Professionals across various application domains of AI have long requested increased transparency of predictions generated by AI black-box models. In response, eXplainable AI (XAI) has gained popularity with researchers developing techniques to provide trustworthy explanations. We believe that incorporating expert knowledge into XAI systems through ontologies is a reliable approach. The Ontology-Based Image classifier (OBIC) introduced a flexible architecture compatible with any machine learning model, using OWL2 ontologies to represent expert knowledge. Later work extended OBIC by introducing ”explanatory” properties linked to logical features deduced from the datasets. This enables explanations using both explicit and implicit interpretable elements, independent of specific data types. This study formalizes ”explanatory” properties by integrating them with fuzzy logic. Building on previous research, we introduce new characteristics to address the vagueness of real-world data and shared knowledge. By embedding these properties into a fuzzy OWL2 ontology, we advance trustworthiness through fuzzy membership degrees, which serve as certainty levels for explanations, particularly in fuzzy knowledge environments.

With the continuous increase in the number of satellites orbiting Earth, the efficient and coordinated management of these assets becomes a critical concern. This paper introduces the innovative project Elicitation, a simulator for satellite constellations based on Multi-Agent Systems (MAS), Blockchain, and the Internet of Mobile Things (IoMT). The current scenario highlights growing challenges in the supervision and coordination of satellites, emphasizing the need for automated solutions. Elicitation addresses this complexity by integrating MAS to simulate dynamic interactions between satellites in Earth climate monitoring tasks, taking advantage of blockchain technology to guarantee the security, transparency and integrity of the data generated. One notable aspect that contributes to Elicitation’s appeal is its ability to take advantage of authentic satellite positioning data and real information relating to natural events. Consequently, the system operates autonomously to identify the ideal satellite capable of capturing data related to natural events at any given time. The average decision time per agent was 0.19 seconds for a collective set of 11 agents, corresponding to 11 satellites in a 120-day dataset with 43 target events.

Rule-based solutions for decision-making processes in complex and uncertain environments are beneficial because they are simple, transparent, and Effective. Considering that dependence on expert knowledge and human subjectivity of rule-based systems leads to inconsistencies or inaccuracies, approaches for the automatic rule selection process from training data are critical to minimize problems related to human interference. This study aims to apply genetic algorithms (GA) to automatically select IF-THEN rules in fuzzy inference systems to minimize the problems impacted by human involvement. To demonstrate the proposed approach’s applicability, FIS with an automatized rules selection method based on GA has been applied for the classification of the Titanic disaster dataset. The executed experiments indicate improved classification performance by twice increasing the classification’s F1-score from the initial generation to the last generation. Though the final metrics are less than the current state-of-the-art approach for the given dataset, the results approved the GA’s eligibility for automatic rule selection.

Quality assurance in manufacturing companies is an essential process for ensuring that products meet established standards. It contributes to customer satisfaction, as well as the reduction of the costs associated with defects. With Quality 4.0, an extension of Industry 4.0 to quality assurance, new possibilities in terms of product quality management are emerging. Thanks to expert knowledge, data collected by machine sensors can be used to anticipate quality issues or manufacturing errors. To semantically detect those situations, an ontology representing manufacturing knowledge linked to quality detection situations is needed. Moreover, as heterogeneous data streams have to be integrated, a combination of stream processing and of-line reasoning can be used. This combination allows a continuous process of data and the use of expert knowledge to detect anomalies. This paper presents an approach for detecting manufacturing quality losses. Therefore, an ontology-based context for manufacturing is introduced to detect quality issues situations. Then, an extension of an existing model using stream reasoning to process heterogeneous data from sensors and predictions is presented to detect the situations continuously.

For a long time, experts in different areas where Artificial Intelligence (AI) is widely applied have been requesting more clarity for the decisions made by AI. DARPA came up with a new framework for eXplainable AI (XAI) where the system exploits an explainable model to provide explanations through the explanation interface to users based on the level of their expertise. Later, ontologies were integrated in various ways, which paved the way for clearer explanations. Provided enough Expert Knowledge, ontologies can be a potent tool in XAI. Based on the ideas of Bellucci et al., their explainable system provides comprehensive explanations based on ”visible” properties found in images by Machine Learning (ML) models and described via ontologies. However, we believe that any property, not only visible ones, can be used to explore the data. New ”explanatory” properties are proposed to be used for explanations. Our system exploits ML models and more user-oriented Expert Knowledge using a wider range of properties for objects to build a more profound XAI.

