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Rouaa Wannous

Université De La Rochelle, L3i, PhD student in Information Retrieval

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Rouaa WANNOUS is an Associate Professor at La Rochelle University, France. In 2014, she obtained her PhD in the field of Computer Science and Applications at L3i Laboratory (La Rochelle University) in collaboration with LIENSs Laboratory. Her thesis concerned the semantic trajectory of moving objects based on the inference over ontology considering domain, temporal and spatial dimensions. Her supervisors are Ass. Pr. Alain Bouju, Ass. Pr. Jamal Malki and Pr. Cecile Vincent. In 2011, she obtained her master in artificial intelligence and the web in Grenoble. Her internship was done at INRIA, supervised by Ass. Pr. Jérôme Euzenat and Ass. Pr. Cassia Trojahn, with the aim of explaining the reasoning behind the argumentation process for ontology alignment to understand the matching results.
Since 2022, her research interests consist mainly in handling knowledge over moving object trajectories using Formal Concept Analysis. She has authored more than 15 papers in refereed international journals and conferences in the field of background Knowledge Engineering and Artificial Intelligence. She has supervised several internships for master’s in research. She is also reviewers for international conferences and journals.
Phone: 0651323400
Address: La Rochelle

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Papers by Rouaa Wannous

Handling Knowledge over Moving Object Trajectories using Formal Concept Analysis

Springer , 2024

We define the term semantic trajectories of moving objects as a sequence of spatiotemporal points... more We define the term semantic trajectories of moving objects as a sequence of spatiotemporal points with associated semantic information. Spatio-temporal points are directly generated by sensors that capture the position of a moving object in time. In this paper, we work on a marine mammal trajectory case study. We consider the seals' trajectories to understand their behavior within groups and identify their activities simultaneously in the same place. We define the activities of mobile objects in the form of rules given by the domain expert. To gain knowledge of trajectories, we use the GALACTIC platform, which is a new platform based on Formal Concept Analysis (FCA) for computing a concept lattice from heterogeneous and complex data. Data in GALACTIC are described by predicates according to their types. Here, we will use interval sequence plugins to analyse the trajectories of seals, where interval sequences are represented by a set of time intervals in which a seal performs an activity in a geographical zone. Finally, the results show the simultaneous activities of a group of seals.

Computational inference of conceptual trajectory model : considering domain temporal and spatial dimensions. (Raisonnement sur la modélisation des trajectoires : prise en compte des aspects thématiques, temporels et spatiaux)

Spatio-temporal data describing trajectories of moving objects has increased as a consequence of ... more Spatio-temporal data describing trajectories of moving objects has increased as a consequence of the larger availability of such data due to current sensors techniques. These devices use different technologies like global navigation satellite system (GNSS), wireless communication, radio-frequency identification (RFID), and sensors techniques. Although capturing technologies differ, the captured data has common spatial and temporal features. Thus, relational database management systems (RDBMS) can be used to store and query the captured data. RDBMS define spatial data types and spatial operations. Recent applications show that the solutions based on traditional data models are not sufficient to consider complex use cases that require advanced data models. A complex use case refers not only to data, but also to the domain expert knowledge and others. An inference mechanism enriches semantic trajectories with this knowledge. Temporal and spatial reasoning are fundamental for the infere...

Temporal Reasoning in Trajectories Using an Ontological Modelling Approach

Control and Cybernetics, 2012

Nowadays, with a growing use of location-aware, wirelessly connected, mobile devices, we can easi... more Nowadays, with a growing use of location-aware, wirelessly connected, mobile devices, we can easily capture trajec-tories of mobile objects. To exploit these raw trajectories, we need to enhance them with semantic information. Several research fields are currently focusing on semantic trajectories to support inferences and queries to help users validating and discovering more knowledge about mobile objects. The inference mechanism is needed for queries on semantic trajectories connected to other sources of information. Time and ...

