Search

Your search keyword '"Freddy Lecue"' showing total 11 results
Start Over Author "Freddy Lecue" Publisher hal ccsd
11 results on '"Freddy Lecue"'

1. A Simplified Benchmark for Ambiguous Explanations of Knowledge Graph Link Prediction using Relational Graph Convolutional Networks

Author

Nicholas Halliwell, Fabien Gandon, Freddy Lecue, Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria), Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), THALES, Halliwell, Nicholas, COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), and THALES [France]

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; Relational Graph Convolutional Networks (RGCNs) are commonly used on Knowledge Graphs (KGs) to perform black box link prediction. Several algorithms have been proposed to explain their predictions. Evaluating performance of explanation methods for link prediction is difficult without ground truth explanations. Furthermore, there can be multiple explanations for a given prediction in a KG. No dataset exists where observations have multiple ground truth explanations to compare against. Additionally, no standard scoring metrics exist to compare predicted explanations against multiple ground truth explanations. We propose and evaluate a method, including a dataset, to benchmark explanation methods on the task of explainable link prediction using RGCNs.

Published
2022

2. The Need for Empirical Evaluation of Explanation Quality

Author

Nicholas Halliwell, Fabien Gandon, Freddy Lecue, Serena Villata, Halliwell, Nicholas, 3IA Côte d'Azur - - 3IA@cote d'azur2019 - ANR-19-P3IA-0002 - P3IA - VALID, Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Institut National de Recherche en Informatique et en Automatique (Inria), Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), and ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; Prototype networks (Li et al. 2018) provide explanations to users using a prototype vector; that is, a vector learned by the network representing a "typical" observation. In this work, we propose an approach that identifies relevant features in the input space used by the Prototype network. We find however that empirical evaluation of explanation quality is difficult without ground truth explanations. We include a discussion about developing methods for generating explanations, identifying when one explanation method is preferable to another, and the complications that arise when measuring explanation quality.

Published
2022

3. Linked Data Ground Truth for Quantitative and Qualitative Evaluation of Explanations for Relational Graph Convolutional Network Link Prediction on Knowledge Graphs

Author

Fabien Gandon, Nicholas Halliwell, Freddy Lecue, Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Institut National de Recherche en Informatique et en Automatique (Inria), Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), THALES [France], ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), THALES, COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), and COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)

Subjects
Theoretical computer science, Artificial neural network, Knowledge representation and reasoning, Computer science, graph neural networks, Linked data, Semantics, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Set (abstract data type), knowledge graphs, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Metric (mathematics), Explainable AI, Benchmark (computing), Leverage (statistics), link prediction
Abstract

International audience; Relational Graph Convolutional Networks (RGCNs) identify relationships within a Knowledge Graph to learn real-valued embeddings for each node and edge. Recently, researchers have proposed explanation methods to interpret the predictions of these blackbox models. However, comparisons across explanation methods for link prediction remains difficult, as there is neither a method nor dataset to compare explanations against. Furthermore, there exists no standard evaluation metric to identify when one explanation method is preferable to the other. In this paper, we leverage linked data to propose a method, including two datasets (Royalty-20k, and Royalty-30k), to benchmark explanation methods on the task of explainable link prediction using Graph Neural Networks. In particular, we rely on the Semantic Web to construct explanations, ensuring that each predictable triple has an associated set of triples providing a ground truth explanation. Additionally, we propose the use of a scoring metric for empirically evaluating explanation methods, allowing for a quantitative comparison. We benchmark these datasets on state-of-the-art link prediction explanation methods using the defined scoring metric, and quantify the different types of errors made with respect to both data and semantics. CCS CONCEPTS • Mathematics of computing → Computing most probable explanation; • Computing methodologies → Knowledge representation and reasoning; Neural networks.