Several approaches have been proposed to study text Classification in serious games. Unfortunately, those works do not investigate the impact of reasoning on text-based Classification. This paper proposes a novel approach to give ontology-driven reasoning on predicted results by machine learning. Thus, we construct text embedding on the serious game data. Next, we use machine learning for prediction and DBpedia ontology for reasoning to analyze the enhancement in prediction results. We conduct a set of benchmarks using several well-known machine learning algorithms, leading to an accuracy of 97%.

The coronavirus disease (COVID-19) spread rampantly around the world at the beginning of 2020 before the governments of each country could prevent it by making decisions based on medical data analysis. With proper formalization, the terabytes of new textual data available online every day could have been used for the early description and detection of cases of this virus. Since then, the number of Event-Based Surveillance (EBS) applications has increased exponentially. These applications aim to mine channels of unstructured data to detect signs of possible public health events. However, one problem with such systems is the need for expert intervention to define which event will be captured, which relevant terms should be used in the search, and to analyze the events to modify the search procedure constantly. Another problem is that many of these applications do not consider both spatial and temporal characteristics. Addressing such limitations, this article presents a novel approach. We propose the biomedical domain specialization of the Core Propagation Phenomenon Ontology (PropaPhen) to capture spatiotemporal characteristics of the propagation of health-related phenomena. We also propose the Description-Detection-Framework (DDF), which leverages PropaPhen, UMLS, and OpenStreetMaps to detect new medical events automatically. Finally, we demonstrate a use case with experiments on extracts from online newspapers about COVID-19. The results show that DDF can be useful for detecting clusters of suspicious cases of possible emerging health-related phenomena.

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International audience

Numerous explainability methods have appeared thanks to the surge of popularity of the explainable AI (XAI) domain. The DARPA depicted an explainable system, where an explainable model interacts with an explanation interface to generate explanations adapted to a user. We propose an explainable image classification system that follows this combination of explainable model and explanation interface as described by the DARPA. It takes advantage of the explainability of ontologies as well as the performance of machine learning models. This system is able to predict the class and properties of an object in an image. The results of this classification system are displayed in an explanation interface to help users understand and analyze the predictions of the proposed system. Our system exploits an ontology to build models that classify an object and its properties. The class and properties predicted by these models are instantiated in the ontology and added as assertions to an individual in order to verify the consistency of these predictions. Therefore, the system is able to warn the user when a prediction is uncertain and explain why, by using the ontology. These capacities will help users trust this system and better understand the predictions.

Nowadays, recommender systems are at use in various domains of everyday life such as social media networks, video on demand platforms or tourism. They help users sorting a vast amount of items and then get a more satisfying experience. However, these recommender systems tend to have a bias in the items recommended, a situation known as the overspecialization or diversity problem. In the tourism domain, this means new points of interest are less likely to be recommended than already established and well known places and that tourists tend to have the same trip over the same places, making it less personal. This paper presents and discusses first thoughts on how to overcome the overspecialization problem in the tourism domain by using the notion of ”semantic trajectory” of tourists in a touristic area.

The COVID-19 (SARS-CoV-2) spread around the globe could have been halted if we had had a better understanding of the situation and applied more restrictive measures for travel adapted to each country. This is due to a lack of efficient tools to visualize, analyze and control the virus dissemination. In the context of virus proliferation, analyzing flight connections between countries and COVID-19 data seems helpful to understand spatial and temporal information about the virus and its possible spread. To manage these complex, massive, and heterogeneous data, we propose a methodology based on knowledge graphs models. Several analyses and visualization tools can be applied, and our results show that these knowledge graph models may be a promising way to study the dissemination of any virus. These graphs can also be easily enriched with additional information that could be useful in the future to analyze or predict other interesting indicators.

Knowledge Graphs (KG) are gaining in popularity recently, notably since big tech giants announced they are using the technology. While the term is becoming popular, it is not new, and its ideas are even older. The research community has extensively studied knowledge Graphs in their various forms. Furthermore, the approach has been applied and proved valuable in many different applications. However, we found a lack of papers presenting the integration of KGs in a system regardless of the downstream application. We explore how KGs can fit in an overall information system independently from any specific use case, i.e., what we will consider knowledge consumption. We propose an architecture to understand better the KG roles within a system and how they can be integrated and implemented in a business context. We introduce each element of the latter architecture and discuss some candidate technology to implement them. Our work implements Knowledge Graph-Based Systems considering the constraints of a small to medium-sized enterprise.

