# Uncertainty In Ontologies: Dempster-Shafer Theory For Data Fusion Applications Amandine Bellenger1, Sylvain Gatepaille1

1EADS, Defence and Security Information Processing, Control and Cognition department Parc d'Affaires des Portes - 27106 Val-de-Reuil - France 
{amandine.bellenger, sylvain.gatepaille}@eads.com Abstract—**Nowadays ontologies present a growing interest in** 
Data Fusion applications. As a matter of fact, the ontologies are seen as a semantic tool for describing and reasoning about sensor data, objects, relations and general domain theories. In addition, uncertainty is perhaps one of the most important characteristics of the data and information handled by Data Fusion. However, the fundamental nature of ontologies implies that ontologies describe only asserted and veracious facts of the world. Different probabilistic, fuzzy and evidential approaches already exist to fill this gap; this paper recaps the most popular tools. However none of the tools meets exactly our purposes. Therefore, we constructed a Dempster-Shafer ontology that can be imported into any specific domain ontology and that enables us to instantiate it in an uncertain manner. We also developed a Java application that enables reasoning about these uncertain ontological instances. 

## Keywords: Ontologies, Uncertainty, Belief Functions, Data Fusion. I. Introduction

An ontology contains the concepts used to describe and represent an area of knowledge. By clearly defining semantically a group of terms in the given domain and the relationships among them, without any ambiguity, an ontology encodes the knowledge of the domain in such a way that it can be understood by a computer. 

For this to be possible, ontologies must be formalized in languages processable by computers. Without any doubt, the most popular is OWL: Web Ontology Language, which is the latest recommendation of W3C (World Wide Web Consortium), made in February 2004 for the first version 1.0 and more recently at the end of October 2009 for version 2.0. OWL 2 adds new functionalities, which some are said to be only "syntactic sugar" while others really offer new expressivity. 

 There has been since 2003 a growing interest of ontologybased technologies within data fusion, as shown by the increasing number of papers in international Conferences. 

For instance during the 6th International Conference on Information Fusion, held in Australia in 2003, was organized a Special Session titled "Ontology Needs and Issues for Higher-Level Fusion." 
Data Fusion embodies the process of data combination to 

![0_image_0.png](0_image_0.png)

refine state estimates and predictions [1]. These data are obtained from various and heterogeneous sensors such as radar, satellite, human messages, etc. The JDL model, elaborated by the Joint Directors of Laboratories Data Fusion Group, presents a pedagogically common understanding of Data Fusion, illustrated on the figure 1 below. 

In the field of National Defense, community focuses on problems involving the location, characterization, and identification of distinct and dynamic entities such as emitters, platforms, weapons, and military units (level 1). Beyond this task, one seeks higher-level inferences about the enemy situation (such as the relationship among entities, their relationships with the environment and higher level enemy organizations for example, which refers to level 2) 
and the possible threat they embody (level 3). 

In that context, ontologies are useful in providing means for describing and reasoning about sensor data, objects, relations and general domain theories. In that way, they enable to explicitly encode a shared understanding of some domain, in order to disambiguate terms associated with that domain. 

However, uncertainty is an important characteristic of our data and information handled by Data Fusion applications. 

At a low level of the JDL model, observations received from 