WEB

CONTENTS AND STRUCTURAL ADAPTIVITY BY

KNOWLEDGE TREE

The Herculaneum Excavation Hypermedia

Silvia Rossi

, Vincenzo Scognamiglio

and Ernesto Burattini

Dip. di Scienze Fisiche, University of Naples ”Federico II”, Via Cinthia, I80126 Naples, Italy

Centro di Ateneo per i Servizi Informativi, University of Naples ”Federico II”

Keywords:

Web Personalization, User Modeling, Metamodeling.

Abstract:

The aim of this work is the design and the implementation of a prototype for supporting a user through the

navigation in a web-site. We exploit the potentiality of having a representation of an hypermedia based on the

concept of an abstract semantic description of the knowledge tree, in order to use some adaptivity processes,

typical of many hypermedia systems, without the limitation of having a ﬁxed set of resources. Our prototype

was implemented for the archaeological Herculaneum Excavations, but the novelty of this work is an attempt to

combine methodologies coming from Adaptive Hypermedia, Recommendation Systems and Semantic Web in

order to have a context-independent approach for Hypermedia on the Web. What we propose is an architecture

with modules that can be reused and adapted in many different contexts. In order to have a context independent

behavior the system was implemented with a structural adaptivity itself. This means that changing the tree the

system adapts itself to the modiﬁcations and dynamically regenerates the code of its components.

1 INTRODUCTION

Nowadays several sites offer personalized portals that

can be customized by the user and may even adapt

to his interest automatically. In this framework there

are many applications that include personal guides for

navigation or ambient devices, portals, e-commerce

Web-sites (e.g. Amazon, Expedia), or recommender

sites (e.g MovieLens).

Usually, in web applications, one can identify two

different classes of adaptivity. In the ﬁrst class, called

Adaptive Hypermedia, the adaptivity process is gen-

erated ad hoc for a speciﬁc system. Such systems

have precise information on a ﬁxed structure of re-

sources, expressed as the content of each resource

and the relationships between them (Goldstein, 1979).

These solutions are hardly generalizable for re-use

in different applications. A reason why this adap-

tive functionality is not re-usable is related to the so–

called open corpus problem in adaptive hypermedia

(Brusilovsky, 2004). Adaptive Hypermedia Systems

are realized by experts that decide how to connect all

the information. That makes difﬁcult to add new ma-

terial and to connect it.

In the second class, that uses Web Mining tech-

niques, the adaptivity is created in order to operate

in a network of Web resources. The are many ap-

proaches for creating adaptive web sites (Perkowitz

and Etzioni, 2001), but the only adaptive systems

that work successfully are the so called Web Recom-

mendation Systems (Brusilovsky, 2004). Those sys-

tems may exploit adaptation starting from the word-

level content of a resource (Content-based ﬁltering)

or recording information of other users with similar

interests (collaborative ﬁltering (Rashid et al., 2002)).

However the adaptivity process in those systems con-

sists in a list of recommended links that may be or-

dered according to some ratings. Moreover, an im-

portant aspect of Semantic Web (Berners-Lee et al.,

2001) is to obtain more and more weight for building

effective personalization in web applications. A mod-

ern system, in order to show ﬂexibility and a context

independent behavior, needs to have a semantic de-

scription of resources. Such description may not be

bound to each single resource but can be expressed in

a more abstract way.

The aim of this work is the design and the imple-

mentation of a prototype for supporting a user through

270

Rossi S., Scognamiglio V. and Burattini E. (2007).

WEB CONTENTS AND STRUCTURAL ADAPTIVITY BY KNOWLEDGE TREE - The Herculaneum Excavation Hypermedia.

In Proceedings of the Third International Conference on Web Information Systems and Technologies - Web Interfaces and Applications, pages 270-275

DOI: 10.5220/0001283402700275

 SciTePress

the navigation in a web-site. The adaptivity process is

obtained without asking any information to the user

that has to interact with the system as a common

web-site. Our prototype was implemented for the ar-

chaeological Herculaneum Excavations

(Sec.2), but

the novelty of this work is the attempt to combine

methodologies coming from Adaptive Hypermedia,

Recommendation Systems and Semantic Web in or-

der to have a context-independent approach that man-

ages Hypermedia on the web. WyW06 – (Whatever)

Web You Want – was born as general purpose adap-

tive system based on user modelling that can be used

as an information system on a huge set of data, but

in his evolution it becomes different from a classic

adaptive system, getting much powerful functionali-

ties typical of recommendation systems. In Sec.2.1

we will introduce our way to represent the knowledge

of a hypermedia, based on semantic description of the

knowledge tree, while the representation of resources

and of user models will be described in Sec.2.2 and

Sec.2.3. In Sec.2.4 and Sec.3 we will introduce the

adaptivity process in WyW06 and we will describe

how the system works.

