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{{Publikation Details | {{Publikation Details | ||
− | |Download=JH paper 2013.pdf, | + | |Abstract=Large amounts of data are being produced daily |
+ | as detailed records of Web usage behavior, but the | ||
+ | task of deriving actionable knowledge from them | ||
+ | remains a challenge. Investigations of user browsing | ||
+ | behavior at multiple websites, while more beneficial | ||
+ | than studies restricted to a single site, still need to | ||
+ | tackle the problems of information heterogeneity and | ||
+ | mapping usage logs to meaningful events from the | ||
+ | application domain. | ||
+ | |||
+ | Focusing on the problem of modeling cross-site | ||
+ | browsing behavior, we present a formalization approach | ||
+ | based on a Web browsing Activity Model | ||
+ | (WAM). We introduce a novel two-staged approach | ||
+ | for the semantic enrichment of usage logs with | ||
+ | domain knowledge, bringing together Semantic Web | ||
+ | technologies and Machine Learning techniques. | ||
+ | For learning the semantic types of logs, we present | ||
+ | a supervised multi-class classification formulation, | ||
+ | deploying structural Support Vector Machines with | ||
+ | new sequential input features. | ||
+ | |||
+ | We provide an implementation | ||
+ | of these approaches and show the results | ||
+ | of evaluation with real-world data. | ||
+ | |Download=JH paper 2013.pdf, | ||
|Forschungsgruppe=Wissensmanagement | |Forschungsgruppe=Wissensmanagement | ||
}} | }} |
Aktuelle Version vom 23. Mai 2013, 08:49 Uhr
Published: 2012
November
Type: Research Technical Report
Institution: Institut AIFB, KIT
Erscheinungsort / Ort: Karlsruhe
Archivierungsnummer:3025
Kurzfassung
Large amounts of data are being produced daily
as detailed records of Web usage behavior, but the
task of deriving actionable knowledge from them
remains a challenge. Investigations of user browsing
behavior at multiple websites, while more beneficial
than studies restricted to a single site, still need to
tackle the problems of information heterogeneity and
mapping usage logs to meaningful events from the
application domain.
Focusing on the problem of modeling cross-site browsing behavior, we present a formalization approach based on a Web browsing Activity Model (WAM). We introduce a novel two-staged approach for the semantic enrichment of usage logs with domain knowledge, bringing together Semantic Web technologies and Machine Learning techniques. For learning the semantic types of logs, we present a supervised multi-class classification formulation, deploying structural Support Vector Machines with new sequential input features.
We provide an implementation
of these approaches and show the results
of evaluation with real-world data.
Download: Media:JH paper 2013.pdf