sender. When a statement is provided by
an unknown source it gets a (low) initial confidence rating. With ’unknown’ we
mean that the source never has provided any information to the receiver before.
Thus, when a peer a receives information from peer b and b is unknown to a, then
the statement from b gets a (low) initial confidence rating. If, however b already
provided statements before to a then out of these statements the new confidence
is calculated. The value will be a weighted average where the weighting factor
is determined by semantic distance between an old statement and the new one.
The s imilarity measure we will use is adapted from [7].
Updating confidence ratings If other peers than the original sender confirm the
statement by repeating it, the statement gains higher confidence. The amount
of gain depends of the confidence in the confirming source. This recursive def-
inition of rating is also use d in PageRank in Google [8] where the rank of a
source depends on the ranks of the sources voting for that source. An important
side effect of updating the confidence ratings is that also the confidence in the
expertise of the original sender automatically increases.
Determining the experts t o be queried When a query is received, the receiver first
tries to answer the query itself. If it doesn’t have a satisfying answer, it tries to
find experts on the topic of the query. The system tries to find experts on topics
that have a close semantic distance to the topic of the query. Again, we use the
similarity measure described above for this.
Aging mechanism to devaluate confidence ratings in time A SWAP pee r can
retrieve large set of statements from other peers and from the generated state-
ments by the ontology extractor. To keep the local repository scalable, we use
an aging mechanism that removes statements that are too old in combination
with a low rating. Experiments have to adjust the right parameters for tuning
the way when and how to remove state ments.
5.2 Access
A problem of the model as described so far is the fact that accessing it becomes
quite inconvenient as the model does not only contain the actual knowledge but
also management metadata. Our solution to this problem is to provide the user
of the SWAP system with a set of definable views on the repository. The views
are defined using the management metadata, but they do not contain it any
more. Being stripped of the metadata a view can be treated like an ordinary
RDF model of a specific part of the Peers knowledge.
In the following, we describe the SeRQL query language we use to define
views over the knowledge repository of a peer. We discuss features for selecting
parts of the repository and for constructing new RDF models from these parts.
Furthermore, we describe mechanisms for persistently defining views as a basis
for providing the user with a visualization of a peers knowledge from a particular
point of view.
The SeRQL Query Language SeRQL is an RDF query language that
has been developed on the basis of experiences with implementing and using
different state-of-the-art query languages such as RQL [9] and RDQL
. The
language is currently being implemented in the Sesame System [10] which is
used to store the knowledge of peers in SWAP. The main feature of SeRQL that
go beyond the abilities of existing languages is the ability to define structured
output in terms of an RDF graph that does not necessarily coincide with the
model that has been queried. In contrast to QEL [11] it has a comprehensive
syntax. This feature is essential for defining personalized views in the repository
of a SWAP peer. Before coming back to this issue, we will first discuss
the selection mechanism used in SeRQL and its application in the context
of selecting parts of the knowledge in the repository based on attached metadata.
The selection mechanism of SeRQL is based on path expressions bor-
rowing from existing languages. RDF triples are seen as arcs in a graph
where combinations of triples form paths in that graph. Path expressions are
now expressions that describe certain types of paths, a trivial graph only
consisting of a single triple will be represented as {resource
} property
}. This path expression can already be used to extract content
from the repository. For example, we could ask for all classes in a model using
the expression {X} <rdf:type> {<rdfs:Class>} where X is a variable. In
the course of querying this variable will be instantiated with all resources in
the queried model that are of type <rfds:Class> which are returned as a result.
In order to formulate more complex selection criteria, the simple path ex-
pressions can be combined in different ways to describe subgraphs in the RDF
model. Typical examples (compare model in figure 2) are:
reification: All subclass-relations and assigned swabbies:
{{X} <rdfs:SubClassOf> {Y}} <swap:hasSwabbie> {S}
sequence: Things that have a Swabbi with the visibility set to true:
{X} <swap:hasSwabbi> {} <swap:visibility> {"true"}
split: ”visible” Swabbies with location C:/Projects/SWAP:
{X} <swap:visibility> {"true"}; <swap:location>
join: Swabbies that refer to the same location:
{X,Y} <swap:location> {Z}
The last example will also return the result where the two variables X and
Y are bound to the same res ource. In order to prevent situation like this and
to more general to define equality and inequality of variables, SeRQL allows to
specify Boolean restrictions on variable binding. In the example we could add
the restriction X != Y to state that we are only interested in pairs of non-equal
Swabbies sharing the same location. Furthermore, we could replace all directly
mentioned resources in the expressions above by variables and add restrictions
to the query that enforce these variables to be equal w ith a certain resource or
View Definitions One of the main new features of SeRQL is the ability to
not only retrieve tuples of resources that match a selection statement, but to
return a complete RDF model as a result of a query. For this purpose, a SeRQL
query can be accompanied by a construction part that specifies an RDF graph.