2 THE HERCULANEUM

EXCAVATIONS HYPERMEDIA

The Herculaneum Excavations Hypermedia comes

from a stand-alone system by our research group

(Burattini et al., 1999). Herculaneum, as Pompeii,

was destroyed in A.D. 79 the 24th August by heavy

shower of lava from the Vesuvius volcano. The

knowledge base contains many information includ-

ing historical data and mythological tales, excavation

reports, description of recovered buildings and ob-

jects, painting and so on. This information comes

under the form of texts, videos, images, contempo-

rary and old maps. The knowledge was appropri-

ately linked – following the experts’ suggestions –

and the results of this analysis have been summarized

in a tree where single chunks of knowledge are iden-

tiﬁed (see Sec.2.1). A root node represents one of the

main points of view from which the knowledge on

Herculaneum Excavations may be explored and sub-

nodes show the ways in which such knowledge may

expanded. This ﬁrst implementation has shown that,

even when a great amount of information is managed,

the system can be used only from a narrow class of

domain expert users since it is unable to adapt its in-

formation to different kinds of users. The current sys-

tem, realized as a web site, is intended for users whose

http://www.ercolano.unina.it

proﬁles can be different, such as archaeologists, his-

torians, onlookers or just tourists.

2.1 A Semantic Representation of the

Knowledge Tree

One of the main reasons for the not re-usability of

many adaptive system is the so-called open corpus

problem. First of all, we have to remark that each

adaptive hypermedia, as any hypermedia system, has

to make some assumptions about documents and their

relationships in a document space. Many of the adap-

tive applications work on a ﬁxed set of documents that

are deﬁned and linked at design time. The process of

adding a resource in the system always requires the

agency of domain experts and makes more complex

the maintenance of the system.

What we propose is an architecture that is context

independent and has modules that can be reused and

adapted. Our system has no a priori knowledge of the

web contents, but is able to manage a high level rep-

resentation deﬁned on this set. The only requirement

we impose is that the resources are expressed using

some semantic annotation (see Sec.2.2) and that ex-

ists a high level representation in terms of a knowl-

edge tree. Web pages or, more generally, any hy-

permedia content, are generated dynamically starting

from those two representations. When the system has

to organize the data in order to provide a chunk of

information, or to create a web page, it collects the

appropriate contents from the database.

In order to clarify this point we have to make a

distinction among the concept of resource class and

the concept of resource instance. A content element

in the database represents a resource instance. For

example, in the case of the Herculaneum Excavation,

a resource instance may be the ”excavation report” of

a particular building or the ”description of a fresco”.

Another example of a resource instance in a different

context, such as travel recommendation systems, may

be the information of a speciﬁc hotel. Such instance

gets the semantic description of its class.

A resource class speciﬁes a set of resource in-

stances that have similar semantic properties (see Fig.

1(d)). All the resource classes are semantically inter-

connected in order to represent the knowledge tree.

For example, in the case of the Herculaneum Excava-

tion, the abstract class building is connected to the

class grafﬁti in the sense that a building may have

grafﬁti. Another example concerning travel recom-

mendation system may be the abstract class town con-

nected to the class hotels, to the class tours, and so on.

At this point we claim that this type of abstract

knowledge representation can be easily used in many

WEB CONTENTS AND STRUCTURAL ADAPTIVITY BY KNOWLEDGE TREE - The Herculaneum Excavation

Hypermedia

271

Figure 1: (a) Virtual relationships generated by a knowledge

tree that is inserted in the database;

(b) To an instantiated tree corresponds a subset of resources;

instantiated tree;

(d) To more resources in the database may correspond the

same node in the knowledge tree.

web applications. For example any recommendation

system may be enhanced by the use of this seman-

tic structure. Those systems, in fact, already have, in

an implicit way, a semantic representation of how the

global knowledge has to be organized and proposed.