Using seal trajectories in biological early warning system for real-time zone tracking

Early warning systems were interested in captured data of mobile objects. From the 2000s, a new g... more Early warning systems were interested in captured data of mobile objects. From the 2000s, a new generation of data capture equipment arrives. These capture devices rise large scale trajectory data. How early warning systems can integrate these masses of data? How they can give real-time answers to users queries? In this paper, we present an ontological approach to model the trajectory. The trajectory’s domain knowledge are expressed as rules used by the ontological inference mechanism. We show the important complexity of the inference and we propose optimizations. We evaluate our contributions over real data. RÉSUMÉ. Les systèmes d’alertes rapides se sont intéressé aux données capturées en particulier celles des objets mobiles. Depuis le début des années 2000, de nouveaux dispositifs de capture sont conçus qui sont capables de restituer de grands volumes de données, appelés trajectoires. Comment les systèmes d’alertes rapides peuvent-ils intégrer ces masses de données ? Comment répo...

Ontology inference using spatial and trajectory domain rules

Capture devices give rise to a large scale spatiotemporal data describing moving object’s traject... more Capture devices give rise to a large scale spatiotemporal data describing moving object’s trajectories. These devices use different technologies like global navigation satellite system (GNSS), wireless communication, radio-frequency identification (RFID), and sensors techniques. Although capture technologies differ, the captured data share common spatial and temporal features. Thus, relational database management systems (RDBMS) can be used to store and query the captured data. For this, RDBMS define spatial data types and spatial operations. Recent applications show that the solutions based on traditional data models are not sufficient to consider complex use cases that require advanced data models. A complex use case refers to data, but also to domain knowledge, to spatial reasoning or others. This article presents a sample application based on trajectories that require three types of independent data models: a domain data model, a semantic model and a spatial model. We analyze ea...

Projet TourinFlux : Apport des Technologies du Web Sémantique pour la Gestion des Données du Tourisme

Le tourisme represente aujourd'hui l'un des secteurs d'activite le plus important pui... more Le tourisme represente aujourd'hui l'un des secteurs d'activite le plus important puisqu'il genere de nombreuses retombees economiques et stimule plusieurs autres secteurs d'activites. Dans un secteur aussi important, la maitrise des donnees devient fondamentale : donner une visibilite sur l'activite touristique d'un territoire, connaitre les profils des visiteurs, pouvoir anticiper les comportements et adapter l'offre touristique en consequence, tels sont les defis auxquels sont confrontes les differents acteurs prives et les pouvoirs publics. Or, le secteur du tourisme est caracterise par un fort afflux de donnees, souvent complexes, heterogenes et fortement dynamiques. Dans ce contexte, l'industrie du tourisme doit s'adapter pour mieux gerer, d'une part les donnees touristiques internes, et d'autre part les donnees externes disponibles sur le Web. Afin de repondre aux exigences aussi bien d'ordre qualitatives que quantitatives d...

Trajectory ontology inference over domain and temporal rules Inference complexity analyzing

Capture devices rise a large scale trajectory data about moving object’s movements. These devices... more Capture devices rise a large scale trajectory data about moving object’s movements. These devices use different technologies like global navigation satellite system (GNSS), wireless communication, radio-frequency identification (RFID), and other sensors. Huge trajectory data are available. In this article, we are interested in these data, so we use an ontological data modeling approach to build a trajectory ontology. This ontology contains temporal concepts, so we map it to a temporal ontology. We present an implementation framework for declarative and imperative parts of ontology rules in a semantic data store. An inference mechanism is computed over these semantic data. The running time and memory of the inference increases very rapidly as a function of the data size. For this reason, we propose a two-tier inference filters on data. The primary filter analyzes the trajectory data considering all the possible domain constraints. The data analyzed are considered as the first knowled...