Su-Field analysis, as one of TRIZ analytical tool for solving inventive problems, can be used to improve the performance of the technical system effectively. Generally, choosing an appropriate inventive standard is critical to solving inventive problems efficiently and accurately. However, these standards are summarized and categorized based on the enormous amount of patents in different domains, and they are built in the high level of abstraction, independently of the specific application field, making their use require much more technical knowledge than other TRIZ tools. In order to facilitate the use of inventive standards, especially for capturing the uncertainty or imprecision depicted in the standards, a rule-based heuristic methodology is proposed in this paper. Firstly, Su-Field analysis ontology and fuzzy analysis ontology are built to represent the precise and fuzzy knowledge in the process of solving inventive problems respectively. Then, SWRL (Semantic Web Rule Language) inference and fuzzy inference are performed for generating heuristic concept solution. Finally, a prototype is developed and the resolution of the case of the “Auguste Piccard’s Stratostat” in prototype is elaborated in detail.

Explainable Artificial Intelligence (XAI) has seen a surge in popularity in the past few years, thanks to new legislations that promote the “right to explanation”. Many popular methods have been developed recently to help understand black-box models, but it is not clear yet how an explanation is defined. Furthermore, the community agrees to say that many important terms do not have commonly accepted definitions. In this paper, we review the literature and show that there is a major issue concerning the definitions of terms such as explainability or interpretability. There is a lack of consensus that slows the development of this field. To address this problem, we propose a terminology that takes into account the context of an AI system, i.e., its users, purposes or design. This terminology is compatible with the majority of the definitions encountered in the literature so that it can be a foundation for future works.

Understanding the replication machinery of viruses contributes to suggest and try effective antiviral strategies. Exhaustive knowledge about the proteins structure, their function, or their interaction is one of the preconditions for successfully modeling it. In this context, modeling methods based on a formal representation with a high semantic expressiveness would be relevant to extract proteins and their nucleotide or amino acid sequences as an element from the replication process. Consequently, our approach relies on the use of semantic technologies to design the SARS-CoV-2 replication machinery. This provides the ability to infer new knowledge related to each step of the virus replication. More specifically, we developed an ontology-based approach enriched with reasoning process of a complete replication machinery process for SARS-CoV-2. We present in this paper a partial overview of our ontology OntoRepliCov to describe one step of this process, namely, the continuous translation or protein synthesis, through classes, properties, axioms, and SWRL (Semantic Web Rule Language) rules.

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As today’s manufacturing domain is becoming more and more knowledge-intensive, knowledge-based systems (KBS) are widely applied in the predictive maintenance domain to detect and predict anomalies in machines and machine components. Within a KBS, decision rules are a comprehensive and interpretable tool for classification and knowledge discovery from data. However, when the decision rules incorporated in a KBS are extracted from heterogeneous sources, they may suffer from several rule quality issues, which weakens the performance of a KBS. To address this issue, in this paper, we propose a rule base refinement approach with considering rule quality measures. The proposed approach is based on a rule integration method for integrating the expert rules and the rules obtained from data mining. Within the integration process, rule accuracy, coverage, redundancy, conflict, and subsumption are the quality measures that we use to refine the rule base. A case study on a real-world data set shows the approach in detail.

Cognitive Vision Systems have gained a lot of interest from industry and academia recently, due to their potential to revolutionize human life as they are designed to work under complex scenes, adapting to a range of unforeseen situations, changing accordingly to new scenarios and exhibiting prospective behavior. The combination of these properties aims to mimic the human capabilities and create more intelligent and efficient environments. Contextual information plays an important role when the objective is to reason such as humans do, as it can make the difference between achieving a weak, generalized set of outputs and a clear, target and confident understanding of a given situation. Nevertheless, dealing with contextual information still remains a challenge in cognitive systems applications due to the complexity of reasoning about it in real time in a flexible but yet efficient way. In this paper, we enrich a cognitive system with contextual information coming from different sensors and propose the use of stream reasoning to integrate/process all these data in real time, and provide a better understanding of the situation in analysis, therefore improving decision-making. The proposed approach has been applied to a Cognitive Vision System for Hazard Control (CVP-HC) which is based on Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) and has been designed to ensure that workers remain safe and compliant with Health and Safety policy for use of Personal Protective Equipment (PPE).

Artificial Intelligence (AI), or Augmented Intelligence (AI)? AI vs AI. Who is the winner? Increasingly often, we tend to agree that, at least at the current state of affairs, it is augmentation ra...