Published
2021
Full Text
View/download PDF

4. User Scored Evaluation of Non-Unique Explanations for Relational Graph Convolutional Network Link Prediction on Knowledge Graphs

Author

Fabien Gandon, Nicholas Halliwell, Freddy Lecue, Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), THALES [France], ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), and THALES

Subjects
Ground truth, explanation evaluation, business.industry, Computer science, graph neural networks, Relational graph, Machine learning, computer.software_genre, Task (project management), [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], knowledge graphs, Knowledge graph, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Black box, Explainable AI, Benchmark (computing), Relevance (information retrieval), Artificial intelligence, Link (knot theory), business, computer, link prediction
Abstract

International audience; Relational Graph Convolutional Networks (RGCNs) are commonly used on Knowledge Graphs (KGs) to perform black box link prediction. Several algorithms, or explanation methods, have been proposed to explain their predictions. Evaluating performance of explanation methods for link prediction is difficult without ground truth explanations. Furthermore, there can be multiple explanations for a given prediction in a KG. No dataset exists where observations have multiple ground truth explanations to compare against. Additionally, no standard scoring metrics exist to compare predicted explanations against multiple ground truth explanations. In this paper, we introduce a method, including a dataset (FrenchRoyalty-200k), to benchmark explanation methods on the task of link prediction on KGs, when there are multiple explanations to consider. We conduct a user experiment, where users score each possible ground truth explanation based on their understanding of the explanation. We propose the use of several scoring metrics, using relevance weights derived from user scores for each predicted explanation. Lastly, we benchmark this dataset on state-of-the-art explanation methods for link prediction using the proposed scoring metrics.

Published
2021
Full Text
View/download PDF

5. Explainable Artificial Intelligence: Concepts, Applications, Research Challenges and Visions

Author

Freddy Lecue, Randy Goebel, Andreas Holzinger, Peter Kieseberg, Luca Longo, Technological University [Dublin] (TU), Alberta Machine Intelligence Institute (Amii), University of Alberta, THALES [France], Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), St. Pölten University of Applied Sciences, Medical University Graz, Andreas Holzinger, Peter Kieseberg, A Min Tjoa, Edgar Weippl, TC 5, TC 8, TC 12, WG 8.4, WG 8.9, WG 12.9, THALES, Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), Alberta Machine Intelligence Institute, and Austrian Science Fund (FWF)

Subjects
Vision, Computer science, Computer Sciences, [SHS.INFO]Humanities and Social Sciences/Library and information sciences, Acknowledgement, 02 engineering and technology, Explainability, Data science, 020204 information systems, General Data Protection Regulation, Transparency (graphic), Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Reinforcement learning, media_common.cataloged_instance, Domain knowledge, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], European union, Explainable artificial intelligence, Coherence (linguistics), media_common
Abstract

International audience; The development of theory, frameworks and tools for Explainable AI (XAI) is a very active area of research these days, and articulating any kind of coherence on a vision and challenges is itself a challenge. At least two sometimes complementary and colliding threads have emerged. The first focuses on the development of pragmatic tools for increasing the transparency of automatically learned prediction models, as for instance by deep or reinforcement learning. The second is aimed at anticipating the negative impact of opaque models with the desire to regulate or control impactful consequences of incorrect predictions, especially in sensitive areas like medicine and law. The formulation of methods to augment the construction of predictive models with domain knowledge can provide support for producing human understandable explanations for predictions. This runs in parallel with AI regulatory concerns, like the European Union General Data Protection Regulation, which sets standards for the production of explanations from automated or semi-automated decision making. Despite the fact that all this research activity is the growing acknowledgement that the topic of explainability is essential, it is important to recall that it is also among the oldest fields of computer science. In fact, early AI was re-traceable, interpretable, thus understandable by and explainable to humans. The goal of this research is to articulate the big picture ideas and their role in advancing the development of XAI systems, to acknowledge their historical roots, and to emphasise the biggest challenges to moving forward.