Enhancement of Trajectory Ontology Inference Over Domain and Temporal Rules

Capture devices rise large scale trajectory data from moving objects. These devices use different... more Capture devices rise large scale trajectory data from moving objects. These devices use different technologies like global navigation satellite system (GNSS), wireless communication, radio-frequency identification (RFID), and other sensors. Huge trajectory data are available today. In this paper, we use an ontological data modeling approach to build a trajectory ontology from such large data. This ontology contains temporal concepts, so we map it to a temporal ontology. We present an implementation framework for declarative and imperative parts of ontology rules in a semantic data store. An inference mechanism is computed over these semantic data. The computational time and memory of the inference increases very rapidly as a function of the data size. For this reason, we propose a two-tier inference filters on data. The primary filter analyzes the trajectory data considering all the possible domain constraints. The analyzed data are considered as the first knowledge base. The second...

Utilisation des trajectoires de phoques dans un système d'alertes biologique pour le suivi de zone en temps réel

Ingénierie des systèmes d'information

Trajectory ontology inference considering domain and temporal dimensions—Application to marine mammals

Future Generation Computer Systems, 2016

Abstract Capture devices rise large scale trajectory data from moving objects. These devices use ... more Abstract Capture devices rise large scale trajectory data from moving objects. These devices use different technologies like global navigation satellite system (GNSS), wireless communication, radio-frequency identification (RFID), and other sensors. Huge trajectory data are available today. In this paper, we use an ontological data modeling approach to build a trajectory ontology from such large data. To accomplish reasoning over trajectories, the ontology must consider mobile object, domain and other knowledge. In our approach, we suggest expressing this knowledge in the form of rules. To annotate data with these rules, we need an inference mechanism over trajectory ontology. Experiments over our model using domain and temporal rules address an inference computation complexity. This complexity has two important factors: time computations and space storage. In order to reduce the inference complexity, we proposed optimizations, such as domain constraints and temporal neighbor refinements. In this paper, we define a refinement specifically for the application domain. Then, we evaluate our contribution over real trajectory data. Finally, the results show the positive impact of the last refinement on reducing the complexity of the inference mechanism. This refinement reduces half of the time computation and then allows considering larger data sets.

Time integration in semantic trajectories using an ontological modelling approach A case study with experiments, optimization and evaluation of an integration approach

Nowadays, with a growing use of location-aware, wirelessly connected, mobile devices, we can easi... more Nowadays, with a growing use of location-aware, wirelessly connected, mobile devices, we can easily capture trajectories of mobile objects. To exploit these raw trajectories, we need to enhance them with semantic information. Several research fields are currently focusing on semantic trajectories to support queries and inferences to help users for validating and discovering more knowledge about mobile objects. The inference mechanism is needed for queries on semantic trajectories connected to other sources of information. Time and space knowledge are fundamental sources of information used by the inference operation on semantic trajectories. This article presents a case study of inference mechanism on semantic trajectories. We propose a solution based on an ontological approach for modelling semantic trajectories integrating time information and rules. We give experiments and evaluations of the proposed approach on generated and real data.

An Ontology-Based Approach for Handling Explicit and Implicit Knowledge over Trajectories

Communications in Computer and Information Science, 2015

The current information systems manage several, different and huge databases. The data can be tem... more The current information systems manage several, different and huge databases. The data can be temporal, spatial and other application domains with specific knowledge. For these reasons, new approaches must be designed to fully exploit data expressiveness and heterogeneity taking into account application’s needs. As part of ontology-based information system design, this paper proposes an ontology modeling approach for trajectories of moving objects. Consider domain, temporal and spatial knowledge gives a complexity to our system. We propose optimizations to annotate data with these knowledge.