The specification consists of a path expression that shares some variables with
the selection part of a query. Whenever the selection part matches a subgraph
in the queried model a new RDF graph is created by instantiating the creation
part with the values that have been bound to the shared variables.
Using SeRQLs creation mechanism, we can extract certain views from the
repository of a peer. The most basic construction mechanisms is to create a
copy of the matched parts of the repository. This mechanism can be used to
extract the real knowledge from the repository without the attached metadata.
The following expression for example extracts the subclass hierarchy and the
instances from a SWAP repository:
{C} <rdf:type> {<rfds:class>}
{I} <rdf:type> {C}
{X} <rdfs:subClassOf> {Y}
One of the reasons of attaching metadata to the knowledge in the repository,
however is to provide criteria for extracting certain parts of the knowledge in a
repository. This could be the part of the knowledge that is actually trusted or the
knowledge that is provided by a certain peer. This kind of selective extraction
of knowledge needs more sophisticated construction statem ents. The following
definition s elec ts the subclass hierarchy that is provided by the external peer
with the label ’peer42’:
{X} <rdfs:subClassOf> {Y}
{{X} <rdfs:subClassOf> {Y}} <swap:hasSwabbie> {}
<swap:hasPeer> {} <swap:hasLabel> {"peer42"}
Beyond this, we also have the possibility to define a view with no direct
structural correspondence with the content of the repository by inventing new
prop e rties in the construction part. Using this possibility we can for example
create a view that describes the expertise of known peers. The following definition
for example creates a model that describes the concepts ce rtain peers know
{P} <view:knowsAbout> {C}
{{C} <rdfs:type> {<rdfs:Class>}} <swap:hasSwabbie> {}
<swap:hasPeer> {} <swap:hasLabel> {P}
Summarizing, we can say that the construction abilities of SeRQL provides
us with a powerful mechanism to extract information to be presented to the
user from the content of a SWAP repository. The metadata model chosen in the
SWAP project provides the necessary background information for the definition
of meaningful views that can assist the user in finding and assessing information.
Storing Views The first thing, we obse rve when looking at the descriptions
so far is the inability to explicitly refer to the result of query. This in fact is
a necessary requirement for the use of a query as a view as the result of a
view definition should be handled like any other RDF model. RDF models are
referred to using a namespace URI. Our first extension to SeRQL is therefore a
construct that makes it possible to ass ign a target URI to a query that represents
the virtual model created. The second important point about a view definition
is that their result should be another RDF structure. Therefore, view definitions
will always contain a construct part instead of a select part. Furthermore, we
need to be able to explicitly represent a query in an RDF repository as part of
a model. For this purpose, we propose the following simple RDF serialization of
views in SeRQL:
<serql:view ID="ExampleView"
Just an example of a view definition without content.
This simple model suffices for our purposes as it assigns an ID to a query in
the same way it is done for any other RDF resource and a target name space
that will be used to refer to the virtual RDF model that is defined by the view.
Furthermore, we include a description part that is supposed to contain a free
text description of the provided view which is mainly meant for maintenance
purp ose s. The second main part of a view definition is a query expression in the
SeRQL language. The only modification we make is to introduce RDF proper-
ties for the different parts of a SeRQL query. These properties are introduced
because they make it possible to enforce that a view definition actually contains
a construct part that can be referred to when accessing data in the view. The
values of these properties are just valid SeRQL expressions for the different parts
as defined in the SeRQL grammar. Reference to such a view definition can now
be made via its target URI. For example:
This simple introduction of an RDF container for a SeRQL query expression
already provides us with a view definition mechanism.