Published
2020
Full Text
View/download PDF

6. Personalizing Actions in Context for Risk Management using Semantic Web Technologies

Author

Eugene Eichelberger, Gemma Gallagher, Peter McCanney, Sara van de Moosdijk, Christophe Guéret, Freddy Lecue, Jer Hayes, Jiewen Wu, Accenture Labs [Ireland], Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria), Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Fjord [Ireland], Université Nice Sophia Antipolis (... - 2019) (UNS), and COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS)

Subjects
Knowledge management, business.industry, Computer science, Process (engineering), 05 social sciences, 020206 networking & telecommunications, Context (language use), 02 engineering and technology, Data type, Social Semantic Web, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering, Portfolio, business, Quality assurance, Semantic Web, 050203 business & management, Risk management
Abstract

International audience; The process of managing risks of client contracts is manual and resource-consuming, particularly so for Fortune 500 companies. As an example, Accenture assesses the risk of eighty thousand contracts every year. For each contract, different types of data will be consolidated from many sources and used to compute its risk tier. For high-risk tier contracts, a Quality Assurance Director (QAD) is assigned to mitigate or even prevent the risk. The QAD gathers and selects the recommended actions during regular portfolio review meetings to enable leadership to take the appropriate actions. In this paper, we propose to automatically personalize and contextualize actions to improve the efficacy. Our approach integrates enterprise and external data into a knowledge graph and interprets actions based on QADs' profiles through semantic reasoning over this knowledge graph. User studies showed that QADs could efficiently select actions that better mitigate the risk than the existing approach.

Published
2017

7. Learning from Ontology Streams with Semantic Concept Drift

Author

Jiaoyan Chen, Jeff Z. Pan, Huajun Chen, Freddy Lecue, Delft University of Technology (TU Delft), Zhejiang University, Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria), University of Manchester [Manchester], Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Accenture Labs [Ireland], School of Computer Science [Manchester], University of Aberdeen, Université Nice Sophia Antipolis (1965 - 2019) (UNS), and COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS)

Subjects
FOS: Computer and information sciences, Data stream, Concept drift, Knowledge representation and reasoning, Computer Science - Artificial Intelligence, Computer science, Inference, Context (language use), 02 engineering and technology, Ontology (information science), computer.software_genre, Semantics, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 0202 electrical engineering, electronic engineering, information engineering, 0501 psychology and cognitive sciences, [INFO]Computer Science [cs], Semantic Web, business.industry, 05 social sciences, Artificial Intelligence (cs.AI), Ontology, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Natural language processing, 050104 developmental & child psychology
Abstract

International audience; Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept drift i.e., unexpected changes in data distribution, causing most of models to be less accurate as time passes. To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic em-beddings. The experiments show accurate prediction with data from Dublin and Beijing.

Published
2017
Full Text
View/download PDF

8. Towards the Composition of Stateful and Independent Semantic Web Services

Author

Freddy Lecue, Alexandre Delteil, Alain Léger, Six, Grégory, Département Systèmes Multi-Agents (SMA-ENSMSE), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre G2I

Subjects
Web standards, medicine.medical_specialty, business.industry, Computer science, Distributed computing, computer.software_genre, Social Semantic Web, Stateful firewall, Scalability, medicine, Semantic Web Stack, Web service, business, WS-Policy, Semantic Web, computer, Web modeling, ComputingMilieux_MISCELLANEOUS
Abstract

Most of the work on automated semantic Web service composition has focused so far on two main levels of composition i.e., functional level and process level composition (respectively FLC and PLC from now). The former level of composition considered Web services as atomic components that can be executed in a single request-response step whereas the latter level studies in more details the protocol and the behavioural features of Web services. Since PLC i.e., a time and particularly space consuming level of composition makes difficult the scalability of composition-based applications, it seems interesting to restrict the composition of stateful but (only) independent Web services. Such a restriction make possible the composition of a large number of Web services in industrial scenarios, the whole with convincing results. In this paper we suggest to study the advantages to apply FLC together with β-composition in order to perform an automated end to end composition of stateful and independent services. In particular we focus on computational complexity results concerning the two models of composition i.e., the well-known PLC vs. the newest FLC+β-composition. Moreover we prove that FLC is an interesting and necessary level of composition to significantly reduce computational complexity not only in space but also in time.