Trajectory ontology inference over domain and temporal rules. Inference complexity analyzing

Capture devices rise a large scale trajectory data about moving object's movements. These devices... more Capture devices rise a large scale trajectory data about moving object's movements. These devices use different technologies like global navigation satellite system (GNSS), wireless communication, radio-frequency identification (RFID), and other sensors. Huge trajectory data are available. In this article, we are interested in these data, so we use an ontological data modeling approach to build a trajectory ontology. This ontology contains temporal concepts, so we map it to a temporal ontology. We present an implementation framework for declarative and imperative parts of ontology rules in a semantic data store. An inference mechanism is computed over these semantic data. The running time and memory of the inference increases very rapidly as a function of the data size. For this reason, we propose a two-tier inference filters on data. The primary filter analyzes the trajectory data considering all the possible domain constraints. The data analyzed are considered as the first knowledge base. These data is passed to the secondary filter. Then the latter computes the inference over the filtered trajectory data. The secondary filter results yield the final knowledge base where the user can query.

Ontology inference using spatial and trajectory domain rules

Capture devices give rise to a large scale spatiotemporal data describing moving object's traject... more Capture devices give rise to a large scale spatiotemporal data describing moving object's trajectories. These devices use different technologies like global navigation satellite system (GNSS), wireless communication, radio-frequency identification (RFID), and sensors techniques. Although capture technologies differ, the captured data share common spatial and temporal features. Thus, relational database management systems (RDBMS) can be used to store and query the captured data. For this, RDBMS define spatial data types and spatial operations. Recent applications show that the solutions based on traditional data models are not sufficient to consider complex use cases that require advanced data models. A complex use case refers to data, but also to domain knowledge, to spatial reasoning or others. This article presents a sample application based on trajectories that require three types of independent data models: a domain data model, a semantic model and a spatial model. We analyze each of them and propose a modeling approach based on ontologies. This work introduces a high-level trajectory ontology and a generic spatial ontology. Also, we present our ontology matching approach for integrating the sample trajectory domain to both defined ontologies. This work has a special focus to the problem of defining ontology inference using business rules combined with spatial rules. We present an implementation framework for declarative and imperative parts of ontology rules using an RDF data store. We discuss various experiments based on spatial inference calculation. We discuss these results and present some solutions to improve the complexity of calculating spatial ontological inferences.

Enhancement of Trajectory Ontology Inference Over Domain and Temporal Rules

Capture devices rise large scale trajectory data from moving objects. These devices use different... more Capture devices rise large scale trajectory data from moving objects. These devices use different technologies like global navigation satellite system (GNSS), wireless communication, radio-frequency identification (RFID), and other sensors. Huge trajectory data are available today. In this paper, we use an ontological data modeling approach to build a trajectory ontology from such large data. This ontology contains temporal concepts, so we map it to a temporal ontology. We present an implementation framework for declarative and imperative parts of ontology rules in a semantic data store. An inference mechanism is computed over these semantic data. The computational time and memory of the inference increases very rapidly as a function of the data size. For this reason, we propose a two-tier inference filters on data. The primary filter analyzes the trajectory data considering all the possible domain constraints. The analyzed data are considered as the first knowledge base. The secondary filter then computes the inference over the filtered trajectory data and yields to the final knowledge base, that the user can query.

Inference on Semantic Trajectory Data Warehouse Using an Ontological Approach

Lecture Notes in Computer Science, 2014

Using location aware devices is getting more and more spread, generating then a huge quantity of ... more Using location aware devices is getting more and more spread, generating then a huge quantity of mobility data. The latter describes the movement of mobile objects and is called as well Trajectory data. In fact, these raw trajectories lack contextual information about the moving object goals and his activity during the travel. Therefore, the former must be enhanced with semantic information to be called then Semantic Trajectory. The semantic models proposed in the literature are in many cases ontology-based, and are composed of thematic, temporal and spatial ontologies and rules to support inference and reasoning tasks on data. Thus, calculating inference on moving objects trajectories considering all thematic, spatial, and temporal rules can be very long depending on the amount of data involved in this process. On the other side, TDW is an efficient tool for analyzing and extracting valuable information from raw mobility data. For that we propose throughout this work a TDW design, inspired from an ontology model. We will emphasis the trajectory to be seen as a first class semantic concept. Then we apply the inference on the proposed model to see if we can enhance it and make the complexity of this mechanism manageable.