Visualizing Views To achieve the main goal of the SWAP system, i.e. satisfy-
ing the information needs of the users, access to information must be easy and
understandable. As seen before, the view building mechanism already offers a
flexible way to select parts of the available information and to render them in
constructs pre-defined by the user. However, these views are RDF files w hich
are very difficult to understand for human users. Visualization plays an im-
portant role in making these views understandable. The employed visualization
techniques depend on the complexity of the views. We distinguish two major
Simple Views We expect many of the views to be instantiated light-weight on-
tologies, i.e. concept hierarchies and their instances. Note that this information
(classes, their hierarchy and instances) can be obtained with SeRQL queries
such as our first query example. We think many views will be light-weight
because of two facts. First, many information systems rely on light-weight
ontologies. Also known as taxonomies, such ontologies are frequently used in
several domains (biology, chemistry, libraries) as classification systems. Infor-
mation architects consider taxonomies as basic building blocks, representing
the backbone of most web sites. Non-formal taxonomies are already widely used
in web applications for product classification (e.g. Amazon) or web-directories
(e.g. Yahoo, Open Directory Project (ODP)). Second, the information sources
of the peers are often semantically shallow: folder structure, email structures,
databases. Current ontology extraction methods are only capable of producing
light-weight ontologies from such sources.
The Cluster Map [12]
technique was specially
developed for visualizing
instantiated concept hier-
archies. Therefore we will
use it for visualizing the
simple views. A Cluster Map
depicts all the concepts,
their hierarchy as well as
their instances. Fig. 3 shows
a collection of job offers or-
ganized according to a very
simple ontology. Each small
yellow sphere represents
Fig. 3: The Cluster Map of a simple view.
an offer (an instance). The big green spheres represent ontology concepts,
with an attached label stating their name and cardinality. Directed edges
connect classes, and point from specific to generic (e.g. IT is a subclass of
Job Vacancies). Balloon-shaped edges connect objects to their most specific
class(es). Objects with the same class membership are grouped in clusters.
This visualization shows two common characteristics of instantiated tax-
onomies which are difficult to represent textually: incomplete classes and
overlaps. The set of subclasses of a class is incomplete when their union does not
contain all the objects of the superclass. Indeed, in our example, the subclasses
of the root class are incomplete as their union does not cover their superclass:
some me mbers of Vacancies were not further classified. Classes that share
instances are overlapping if no specialization relationship holds between them.
For e xample Technology and Management overlap.
Cluster Maps support several user tasks such as analysis, query and navi-
gation of semantic structures [12]. Of all these, analysis is the most beneficial
for the SWAP system. There are many ways to do analysis. First, one can
inspect the same data source from different perspectives by visualizing it
according to different views. For example, a set of web-pages describing job
vacancies can be presented through at least two views. A first view can contain
all the sub-concepts of the ”Geographic-location” concept and result in the
visualization of the vacancies according to their geographic distribution. Yet
another view can be constructed from concepts describing the economical
sector relevant for the job offer (see Fig. 3). Second, different data sets can be
visualized according to the sam e view, allowing for a comparative analysis the
data se ts. Figure 4 compares the activity profiles of two banks by visualizing
the pages on their web sites according to the same view. To get such data as
in Fig. 4 it is enough to instantiate a construct structure that extract concepts
and their instances (a simplified version of the first example query).
Fig. 4. The web-pages of two banks according to the same view.
Complex Views. By using the CONSTRUCT part of the SeRQL queries it
is possible to extract information in complex views defined by the user. This
was demonstrated in the third example SeRQL query which introduces a new
prop e rty (knowsAbout). The returned answer for this query will contain a set of
resources linked by properties, a s tructure which cannot be displayed with the
Cluster Map. Of course, this is just a simple example: SeRQL allows defining
much more complex RDF structures.
We chose two types of visualizations for depicting views with different com-
plexity: a tree-based technique is e mployed for simple concept hierarchies and
a graph-based technique is used to visualize complex RDF structures. As dis-
cussed before they both have their strength and weaknesses. As future work we
consider adapting techniques that combine the benefits of both approaches. For
example the EROS[13] system which was specially designed to depict complex
structures in such a way that (1) the concept hierarchy is visible and, meanwhile,
(2) properties can be depicted as well.