The knowledge tree deﬁnes in an explicit way also the

resources and so the contents that we want to person-

alize and adapt to the user (see Fig.1). For instance

some of the nodes that are the leaves of the tree rep-

resent a more speciﬁc and technical content that can

be hidden for a user that is not an expert in the ﬁeld.

Starting from the description of the knowledge tree

the system can make a search on the database and can

make different instances of the tree using the avail-

able resources. In this way we can create virtual re-

lationships among the resources in the database that

are not ﬁxed a priori. Any modiﬁcation of the knowl-

edge tree will automatically imply a modiﬁcation of

the virtual links among the resources and the process

of adding a new resource does not need the interven-

tion of domain expert to connect it to the others. In

fact when a user requests a web page the system will

dynamically instantiate the correspondent virtual tree

and recognize all the new resources. The resource

tree for the Herculaneum Excavations is represented

in Fig.2 where each node represents a resource class.

2.2 Representation of the Resources

With the help of a group of architects, historians and

archaeologists we detected the main properties that

may characterize any information content of our hy-

permedia. Those features are related to the types of

informative content that a resource may have and to

the interests that a resource may arise. For example, a

feature is the historical content - i.e. how much a re-

source class contains contents coming from historical

data. A resource class is a vector

−→

r = (w

, ..., w

) in

the space R

, where n is the number of characteris-

Figure 2: The knowledge tree for the Herculaneum Excava-

tions Hypermedia.

tics or features (w) and the orthogonal base is repre-

sented by the vectors that has a single feature with

value 100% and the other features with value 0%.

The vector model of a resource class represents how

much every speciﬁc characteristic is present in the

resource (see Fig.3). Differently from the common

usage in data mining the vector, that represents a re-

source, does not contain the frequency of occurrence

of some speciﬁc words within the text, but represents

how much a speciﬁc text can be classiﬁed according

to some typologies of texts. This can be interpreted

also as an analysis of the semantics of the keywords

within the text.

Once the vectors for the resource classes are de-

ﬁned, any resource instance will inherit the model

from its correspondent class; this means that all the

instances of a class have the same semantic proper-

ties. A resource is an atomic and content-closed in-

formative kernel. Each hypermedia page is viewed as

speciﬁc subset of resources (see Sec.3).

<?xml version="1.0"?> <rdf:RDF xmlns:rdf=

"http://www.w3.org/1999/02/22-rdf-syntax-ns#"

xmlns:cd="http://www.ercolano.unina.it/modelli#">

<rdf:Description rdf:about=

"http://www.ercolano.unina.it/modelli/ModelloRisorsa">

<models:context>Archeological Cultural Heritage</context>

<models:nameOfResource>History of Excavations - archive

reports</nameOfResource>

<models:topographical>100</topographical>

<models:typological>80</typological>

<models:planimetric>0</planimetric>

<models:informative>0</informative>

<models:historical>100</historical>

<models:decorative>0</decorative>

...

<models:technological>0</technological>

</rdf:Description> </rdf:RDF>

Figure 3: An example of a vector class in RDF.

In this work we are not dealing with the prob-

lem of resources’ classiﬁcation. Resource models are

created by domain-knowledge experts and the addi-

WEBIST 2007 - International Conference on Web Information Systems and Technologies

272

tion of a node in knowledge tree implies the necessity

of deﬁning a new resource model. Resource models

are static through the user interaction but can be eas-

ily changed by administrator that only has to update

the resource model. The system automatically re-

loads each redeﬁnition when new user session starts.

Also the creation of new hypermedia pages, as con-

tainers of resources, is obtained dynamically by the

system just using the relationships described in the

knowledge-tree.

2.3 User Model

In many classic adaptive hypermedia systems the

classiﬁcation of the user is made through the use of

stereotypes (Kobsa, 1993), or by clustering a user

with a group of people that has shown the same be-

havior during past interactions. In the ﬁrst case,

stereotypes must be incorporated in the system in the

designing phase and, during the execution, the system

will have to recognize the correct stereotype (Rich,

1999). In the second case the algorithms for cluster-

ing and the adaptivity process – i.e. the recommen-

dation process – are exploit just recording the already

visited web pages or the rankings made by previous

users and they need a large amount of data in order to

be effective.