As a scientific discipline, systems biology aims to build models of biological systems and processes through the computer analysis of a large amount of experimental data describing the behaviour of whole cells. It is within this context that we already developed the Biomolecular Network Ontology especially for the semantic understanding of the behaviour of complex biomolecular networks and their transittability. However, the challenge now is how to automatically populate it from a variety of biological documents. To this end, the target of this paper is to propose a new approach to automatically populate the Biomolecular Network Ontology and take advantage of the vast amount of biological knowledge expressed in heterogeneous unstructured data about complex biomolecular networks. Indeed, we have recently observed the emergence of deep learning techniques that provide significant and rapid progress in several domains, particularly in the process of deriving high-quality information from text. Despite its significant progress in recent years, deep learning is still not commonly used to populate ontologies. In this paper, we present a deep learning-based NLP ontology population system to populate the Biomolecular Network Ontology. Its originality is to jointly exploit deep learning and natural language processing techniques to identify, extract and classify new instances referring to the BNO ontology’s concepts from textual data. The preliminary results highlight the efficiency of our proposal for ontology population.

Within manufacturing processes, anomalies such as machinery faults and failures may lead to the outage situation of production lines. The outage of production lines is detrimental for the availability of production systems and may cause severe economic loss. To avoid the economic loss that may be caused by the outage situation, the prediction of anomalies on production lines is a crucial concern for manufacturers. Recently, data mining techniques have been applied to the manufacturing domain for predicting occurrence time of anomalies, such as the moment of machinery failure. However, existing predictive maintenance approaches have been limited to the prediction of the time of occurrence of machinery failures, while lacking the capability for identifying the criticality of the failures. This may lead to inappropriate maintenance plans and strategies. In this context, in this paper, we introduce a novel ontology-based approach to facilitate predictive maintenance in industry. The proposed approach is a combination use of fuzzy clustering and semantic technologies, where fuzzy clustering techniques are used to learn the criticality of failures based on machine historical data, and semantic technologies use the results of fuzzy clustering to predict the time of failures and the criticality of them. As results, a domain ontology for modeling predictive maintenance knowledge is developed, and a set of Semantic Web Rule Language (SWRL) predictive rules are proposed to reason about the time and criticality of machinery failures. A case study on a real-world industrial data set is followed to evaluate the usefulness and effectiveness of the proposed approach.

With the coming era of Industry 4.0, more assets and machines in plants are equipped with sensors which collect big amount of data for effective on-line equipment condition monitoring. Monitoring equipment conditions can not only reduce unplanned downtime by early detection of relevant situations like anomalies but also avoid unnecessary routine maintenance. For the detection of these situations it is necessary to integrate distributed, heterogeneous data sources and data streams. In this context, semantic web technologies are increasingly considered as key technologies to improve data integration. However, they are mainly used for data that is assumed not to change very often in time. In order to tackle this issue, stream reasoning combines reasoning and stream processing methods. Such a combination enables the processing of dynamic and heterogeneous data continuously produced from a large number of sources and implementing real-time services. This paper presents an approach that uses stream reasoning to identify in real time certain situations that lead to potential failures. Early detection enables to choose the most appropriate decision to avoid the interruption of manufacturing processes. In order to achieve this, data collected from sensors are enriched with contextual information. The use of stream reasoning allows the integration of data from different data sources, with different underlying meanings, different temporal resolutions as well as the processing of these data in real time.

Manufacturing enterprises are engaged in implementing new technologies to enhance their manufacturing lines in a smart way. These new technologies give manufacturing enterprises the knowledge, understanding, insight and foresight to improve products, processes and decisions, thereby creating a competitive advantage. In this paper, we explore the use of semantics to enhance deep learning models. We propose an ontology-based LSTM neural network, in which the deep architecture is designed with an ontology to extract high-level cognitive features and stacked LSTM layers for learning temporal dependencies. Our model is applied to a real manufacturing data set with multivariate time series for classification problems. The experiments show that our model can improve performance compared with conventional methods.

Following the trend of Industry 4.0, automation in different manufacturing processes has triggered the use of intelligent condition monitoring systems, which are crucial for improving productivity and availability of production systems. To develop such an intelligent system, semantic technologies are of paramount importance. This paper introduces an ontology that will be used to develop an intelligent condition monitoring system. The proposed ontology formalizes domain knowledge related to condition monitoring tasks of manufacturing processes. After introducing the ontology in detail, we evaluate the proposed ontology by instantiating it with a case study: a conditional maintenance task of bearings in rotating machinery.

