SemanticWeb Mining aims at combining the two fast-developing research areas SemanticWeb andWeb Mining.
is survey analyzes the convergence of trends from both areas: an increasing number of researchers is working on
proving the results ofWeb Mining by exploiting semantic structures in theWeb, and they make use ofWeb Mining
chniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic
b itself.
e Semantic Web is the second-generation WWW, enriched by machine-processable information which supports
e user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of
ese resources. Therefore, automated schemes for learning the relevant information are increasingly being used.
b Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily
ntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore,
rmalizations of the semantics of Web sites and navigation behavior are becoming more and more common.
rthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web
ning and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not
t realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer
tegration could be profitable.

SemanticWeb Mining aims at combining the two fast-developing research areas SemanticWeb andWeb Mining.
is survey analyzes the convergence of trends from both areas: an increasing number of researchers is working on
proving the results ofWeb Mining by exploiting semantic structures in theWeb, and they make use ofWeb Mining
chniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic
b itself.
e Semantic Web is the second-generation WWW, enriched by machine-processable information which supports
e user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of
ese resources. Therefore, automated schemes for learning the relevant information are increasingly being used.
b Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily
ntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore,
rmalizations of the semantics of Web sites and navigation behavior are becoming more and more common.
rthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web
ning and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not
t realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer
tegration could be profitable.

The purpose of Web mining is to develop methods and systems for discovering models of objects and processes on the World Wide Web and for web-based systems that show adaptive performance. Web Mining integrates three parent areas: Data Mining (we use this term here also for the closely related areas of Machine Learning and Knowledge Discovery), Internet technology and World Wide Web, and for the more recent Semantic Web. The World Wide Web has made an enormous amount of information electronically accessible. The use of email, news and markup languages like HTML allow users to publish and read documents at a world-wide scale and to communicate via chat connections, including information in the form of images and voice records. The HTTP protocol that enables access to documents over the network via Web browsers created an immense improvement in communication and access to information. For some years these possibilities were used mostly in the scientific world but recent years have seen an immense growth in popularity, supported by the wide availability of computers and broadband communication. The use of the internet for other tasks than finding information and direct communication is increasing, as can be seen from the interest in ldquoe-activitiesrdquo such as e-commerce, e-learning, e-government, e-science.

The purpose of Web mining is to develop methods and systems for discovering models of objects and processes on the World Wide Web and for web-based systems that show adaptive performance. Web Mining integrates three parent areas: Data Mining (we use this term here also for the closely related areas of Machine Learning and Knowledge Discovery), Internet technology and World Wide Web, and for the more recent Semantic Web. The World Wide Web has made an enormous amount of information electronically accessible. The use of email, news and markup languages like HTML allow users to publish and read documents at a world-wide scale and to communicate via chat connections, including information in the form of images and voice records. The HTTP protocol that enables access to documents over the network via Web browsers created an immense improvement in communication and access to information. For some years these possibilities were used mostly in the scientific world but recent years have seen an immense growth in popularity, supported by the wide availability of computers and broadband communication. The use of the internet for other tasks than finding information and direct communication is increasing, as can be seen from the interest in ldquoe-activitiesrdquo such as e-commerce, e-learning, e-government, e-science.

Semantic Web Mining aims at combining the two fast-developing
search areas Semantic Web and Web Mining.
b Mining aims at discovering insights about the meaning of Web
sources and their usage. Given the primarily syntactical nature
data Web mining operates on, the discovery of meaning is
possible based on these data only. Therefore, formalizations of
e semantics of Web resources and navigation behavior are
creasingly being used. This fits exactly with the aims of the
mantic Web: the Semantic Web enriches the WWW by
chine-processable information which supports the user in his
sks. In this paper, we discuss the interplay of the Semantic Web
th Web Mining, with a specific focus on usage mining.

Semantic Web Mining aims at combining the two fast-developing
search areas Semantic Web and Web Mining.
b Mining aims at discovering insights about the meaning of Web
sources and their usage. Given the primarily syntactical nature
data Web mining operates on, the discovery of meaning is
possible based on these data only. Therefore, formalizations of
e semantics of Web resources and navigation behavior are
creasingly being used. This fits exactly with the aims of the
mantic Web: the Semantic Web enriches the WWW by
chine-processable information which supports the user in his
sks. In this paper, we discuss the interplay of the Semantic Web
th Web Mining, with a specific focus on usage mining.

Semantic Web Mining aims at combining the two fast-developing
search areas Semantic Web and Web Mining.
b Mining aims at discovering insights about the meaning of Web
sources and their usage. Given the primarily syntactical nature
data Web mining operates on, the discovery of meaning is
possible based on these data only. Therefore, formalizations of
e semantics of Web resources and navigation behavior are
creasingly being used. This fits exactly with the aims of the
mantic Web: the Semantic Web enriches the WWW by
chine-processable information which supports the user in his
sks. In this paper, we discuss the interplay of the Semantic Web
th Web Mining, with a specific focus on usage mining.