Semantic Knowledge Representations for Soft Data Fusion

Abstract

ion is the process of highlighting some of the aspects of a thing in order to grasp its characteristics. It is somehow a process of generalization. Abstracting an observable thing leads to a general representation of this reality, which is often called a concept. Data items are unprocessed and uninterpreted symbols. They are elementary descriptions of measurable properties. Information is what data items become once they have been interpreted and contextualized so to become useful within a specific objective and for a specific user. Having information is “knowing what is happening”. The information answers to questions such as “who?”, “what?”, “where?” and “when?” Knowledge is a combination of information with experience and judgment. It allows reasoning among information and interpreting data in order to create new data and information items. The knowledge answers to the question “How?”. In the specific case of fusion, the notions of data, information and knowledge are also linked one to another within the process of abstraction (see Figure 1). The aim of information and data fusion is to have a representation of an external situation. This representation can be built thanks to observations of the external situation that are acquired through sensors and reported to fusion systems. Sensors are either low-level physical sensors, that report about physical measurements such as temperature or speed, or human observers that report about (some parts of) complex situations. In the first case, the physical sensors give a set of data that must be interpreted. The human sensors, on the contrary, provide interpreted information. Combining all the information items in order to deduce new information and pieces of knowledge is the role of the information fusion systems. Both data and information fusion systems use domain knowledge in order to interpret and combine the data and information items, according to a specific aim and within a specific context. Domain knowledge is also used in order to solve inconsistencies and discrepancies among the data and information items. 2.2 Soft data: a new challenge for decision support systems “Soft data” items are observations that are generated by humans. They may be expressed as unconstrained natural language (see Sambhoos et al. (2008)), through textual data or speech signal, but can also be made of semi constrained data items such as xml files or data bases, which are keyed in by humans through forms for instance. As soft data is provided by 4 Efficient Decision Support Systems – Practice and Challenges From Current o Future