Analysis of biological systems is being progressively facilitated by computational tools. Most of these tools are based on qualitative and numerical methods. However, they are not always evident, and there is an increasing need to provide an additional semantic layer. Semantic technologies, especially ontologies, are one of the tools frequently used for this purpose. Indeed, they are indispensable for understanding the semantic knowledge about the operation of cells at a molecular level. We describe here the biomolecular network ontology (BNO) created specially to address the needs of analysing the complex biomolecular network’s behaviour. A biomolecular network consists of nodes, denoting cellular entities, and edges, representing interactions among cellular components. The BNO ontology provides a foundation for qualitative simulation of complex biomolecular networks. We test the performance of the proposed BNO ontology by using a real example of a biomolecular network, the bacteriophage T4 gene 32. We illustrate the proposed BNO ontology for reasoning and inferring new knowledge with sets of rules expressed in SWRL. Results demonstrate that the BNO ontology allows to precisely interpret the corresponding semantic context and intelligently model biomolecular networks and their state changes. The Biomolecular Network Ontology (BNO) is freely available at https://github.com/AliAyadi/BNO-ontology-version-1.0.

The fundamental goal of systems biology is to understand the dynamic aspects of cells and their behaviour. This organism is represented by a network so-called complex biomolecular network in which the nodes represent the different cellular components and the edges represent the interactions occurring among them. Through this network, it is easy to study the transition states and the dynamic behaviour of cells. Indeed, perturbing some nodes of the biomolecular network induce the transition of all the network. This process, known as the ”transittability”, expresses the idea of steering the complex biomolecular network from an unexpected state to a desired state. In this context, we are thus interested in how to use the transittability of biomolecular networks to increase the efficiency of translational medicine for improving human health and disease, including genetic and environmental factors of of patient’s well-being. This is a great opportunity to understand diseases, and find new diagnoses and treatments. Due to its complexity, the transittability of complex biomolecular networks can be considered as an optimization problem. Up to a recent date only few studies have been carried out in this problem. Most of them focused only on the minimization of the required nodes to steer the entire network, and others considered the minimization of the number of stimuli to be applied on the network. However, this assumption is not always realistic, because steering complex biomolecular networks is in general a multi-objective optimization problem. It requires finding appropriate trade-offs among various objectives, for example between the appropriate nodes to be stimulated and the number of external stimuli to be used and their cost, and the impact on patient’s well-being. In this paper, the optimization of the transittability of complex biomolecular networks is investigated from the multi-objective perspective. In the mathematical model four criteria are considered simultaneously: the minimization of the number of external stimuli, the minimization of their total cost, the minimization of the number of target nodes, and the minimization of the patient discomfort. All these objectives are described theoretically and mathematically in detail.

With the development of high-throughput techniques, systems biology has been pushing researchers to focus on how to optimize the steering of biomolecular networks from their actual state to a desired state. This phenomenon known as the ”transittability” means that complex biomolecular networks can be steered from an unexpected state to a desired state. This paper investigates the optimization of the transittability of complex biomolecular networks taking into account different objective functions. To solve this problem, we propose a multi-objective optimization approach which consists of two steps, the search and decision making step. The search step is based on a powerful multi-objective genetic algorithm, the non-dominated sorting genetic algrorithm (NSGA-II), to solve our problem and obtain a Pareto-optimal set. As regards the decision making step is based on the use of a multi-criteria decision making method, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), for providing the best compromise solution according to the user preferences. The proposed approach was tested and applied to solve the steering of the p53 Signaling network. Experimental results illustrate the effectiveness of this approach.

Industry 4.0 is an initiative combining a set of technologies that help to achieve more efficient manufacturing processes. An important characteristic for industrial production in Industry 4.0 is that physical items such as sensors, devices and enterprise assets are connected to each other and to the Internet. In this environment, devices and sensors generate increasing amount of data. A key point to consider is that the execution of industrial processes should depend not only on their internal state and on user interactions but also on the context of their execution, in order to become context-aware and provide added-value information to improve the monitoring of operations and their performance. Ontologies emerge as a relevant method for representing manufacturing knowledge in a machine-interpretable way. Therefore, an ontology-based context model for industry is introduced in this paper. The model facilitates context representation and reasoning by providing structures for context-related concepts, rules and their semantics.