We chose two types of visualizations for depicting views with different com-
plexity: a tree-based technique is e mployed for simple concept hierarchies and
a graph-based technique is used to visualize complex RDF structures. As dis-
cussed before they both have their strength and weaknesses. As future work we
consider adapting techniques that combine the benefits of both approaches. For
example the EROS[13] system which was specially designed to depict complex
structures in such a way that (1) the concept hierarchy is visible and, meanwhile,
(2) properties can be depicted as well.
Fig. 5. The EROS interface.
6 Conclusion
The completely distributed nature and the high degree of autonomy of in-
dividual peers in a P2P system comes with new challenges for the use of
semantic descriptions. If we want to benefit from the advantages that normally
accompany the use of ontologies as specifications of a shared vocabulary we
have to find ways to dynamically align the semantic models of different peers.
In this paper, we described a model that combines features of traditional
ontologies with rich metadata about the origin of information and the reliability
of sources. Furthermore, we introduced methods for creating, assessing and
accessing semantics and metadata.
Our model has several advantages over traditional ontologies in the context
of Peer-to-Peer information exchange. The most important feature with this re-
spect is the fact that statements in the semantic models are not seen as b e ing the
truth as in most traditional models. We rather see the semantic model as a col-
lection of opinions supported by different sources of information. Opinions many
sources agree about are more likely to be true than opinions that are not shared
across the system or that even contradict other opinions. This makes it possible
to directly extract semantic models from information sources even if these are
not completely compliant with the existing model. Furthermore, we can use
heuristic methods to align and update semantic models. If the result of s uch an
update is shared by m any peers it will persist, if not it does not harm the system.
A key issue for the acceptance of such heuristic methods of course is a careful
evaluation of their performance in general and in concrete applications. In order
to evaluate the model and the methods on a general level, test procedures are
developed in the SWAP project that use simulation te chniques to experiment
with large scale Peer-to-Peer systems [14]. Furthermore, case studies in using
the SWAP system in the tourism and the finance domain are planned. These
case studies will show whether the general ideas really provide benefits in real
world applications.
One of the most fundamental questions that has to be answered by the case
studies is whether it is sufficient to rely on structures that have been extracted
from information sources instead of hand-crafted knowledge structures and meta-
data. It may turn out that in addition to the extraction approach, we also need to
annotate information by hand. In this case we have to investigate how methods
for supporting semantic annotation (e.g. [15]) can be integrated in the system
in order to build up the knowledge structures described in this paper.
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... As the tremendous growth of peer-to-peer networks for file sharing continues, the research community continues to focus on its use for knowledge sharing [12] [42] in order to exploit its further benefits. Peer-to-peer databases like Xpeer [70] and AmbientDB [31] emerge as a way for retrieving data from an unstructured and decentralized peer-to-peer network. ...
... With the growing trend of peer-to-peer networks for file sharing, there is a growing interest in using it for variety of new services including information exchange [12][42] between mobile peers. Building and maintaining peer-to-peer databases like Xpeer [70] and AmbientDB [31] enables mobile users to exchange information about each others interests and opens doors for the social networking community to build emerging information sharing applications. ...
... Dissimilar Sample Queries on ESWC 2007 Dataset12 In most simple case, our aggregation engine ignores the complete WHERE clause(s) in aggregated query except one, when two or more queries are exactly same. ...
... Broekstra et al [27] consider semantic-based P2P systems. Their main focus is placed on knowledge representation and management in query processing in P2P networks. ...
... Recently many articles, related to automated composition of (Semantic) Web services [134,200], agent technologies and P2P networks [2,11,27,37,137] (see Chapter 2 for a review) have been published. Although many of them [9,13,47,106,147] discuss a combined approach, to the best of our knowledge there are currently no systems available, which apply agent technologies to distributed composition of Semantic Web services over structured P2P networks. ...
... In recent years we have seen a range of research work on methods proposing alignments [Doan et al., 2003b, Noy andMusen, 2003]. When one tries to apply these methods to some of the real-world scenarios of other research contributions [Ehrig et al., 2003], one finds that existing alignment methods do not suit the given requirements: high quality results, efficiency, optional user-interaction, flexibility with respect to use cases, and easy adjustment and parametrization. The goal is to provide the end-user with a tool taking ontologies and returning alignments meeting these requirements. ...
... P2P recent work is marked by the use of ontology and semantics in order to overcome the problems inherent to this mode [6] [7] [8]. Our work can be classified in the field of indexing metadata in order to confer the semantic aspect to P2P DHT. ...
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