Name
Abstract | ||
|---|---|---|
Information fusion exists over many forms of hard data (e.g. from physical sensors) and soft data (e.g. from human reports) to interpret observations of real-world objects. As demonstrated from the Geographical Information Systems (GIS) community, there is a growing need for the linking and alignment of both (1) exploited physical imagery products and (2) derived ontological textual labels (semantic markup). Semantic markup can be done on both exploited data (e.g. automated image segmentation), as well as user reports (e.g. weather forecasts). Since the derived information is collected, stored, and displayed into distinct ontological structures by different agencies; ontological alignment is thus required whenever the semantic information is paired with distinct real-world imagery observations. In this paper, we explore issues of fusing hard and soft data as related to ontology alignment. A maritime domain situational awareness example with geographical imagery and textural ontologies is shown to demonstrate the need for ontology alignment to assist users for pragmatic surveillance. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICIF.2010.5712081 | Information Fusion |
Keywords | Field | DocType |
geographic information systems,geophysical image processing,image fusion,ontologies (artificial intelligence),surveillance,GIS,geographical hard-soft information fusion system,geographical imagery,geographical information system,hard data,ontological textual label,physical imagery product,pragmatic surveillance,real-world imagery,semantic markup,soft data,Fusion,Situational Assessment,hard-soft fusion,ontology alignment | Ontology (information science),Information system,Geographic information system,Ontology,Ontology alignment,Information retrieval,Computer science,Image segmentation,Semantic HTML,Semantics | Conference |
ISBN | Citations | PageRank |
978-0-9824438-1-1 | 4 | 0.39 |
References | Authors | |
17 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Erik Blasch | 1 | 1051 | 90.91 |
| Eric Dorion | 2 | 14 | 1.70 |
| Pierre Valin | 3 | 97 | 13.08 |
| Éloi Bossé | 4 | 386 | 26.19 |
| Jean Roy | 5 | 4 | 0.39 |