Analysis of biological systems is being progressively facilitated by computational tools. Most of these tools are based on qualitative and numerical methods. However, they are not always evident and there is an increasing need to provide an additional semantic layer. Semantic technologies, especially ontologies, are one of the tools frequently used for this purpose. In fact, they are indispensable for understanding the semantic knowledge about the functioning of cells on a molecular level. We describe here the biomolecular network ontology (BNO) created specially to address the needs of analysing the complex biomolecular network's behaviour. The BNO ontology is freely available at https://github.com/AliAyadi/The-BiomolecularNetwork-Ontology and can be viewed using the standard ontology visualization editor Protégé. This ontology provides qualitative simulation of large and complex biomolecular networks. Therefore, in order to evaluate the efficacy of the BNO ontology we present two kinds of reasoning mechanisms. The first consists on an SWRL rule based reasoning developed using the SWRL rules editor tab, and another consists on an implementation of a rule-based system under the MATLAB/SIMULINK development environment and can be freely downloaded at https://github.com/AliAyadi/QualitativeReasoningInMATLAB. Both of these reasoning mechanisms have been applied to the analysis of a real network of biological interest, the "bacteriophage T4 gene 32" use case.

The specific attention paid to the quality perceived through the senses of costumers when touching a product has led to a rapid growth in the industrial interest for the field of haptics. Controlling the quality of products with such expectations has become a challenge for manufacturers, especially considering the current lack of a generic method to standardize control specifications and provide efficient control tools, whether a manual or automated control is considered. This study provides a new insight on the definition of control specifications regarding perceived quality control. Smart systems have proven useful and efficient in a number of other domains, but has never been applied in a generic manner to the control of the quality related to the sense of touch. Therefore, a system based on formalized knowledge on haptic perceptions and its relations with quality control is proposed. This paper presents the proposed approach for the standardization of haptic quality control specifications, along with an example of a manufacturing application. The structure of the proposed knowledge model is detailed, as well as the semantic approach that enabled the development of a formalized haptic sensation vocabulary. An experimental method was used to model the influence of exploration on perception, considering the application case.

Because of the lack of satisfactory solutions to explain biological systems, biologists usually focus on modelling and simulation tools to understand the behaviour of these complex organisms. Indeed, computational modelling and simulation of cells plays a pivotal role in systems biology. In this paper, we tackle the problem of studying the behaviour of human cells by reproducing the behaviour of complex biomolecular networks. To this end, we present in this paper an approach for simulating complex biomolecular networks inspired by the discrete-event simulation model (DEVS), a formalism developed for supporting the modelling of complex systems. In this paper, we propose a simulation tool, named ”CBNSimulator”, based on a logical model of the biomolecular network and taking advantage of the performance of a discrete-time simulation model for understanding the evolution and the behaviour of complex biomolecular networks as a discrete sequence of events in time. The proposed tool has been applied to the case study of a ribosomal protein regulation network, named ”the bacteriophage T4 gene 32”, and results given by this simulation tool are in agreement with the expert’s judgement. Moreover, the graphical user interface of CBNSimulator allows biologists to easily reproduce, analyse and understand behaviour of complex biomolecular networks through discrete simulation.

The use of semantic technologies, such as ontologies, to describe and analyse biological systems is at the heart of systems biology. Indeed, understanding the behaviour of cells requires a large amount of context information. In this paper, we propose an ontology entitled ”Biomolecular Network ontology” using the OWL language. The BNO ontology standardises the terminology used by biologists experts to address issues including semantic behaviour representation, reasoning and knowledge sharing. The main benefit of this proposed ontology is the ability to reason about dynamical behaviour of complex biomolecular networks over time. We demonstrate our proposed ontology with a detailed example, the bacteriophage T4 gene 32 use case.

Nowadays, innovation represents one of the most crucial factors driving the success of companies. The Theory of Inventive Problem Solving (also known as TRIZ) is a well-established method to facilitate systematic inventive design. Although, TRIZ allows solving inventive problems through a panoply of knowledge sources, it may make inventive problem solving a time-consuming, experience demanding process and lead to waste of resources of the companies. To avoid the use of these tools and to help new users in solving their inventive problems without completely mastering TRIZ, we propose in this paper an approach based on the use of the Case-based reasoning (CBR) in order to capitalize experience. CBR is a knowledge paradigm that solves a new problem by finding the old similar cases and reusing them. The retrieval is conducted in order to find the old similar cases, and the old solutions of the retrieved cases are adapted to solve the new problem. In this paper, a systematic three-level adaptation is proposed to reduce the effort required of the users in choosing the suitable solution to solve their problem. An example is used to illustrate in detail the proposed approach.