Taking the advantage of the semantic descriptions

of resources and of the knowledge tree, we decided to

implement a user model that takes into account only

the current behavior of the user starting from the spec-

iﬁcation of the behaviors of ”ideal” users. What is

different from the classical use of stereotypes is that

we do not try to classify a user in a speciﬁc class but

we start from the assumption that a real use may ex-

hibit a behavior that is a combination of ideal classes.

An ideal users’ class is the representation of a set of

users, like a prototype, whose behaviors and interests

are formally deﬁned. It is fundamental that each ideal

class of users does not have any behavior that is in

common with another ideal class, i.e. the vectors rep-

resenting ideal classes are an orthogonal base.

A user model is a vector

−→

u in the space of ideal

user classes. When a user session start, the user model

vector components are set to zero. During the user in-

teraction, the choices made by the user lead the sys-

tem to change the values of the user model. The user

model vectors in WyW06 are represented as couples

attribute–value and such values represent the percent-

age of similarity of the user model to an ideal user

class (see Fig.4). This is to say that the user model

−→

of the user i is a linear combination of ideal user’s

models (

−→

), such as

−→

= α

−→

, β

−→

, ..., γ

−→

, where

Greek letters represent percentages.

The content-independency of the system, obtained

for resource classes, is kept also in the management

of the user model. In WyW06 this vector represents a

user model in an explicit way, starting from the deﬁn-

ition of ideal classes. The modiﬁcation or the adding

of a new ideal class implies an automatic adaptation

of the system to the new conﬁguration, without the

need of implementing new code since it is generated

by the system itself.

<?xml version="1.0"?> <rdf:RDF

xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#

"xmlns:cd="http://www.ercolano.unina.it/modelli#">

<rdf:Description rdf:about=

"http://www.ercolano.unina.it/modelli/Modelloutente">

<models:context>Archeological Cultural Heritage</context>

<models:archaeologist>90</archaeologist>

<models:architect>25</architect>

<models:historian>62</historian>

...

<models:tourist>70</tourist>

</rdf:Description> </rdf:RDF>

Figure 4: An example of a user model vector in RDF.

2.4 Extension and Association Rules:

Adaptivity

Up to now, we described the user model and the re-

source model as two distinct concepts, without men-

tioning how to go from one representation to the other.

The user model changes according to the choices that

the user made browsing the resources, and the adap-

tivity process on resources depends on the current

user model. In Sec.2.2 we proposed the user model

as a composition of orthogonal ideal user classes. As-

sociated with each ideal class there is the speciﬁcation

of the ”interests” of an ideal user. This speciﬁcation

is made by using association rules. In the other way

the updating of the user model is made though the ap-

plication of extension rules – e.g. rules that specify

how to modify the user model after a speciﬁc event.

Usually these rules are speciﬁed using statistical

methodologies on real data. In our case an associa-

tion rule is represented by the deﬁnition of a resource

class (

−→

) considered optimal for an ideal user (

−→

Such class is the one that has the typological content

more similar to the user interests. Moreover, the re-

sources, selected to be representative for the ideal user

models, are linearly independent, in order to respect

the fundamental assumption that the ideal users rep-

resent an orthogonal base (see Sec.2.3). To verify the

consistency of the user model representation, the sys-

tem, whenever there is a change in the ideal users’

speciﬁcations, calculates scalar products among the

resources associate to the ideal users and veriﬁes that

they are orthogonal.

WEB CONTENTS AND STRUCTURAL ADAPTIVITY BY KNOWLEDGE TREE - The Herculaneum Excavation

Hypermedia

273

When a user requires a new hypermedia page, the

system takes the user model, computed during the in-

teraction up to that moment, and calculates the ideal

resource for the current user (

−→

). Then it compares

a distance (di f f ) respect to each real resource (

−→

) in

the selected page and, using a threshold for the ob-

tained distance, makes decision about which content

of the resource it has to show or hide. This process

is summarized in the following algorithm. Moreover

we want to remark that an ideal resource may not cor-

respond to a real resource.

