Probabilistic Temporal Process Model for Knowledge Processes: Handling a Stream of Linked Text
The paper presents an approach to modelling the data obtained from an observed environment driven by knowledge processes. It is based on the proposed a formalism for presenting probabilistic temporal process model consisting of three major components: (1) background knowledge (in the form of ontologies), (2) observed data (in the form of a stream of data items represented in different data modalities and possibly enriched with background knowledge) and, (3) objectives to optimize (providing guidelines for analytic techniques). The goal is to enable maintaining a data structure - to store, summarize and respond to a wide variety of queries about the observed low level data and about information and knowledge derived from the process. The formalism is realized in software components. Its functioning is illustrated on three scenarios: personal email, corporate email and document collections. The resulting platform is called TNT (Text-Network-Time) according to the main data modalities being addressed within the software.