Land use and urban development surveys involve the interpretation of a large volume of data coming from satellite images processing as well as from remote sensors networks. In order to facilitate this interpretation, the development of a multipurpose Intelligent Data Analysis (IDA) framework for supporting geographical data perception is proposed here. The framework makes use of semantic technologies and relies on a novel knowledge model composed by a foundational ontology (DOLCE Ultra-Lite, also called DUL), three core reference ontologies (the Temporal Abstraction Ontology or TAO, the Semantic Sensor Network ontology or SSN and the SWRL Temporal Ontology or SWRLTO) and two specific domain ontologies (the Urban Ontology or URO and the Geographic Data ontology or GeoD, developed by our team). They play different and well specific roles in the whole process of perception. The paper shows how to apply SSN to manage measurements of geographical regions provided by satellite images processing software. In a similar way, TAO has been extended to deal with the abstractions resulting from geographical data interpretation. An example shows a SWRL based implementation of a perception process that gradually abstracts geographical features and objects.

Best Student Paper of the Conference

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The work described in this paper is part of the IDEX (excellence initiative) project ``Complex Identities launched by Strasbourg University in 2015. The main goal is to map available knowledge in Strasbourg university in order to provide a comprehensive and structured view of its different components. Our approach consists, first, in building an ontology able to represent available knowledge in the university, making it understandable by users. Then, we are interested in visualizing the ontology to help users explore easily the represented knowledge.

Systems biology models aim to describe and understand the behaviour of a cell. This living organism is represented by a complex biomolecular network. In the literature, most researches focus only on modeling isolated parts of this network, such as the metabolic network or the gene regulatory network. However, to fully understand the behaviour of a cell we should model and analyze the biomolecular network as a whole. Towards this goal, we firstly present a formalization for describing the logical structure, function and behaviour of complex biomolecular networks. In addition, we propose a semantic approach based on four ontologies to provide a rich description for modeling a biomolecular network and its state changes. This approach contributes to propose to the biologist a platform where to simulate the state changes of biomolecular networks with the hope of steering their behaviours.

In the present world, it is not rare to see multiple cultures coexist in large global projects spanning multiple geographies. The misunderstandings arising from these cultural differences are responsible for many failures, which are then blamed by each nationality on the others, without trying to understand and address those differences first. We propose to prepare the ground for the development of culturally aware information systems based on the application of the Hozo ontology to describe this field and the rules that can be applied to correct the understanding of behaviors in one culture by the individuals representing the other cultures, especially when more than two cultures coexist. The American author Erin Meyer has provided a reference model for this, using eight dimensions. The example of Lean will be used to illustrate the approach of creating a common culture that enables employees from all over the world to work together, using a common language, but at the same time highlighting the fundamental task of translating this common language locally in a way that can be understood by each representative of each culture.

International audience

The problem of the transportation of patients from or to some health care center given a number of vehicles of different kinds can be considered as a common Vehicle Routing Problem (VPR). However, in our particular case, the logistics behind the generation of the vehicle itineraries are affected by a high number of requirements and constraints such as the enterprise benefits, the satisfaction of the patients, and the respect of certain law regulations regarding the patients and the employees. In this work, we discuss the main aspects of the implementation of a Multi Objective Evolutionary Algorithm focused on providing a set of valid solutions to the end users of Patient Transport Services. We provide a detailed description of the process of integrating all the information on different genetic operators and multiple fitness functions. Finally, we present the preliminary results on a real-life problem from an small company that provides transport service and we compare the results that our implementation gets with the itineraries proposed by human experts.

In this paper, we describe the characteristics of the Lean Enterprise and make the case for modelling it in order to reproduce its successful practices more easily. The literature contains many good descriptions of the Toyota Production System and Lean in general, but no formal model that we can build upon. We then make the hypothesis that Lean is a Complex System, which can be modelled formally. We propose to follow the KREM model which comprises four components. The K (Knowledge) component includes domain knowledge about Lean in the form of several ontologies, the R (Rules) component is expressed by probabilistic rules, the E (Experience) component describes the practices (Kata) and the M (Meta-data) component describes the context of the application of Lean (different types of companies or cultural environments, for example). A practical example modelling the Hoshin Kanri process for setting objectives at the enterprise level demonstrates how to put this approach into practice.

This article describes the research project MAEOS, whose purpose is to model the organizational and strategic development of SMEs. The main objective of this project is to improve the efficiency and performance of business advice given to this kind of companies by establishing a set of methods and software tools for analysis and diagnosis. In order to achieve this, a multi-disciplinary team was created in which two main research areas are represented: artificial intelligence and management science. In this work several key questions of the knowledge engineering field are addressed by the team: how to extract theoretical knowledge (e.g. from scientific works in management science) and practical one (e.g. from consultants); how to formalize it and use it to assist consultants in their daily work.