Analysis of Trajectory Ontology Inference Complexity over Domain and Temporal Rules

Lecture Notes in Computer Science, 2014

Capture devices rise large scale trajectory data from moving objects. These devices use different... more Capture devices rise large scale trajectory data from moving objects. These devices use different technologies like global navigation satellite system (GNSS), wireless communication, radio-frequency identification (RFID), and other sensors. Huge trajectory data are available today. In this paper, we use an ontological data modeling approach to build a trajectory ontology from such large data. This ontology contains temporal concepts, so we map it to a temporal ontology. We present an implementation framework for declarative and imperative parts of ontology rules in a semantic data store. An inference mechanism is computed over these semantic data. The computational time and memory of the inference increases very rapidly as a function of the data size. For this reason, we propose a two-tier inference filters on data. The primary filter analyzes the trajectory data considering all the possible domain constraints. The analyzed data are considered as the first knowledge base. The secondary filter then computes the inference over the filtered trajectory data and yields to the final knowledge base, that the user can query.

Modelling Mobile Object Activities Based on Trajectory Ontology Rules Considering Spatial Relationship Rules

Studies in Computational Intelligence, 2013

Several applications use devices and capture systems to record trajectories of mobile objects. To... more Several applications use devices and capture systems to record trajectories of mobile objects. To exploit these raw trajectories, we need to enhance them with semantic information. Temporal, spatial and domain related information are fundamental sources used to upgrade trajectories. The objective of semantic trajectories is to help users validating and acquiring more knowledge about mobile objects. In particular, temporal and spatial analysis of semantic trajectories is very important to understand the mobile object behaviour. This article proposes an ontology based modelling approach for semantic trajectories. This approach considers dierent and independent sources of knowledge represented by domain and spatial ontologies. The domain ontology represents mobile object activities as a set of rules. The spatial ontology represents spatial relationships as a set of rules. To achieve this approach, we need an integration between trajectory and spatial ontologies.

Explaining Argumentation over Alignment Agreements

2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013

Argumentation frameworks have been used as tools for reconciliating ontology alignments, through ... more Argumentation frameworks have been used as tools for reconciliating ontology alignments, through a series of proposals and counter-proposals, i.e., arguments. However, argumentation outcomes may not be so obvious to human users. Explaining the reasoning behind the argumentation process may help users to understand its outcome, and influence the user's confidence and acceptance on the results. This paper presents a mechanism for providing explanations on the way agreed alignments are established. Our mechanism is based on tracing each step of the argumentation process. These traces are then interpreted using a set of association rules, built from a decision tree that represents all possible statuses of arguments. From these rules, a multi-level explanation, in natural language, is provided to the users.

Temporal Reasoning in Trajectories Using an Ontological Modelling Approach

Nowadays, with a growing use of location-aware, wirelessly connected, mobile devices, we can easi... more Nowadays, with a growing use of location-aware, wirelessly connected, mobile devices, we can easily capture trajectories of mobile objects. To exploit these raw trajectories, we need to enhance them with semantic information. Several research elds
are currently focusing on semantic trajectories to support inferences and queries to help users validating and discovering more knowledge about mobile objects. The inference mechanism is needed for queries on semantic trajectories connected to other sources of information. Time and space knowledge are fundamental sources of information used by the inference operation on semantic trajectories. This article discusses new approach for inference mechanisms on semantic trajectories.
The proposed solution is based on an ontological approach
for modelling semantic trajectories integrating time concepts and
rules. We present a case study with experiments, optimization and
evaluation to show the complexity of inference and queries. Then,
we introduce a renement algorithm based on temporal neighbour
to enhance the temporal inference. The results show the positive
impact of our proposal on reducing the complexity of the inference
mechanism.