Published
2008

9. DL Reasoning and AI Planning for Web Service Composition

Author

Alexandre Delteil, Alain Léger, Freddy Lecue, Six, Grégory, Département Systèmes Multi-Agents (SMA-ENSMSE), École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre G2I

Subjects
Theoretical computer science, Knowledge representation and reasoning, Programming language, Computer science, computer.software_genre, GeneralLiterature_MISCELLANEOUS, Description logic, Automated planning and scheduling, Automated reasoning, Situation calculus, Web service, computer, Semantic Web, Logic programming, ComputingMilieux_MISCELLANEOUS
Abstract

We claim that a key feature for correct and effective web service composition, and one that has largely been ignored,is the joint consideration of (semantic) causal links and causal laws, respectively in area of description logics (DL) and AI planning. In this paper we propose a means of specifying both causal links and laws into Web service composition by integrating DL reasoning and situation calculus. To this end an augmented and adapted version of the logic programming language Golog i.e., sclGolog is presented as a natural formalism not only for reasoning about the latter links and laws, but also for automatically composing services. sclGolog operates as an offline interpreter that supports n-ary sensing actions to retrieve conditional compositions of services. Lastly sclGolog has been implemented and tested in the context of telecommunication scenarios.

Published
2008

10. Using Comp-O to Build and Describe Component-Based Services

Author

Alary, Grégory, Hernandez, Nathalie, Arcangeli, Jean-Paul, Trouilhet, Sylvie, Bruel, Jean-Michel, Systèmes Multi-Agents Coopératifs (IRIT-SMAC), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées, MEthodes et ingénierie des Langues, des Ontologies et du DIscours (IRIT-MELODI), Université Toulouse - Jean Jaurès (UT2J), Université Toulouse III - Paul Sabatier (UT3), Smart Modeling for softw@re Research and Technology (IRIT-SM@RT), Kerry Taylor, Rafael Gonçalves, Freddy Lecue, Jun Yan, and TROUILHET, Sylvie

Subjects
OWL-S extension, [INFO]Computer Science [cs], ontology, [INFO] Computer Science [cs], emerging service description
Abstract

International audience; Comp-O is an extension of OWLS that we have proposed to describe the semantics of component-based services. Based on a proof-of-concept prototype, this demonstration shows how Comp-O is used to support assistance to developers of component-based services and automatically generate their descriptions for publication and discovery purposes.

Published
2020

11. Can You Imagine... A Language for Combinatorial Creativity?

Author

Colette Menard, Meghyn Bienvenu, Cyril Chapellier, Fabian M. Suchanek, Télécom ParisTech, STIM, Graphs for Inferences on Knowledge (GRAPHIK), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), sans affiliation, Paul Groth, Elena Simperl, Alasdair Gray, Marta Sabou, Markus Krötzsch, Freddy Lecue, Fabian Flöck, Yolanda Gil, Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Inria Sophia Antipolis - Méditerranée (CRISAM), and Sans affiliation

Subjects
Theoretical computer science, Degree (graph theory), Computer science, business.industry, media_common.quotation_subject, Order (ring theory), 02 engineering and technology, Creativity, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Description logic, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, media_common
Abstract

International audience; Combinatorial creativity combines existing concepts in a novel way in order to produce new concepts. For example, we can imagine jewelry that measures blood pressure. For this, we would combine the concept of jewelry with the capabilities of medical devices. In this paper, we concentrate on creating new concepts in the description logic EL. We propose a novel language to this effect, and study its properties and complexity. We show that our language can be used to model existing inventions and (to a limited degree) to generate new concepts.

Published
2016
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results