The Region Connection Calculus (RCC), and particularly its RCC8 subset, have been extensively studied and used for qualitative spatial reasoning. Some sets of computational operations have also been defined for topological relations, as the CM8 set, that allows to compute the RCC8 relationships on raster images. In this paper, we propose a reified representation of the RCC8 spatial relationships and of the CM8 primitives, within a lattice of concepts, implemented in OWL (Ontology Web Language) in order to help the interpretation of urban satellite images. Our approach allows for a straightforward representation of concepts corresponding to conjuctions or disjunctions of RCC8 spatial relationships, and thus offers the advantage to overcome some drawbacks of the existing approaches in OWL, where spatial relations are represented as roles. Indeed, the OWL language does not allow the expression of the disjunction or of the conjunction of roles. We can then implement a reasoning on the RCC8 relationships, which in particular allows to compute the composition table and its transitive closure. As the reification of roles precludes the use of role's properties, such as symmetry and transitivity, we propose to implement RCC8 inferences through SWRL rules (Semantic Web Rule Language).

The extended use of high and very high spatial resolution imagery inherently demands the adoption of classification methods capable of capturing the underlying semantic. Object-oriented classification methods are currently considered the most appropriate alternative, due to the incorporation of contextual information and domain knowledge into the analysis. Integrating knowledge initially requires a detailed process of acquisition and later the achievement of a formal representation. Ontologies constitute a very suitable approach to address both knowledge formalization and exploitation. A novel semi-automatic semantic approach focused on the extraction and classification of urban objects is hereby introduced. The use of a three-layered architecture allows the separation of concerns among knowledge, rules and experience. Knowledge represents the fundamental layer with which the other layers interact. Rules are meant to derive conclusions and make assertions based on knowledge. Finally, the experience layer supports the classification process in case of failure when attempting to identify an object, by applying specific expert rules to infer unusual membership.

Su-Field analysis, as one of the inventive problem solving tools, can be used to analyze and improve the efficacy of the technical system. Generally, the process of using Su-Field model to solve a specific inventive problem includes: building a Problem Model, mapping to a Generic Problem Model, finding a Generic Solution Model based on the corresponding inventive standard, and finally establishing and instantiating a Solution Model. As one of the most important phases of Su- Field analysis, the last step is normally implemented manually with the help of physical effects, which link generic technical functions with specific applications and systems. The physical effects compatible with the context of the specific problem should be chosen to assist the users to instantiate the Solution Model. However, the physical effects and the specific problems are built at different levels of abstraction, and it is difficult for the users to choose, that is, given a certain function, too many physical effects are chosen while with the detailed context of the problem, no physical effect is returned. This paper proposes a new way of representing knowledge: both inventive standards and physical effects are represented as the change of two states, that is, the couple of the problem standard and the solution standard for inventive standards, and the couple of two states before and after applying physical effects. Firstly, three ontologies, that is, Su-Field Model Ontology, Su-Field Analysis Ontology and Physical Effects Ontology, are built to describe the problems with different granularities, and then the IS (Inventive Standard) rule and PE (Physical Effect) rules are established respectively for two kinds of reasoning. Finally the ontology reasoning is launched to provide the heuristic physical effects for the users. A case is used to elaborate the whole process in detail.

The increasing availability of high spatial resolution satellite images is an opportunity to characterize and identify urban objects. Object-based approaches using domain knowledge for image analysis are necessary to classify data. A major issue in these approaches is domain knowledge formalization and exploitation. The use of formal ontologies seems a judicious choice to deal with these issues, and therefore, an ontology concerning urban objects has been developed. Description logics (DL) have been used to exploit the knowledge in the ontologies and develop software tools to assist the automatic labelling of satellite images.

The theory of inventive problem solving (TRIZ) was developed to solve inventive problems in different industrial fields. In recent decades, modern innovation theories and methods proposed several different knowledge sources, whose use requires extensive knowledge about different engineering domains. In order to facilitate the use of the TRIZ knowledge sources, this paper explores a heuristic TRIZ problem solving approach. Firstly, TRIZ users start solving inventive problem with the TRIZ knowledge source of their choice. Then other similar knowledge sources are used according to a calculation of semantic relatedness. Finally, heuristic solutions are returned by ontology reasoning on the knowledge sources. The case of a “Diving Fin” is used to show the heuristic TRIZ problem solving process in detail.