Algorithm 1 DIFFERENCE(

−→

)

 user model

−→

, resource class

−→



−→

= (w

, ..., w

)



−→

= (α

−→

, β

−→

, ..., γ

−→

)

1 for j = 1, j = n, j + +

2 r

i j

= max(αr

1 j

, βr

2 j

, ..., γr

m j

)

 r

i j

is the j component of the ideal resource

−→

r for the user i. r

i j

is the maximum value of all the j components of the resources

−→

associated to the ideal classes of users

−→

and multiplied by the

same factors as in the user model

3 di f f = 0

4 for all

−→

secondaryNodesO f (

−→

)

 For all the secondary children of the node

for j = 1, j = n, j + +

 For all the features of a node

5 if (r

i j

> r

k j

)

 The user is not satisﬁed

6 di f f = di f f + 1

7 distance = 100 − (

di f f

∗ 100)

 Compute the percentage of ﬁelds that are not satisﬁed

8 if (distance <= threshold)

9 show(

−→

)

10 else

11 hide(

−→

)

The metrics we use to compute the distance starts

from the assumption that a feature of a real resource

”satisﬁes” the user interests if his value is equal or

greater than the value of the same feature in the ideal

resource. This is to say that the real resource is ap-

pealing for the user at least for this speciﬁc feature.

For example if one has an interest of 20% for the his-

torical property, every real resource with the histori-

cal feature greater than 20% will satisfy his interest.

For each real resource, within a web page, the sys-

tem computes how many features do not satisfy the

user interests. If this number is low the content will

be shown otherwise it will be hidden. In future work

we are planning an evaluation of this metrics. It is im-

portant to highlight that the system lets the user free

to show the content of resources considered not in-

teresting for him and to hide the content of resource

automatically showed.

Finally, as we said, associated with each resource

there is an extension rule that speciﬁes how to changes

the user model when the resource is activated. This

rule refers to the nodes of knowledge–tree but not

Figure 5: A snapshot of the prototype of the Herculaneum

Excavation web hypermedia.

to the resource instances. The adaptivity process in

WyW06 is achieved just by analyzing the user’s in-

teractions with the system environment without any

explicit questions about preferences.

3 SYSTEM AT WORK

WyW06 introduced a new and potentially powerful

layer of adaptation: the structural-adaptation. This

structural adaptation refers to the ability of a system

to create or regenerate some of his modules, dynami-

cally and autonomously, when some speciﬁc parame-

ters are changed.

To translate the tree in a hypermedia with his rel-

ative hyper–linked structure, WyW06 makes a fun-

damental and dynamical distinction between main

nodes and secondary nodes. How to make this choice

is relatively important, anyway, the current choice is

to evaluate the number of child of a speciﬁc node. If

it is smaller than a deﬁned threshold then this node is

classiﬁed as secondary, a main node otherwise. The

addition of new instances of a resource does not re-

quire neither a complex intervention of system man-

agers – the system automatically integrates the new

resource – nor the intervention of knowledge-domain

expert – the semantic of any new element refers to his

speciﬁc class model. The addition of a new node in

the tree is not absolutely an issue for the system that

regenerates parts of code of his modules and the hy-

pertext structures without other interventions.

At the beginning of a session each main resource

will appear as a web page with his main resource con-

tent and the list of resources that are children of the

main. If a main resource is a child of another, it will

appear as a link to another web page in the left box

(see Fig.5). If a secondary resource is child of the

main resource, it will appear as a expandable text

With the term expandable text we mean a subsection of

WEBIST 2007 - International Conference on Web Information Systems and Technologies

274

(see Fig.5) that user can expand in every moment.

When user expands or hides a secondary resource, its

associated extension rule is applied to the user model.

When the user model is sufﬁciently deﬁned for the

system criteria and a user accesses to a main resource,

the system automatically expands the content of sec-

ondary resources that are considered interesting for

him. The system also provides to the user the pos-

sibility of saving resources as personal notes. This

action also implies the application of extension rules

to the user model.

For obtain the multilevel adaptation we imple-

mented the system using different language on server-

side and client-side. We used JavaScript combined

with Cascade Style Sheets (CSS) to realize the dy-

namic update of the ﬁelds of the user-model, resulting

by the application of extension rules, and the user’s

choice about viewing/hiding resources on client-side.

