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Adapting web sites by spreading activation in ontologies

In this paper we introduce SPREADR, a model-based technique for creating context-adaptive web applications. In this approach, the domain knowledge as well as context factors are represented by an ontology. Context factors depend on the application domain and may include location, time, user role, weather or any other relevant context information, often information which can automatically be sensed. Both domain objects and context factors are treated as concepts or instances in the ontology, which can be linked to each other, thus forming a semantic network. In addition to this representation, we introduce scalar activation levels for concepts as well as weights for relations. Concept activation represents the level of user interest whereas activation of context factors can be seen as the level of fulfilment based on some measurement. The structure of the semantic network remains the same for each user while activation levels may differ. Recognition of context factors or user actions trigger an activation flow through the network, thus increasing the activation of contextually important nodes. Relations are associated with different weights resulting in different amounts of activation spreading along different relations. The resulting spreading activation network can be used both for generating a web site as well as for controlling the adaptive behaviour of the system. Adaptation may include effects such as sorting navigation items by relevance, showing or hiding information depending on the activation value and highlighting or recommending important items. The system is also able to learn user preferences through implicit feedback. We present a demonstrator application as well as initial evaluation results.