For the server-side, we used the PHP language to

obtain the dynamic generation by the server of the

contents–selection for the web pages. The PHP code

has the important role of applying the association

rules in order to obtain the user-model transformation.

The structural adaptation is made representing web

site structure, and relative resources, by knowledge-

tree. The real resources, the rules and the knowledge-

tree are stored in a database set up in MySQL envi-

ronment managed dynamically by PHP code.

4 DISCUSSION

WyW06 was born as general purpose adaptive sys-

tem based on user model. Like classic adaptive sys-

tem WyW06 has the aim of supporting individual

user in ﬁnding, selecting and managing content of-

fered by web sites. In his evolution WyW06 becomes

quickly more than a classic adaptive system, getting

much functionality typical of recommendation sys-

tems, melting the advantages of both those typolo-

gies of systems. WyW06 is based on the point of

view that each single web page is like a collection of

resources. A resource is an atomic informative ker-

nel semantically linked to other resources by a spe-

ciﬁc tree of relationships. The use of a tree to spec-

ify relationships between resources is a peculiar char-

acterization of adaptive hypermedia systems (Burke,

2002). In the adaptive hypermedia a common prob-

lem is the adding of a node-resource in the system.

As we said in section 2.1 to overcome this obstacle,

WyW06 introduced a new and potentially powerful

layer of adaptation: the structural-adaptation. The

the web page that can be hidden or shown by a click on a

button.

structural adaptation refers to the ability of system

to create or regenerate some of his modules, dynam-

ically and autonomously, when some speciﬁc para-

meters are changed. Respect to classical deﬁnitions

in adaptive systems WyW06 is a user model based

system. The adaptive behavior of the system aims

to satisfy automatically the desires of the user but,

it is important to highlight that, lets the user free to

access to the resources that are not near to the self-

selection threshold. The use of such techniques leads

the system easily to become a recommendation sys-

tem. In fact the system evaluates the secondary re-

sources that are of more interest respect to the ac-

tual state of the user model and propose them to the

user. Summarizing we can say that WyW06 has a be-

havior comparable to an adaptive hypermedia, some

peculiarity of content-based recommendation system,

some characteristics of inferential reasoning by using

rules schemas, some fundamental notion for Seman-

tic Web and something innovative respect all, that is

the structural adaptive layer.

REFERENCES

Berners-Lee, T., Hendler, J., and Lassila, O. (2001). The

semantic web. Scientiﬁc Am., pages 34–43.

Brusilovsky, P. (2004). Adaptive navigation support: From

adaptive hypermedia to the adaptive web and beyond.

Psychnology, 2(1):7–23.

Burattini, E., Gaudino, F., and Serino, L. (1999). Hyper-

media knowledge acquisition and a bdi agent for nav-

igation assistance. a case study: Herculaneum exca-

vations. In Europ. Conf. on Cognitive Science, pages

437–440.

Burke, R. (2002). Hybrid recommender systems: Survey

and experiments. User Modeling and User-Adapted

Interaction, 12(4):331–370.

Goldstein, I. P. (1979). The genetic graph: A representation

for the evolution of procedural knowledge. Interna-

tional Journal of Man-Machine Studies, 11:51–77.

Kobsa, A. (1993). User modeling: Recent work, prospects

and hazards. In Adaptive User Interfaces: Principles

and Practice, pages 111–128. North-Holland, Ams-

terdam.

Perkowitz, M. and Etzioni, O. (2001). Adaptive web sites:

Concept and case study. Artiﬁcial Intelligence, 118(1–

2):245–275.

Rashid, A. M., Albert, I., Cosley, D., Lam, S. K., McNee,

S. M., Konstan, J. A., and Riedl, J. (2002). Getting

to know you: learning new user preferences in rec-

ommender systems. In IUI ’02: Proc. of the 7th int.

conf. on Intelligent user interfaces, pages 127–134,

New York, NY, USA. ACM Press.

Rich, E. (1999). Users are individuals: individualizing user

models. International Journal of Human Computer

Studies, 51(2):323–338.

WEB CONTENTS AND STRUCTURAL ADAPTIVITY BY KNOWLEDGE TREE - The Herculaneum Excavation

Hypermedia

275