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Titre : Knowledge graph management and streaming in the context of edge computing Type de document : Thèse/HDR Auteurs : Weiqin Xu, Auteur ; Olivier Curé, Directeur de thèse Editeur : Champs-sur-Marne [France] : Université Gustave Eiffel Année de publication : 2021 Importance : 122 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université Gustave Eiffel, Spécialité InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] flux continu
[Termes IGN] informatique en nuage
[Termes IGN] internet des objets
[Termes IGN] langage de requête
[Termes IGN] module d'extension
[Termes IGN] ontologie
[Termes IGN] OWL
[Termes IGN] RDF
[Termes IGN] réseau sémantique
[Termes IGN] SPARQL
[Termes IGN] stockage de données
[Termes IGN] web sémantiqueIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Edge Computing proposes to distribute computation and data storage closer to original data sources. This technology is becoming an important trend in IT. This is mainly due to the emergence of the Internet of Things and its set of compact devices, eg sensors, actuators or gateways, whose computing and storing capacities are ever-increasing. Different from Cloud Computing, which targets large data centers, Edge Computing's computation distribution strategy can potentially reduce network pressure and make full use of computation power of edge devices.In order to support smart data processing at the edge of the network, a knowledge representation strategy is needed. In 2021, technologies belonging to the so-called Semantic Web are mature and robust enough to bring intelligence to Edge computing. These technologies correspond to the RDF (Resource Description Framework) data model, the RDFS (RDF Schema) and OWL (Web ontology Language) ontology languages and their associated reasoning services, the SPARQL query language. A cornerstone of such an approach is an Edge device compliant RDF database management system. However, most RDF stores are designed for powerful servers or Cloud Computing. These systems partly owe their efficiency to costly indexing strategies, ie based on multiples indexes.In the context of Edge computing, characterised by relatively limited memory footprint and computing power, it is not reasonable to use any of these RDF stores. Hence, a novel kind of RDF store is needed. In this work, we consider that some of its features must be an in-memory approach, low-memory footprint for both the system and its managed data, adapted query optimization techniques to make query processing as fast as possible. Moreover, reasoning at query run-time and stream processing are required by several of the use cases that we have identified in real-world situations.For the aim of compressing RDF data while maintaining querying speed, we make an extensive use of Succinct Data Structure (SDS) data structures to benefit from its data compression and high data retrieving speed simultaneously. This help us to get a self-indexed compact RDF store which does not require decompression operation. Our query processing approach is adapted to our storage layout and to standard SDS operations, namely access, rank and select. We prove the efficiency of our approach with thorough evaluation.In order to help the acceleration of RDFS reasoning, we have designed our system based on a semantic-aware encoding strategy named LiteMat. This encoding scheme, which has been developed and maintained by our research team, has been extended in the PhD thesis to support multiple inheritance, transitive and inverse properties. It thus extends the expressive power of addressed ontologies.In real IoT use cases, data are usually continuously coming from sensors or actuators. To address this issue, an extension of SuccinctEdge has been designed to handle those streaming data. This extension includes an extra data structure in our RDF store to process numeric data with time-based aggregations and an adapted streaming-SPARQL extension processor to permit the querying of streaming data. With the help of this extra data structure and the adapted query processor, one can easily query the dynamic RDF graph by a streaming-SPARQL query. However, query execution on a dynamic graph may have many repeating graph searching, which may heavily slow down the system. In order to solve this problem, we separate a query into dynamic part and static part. The result of the static part is computed once and stored all along the duration of the continuous query processing. Concerning the dynamic part, the corresponding result is combined with the static part result to generate the final result of each query execution. We prove that our streaming extension system is of low latency and of high throughput with good robustness and correctness properties. Note de contenu : 1- Introduction
2- Background knowledge
3- LiteMat, an encoding scheme for RDFS++
4- SuccinctEdge
5- Streaming SuccinctEdge
6- ConclusionNuméro de notice : 24026 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Gustave Eiffel : 2021 Organisme de stage : Laboratoire d’Informatique Gaspard Monge DOI : sans En ligne : https://tel.hal.science/tel-03697222/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101824
Titre : Learning digital geographies through geographical artificial intelligence Type de document : Thèse/HDR Auteurs : Pengyuan Liu, Auteur ; Stefano de Sabbata, Directeur de thèse ; Yu-Dong Zhang, Directeur de thèse Editeur : Leicester [Royaume-Uni] : University of Leicester Année de publication : 2021 Importance : 199 p. Format : 21 x 30 cm Note générale : bibliographie
A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy, Geology and EnvironmentLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de groupement
[Termes IGN] analyse socio-économique
[Termes IGN] apprentissage profond
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] croissance urbaine
[Termes IGN] détection de changement
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] données localisées des bénévoles
[Termes IGN] données spatiotemporelles
[Termes IGN] géomatique web
[Termes IGN] intelligence artificielle
[Termes IGN] Londres
[Termes IGN] réseau neuronal de graphes
[Termes IGN] réseau sémantique
[Termes IGN] système d'information urbain
[Termes IGN] zone urbaineIndex. décimale : THESE Thèses et HDR Résumé : (auteur) As the distinction between online and physical spaces rapidly degrades, digital platforms have become an integral component of how people’s everyday experiences are mediated. User-generated content (UGC) shared on such platforms provides insights into how users want to represent their everyday lives, which augments and reinforces our understanding of local communities through time and layers dynamic information across and over the geographic space. Inspired by the development of the newly arisen scientific disciplines within geography: geographical artificial intelligence (GeoAI), this thesis adopts deep learning approaches on graph representations of human dynamics illustrated through geotagged UGC to explore how place representations are augmented and reinforced through users’ spatial experiences by classifying their multimedia activities and identifying the spatial clusters of UGC at the urban scale. Having the place representations described through UGC, this thesis explores how these representations can be used in conjunction with various official spatial statistics to understand and predict the dynamic changes of the socio-economic characteristics of places. The principal contributions of this thesis are: (1) to provide frameworks with higher classification and prediction accuracy but requiring fewer sample data; thus, contributing to an advanced framework to summarise spatial characteristics of places; (2) to show that multimedia content provides rich information regarding places, the use of space, and people’s experience of the landscape; thus, benefiting a better understanding of place representations; (3) to illustrate that the spatial patterns of UGC can be adopted as a valuable proxy to understand urban development and neighbourhood change; (4) to reinforce the concept that Spatial is Special. Spatial processes are commonly spatially autocorrelated. The mainstream of machine learning methods do not explicitly incorporate the spatial or spatio-temporal component to address such a speciality of spatial data. This thesis highlights the importance of explicitly incorporating spatial or spatio-temporal components in geographical analysis models. Note de contenu : 1- Introduction
2- Towards quantitative digital geographies: Concepts, research and implications
3- Data and methods
4- Classification learning through a graph-based semi-supervised approach
5- Location estimation of social media content through a graph-based linkPrediction
6- Urban change modelling with spatial knowledge graphs
7- DiscussionNuméro de notice : 28629 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD Thesis: Geology and Environment: Leicester : 2021 DOI : sans En ligne : https://leicester.figshare.com/articles/thesis/Learning_Digital_Geographies_thro [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99618 Représentation sémantique de données géospatiales au service de l'analyse de changements / Jordan Dorne (2021)
Titre : Représentation sémantique de données géospatiales au service de l'analyse de changements Type de document : Thèse/HDR Auteurs : Jordan Dorne, Auteur ; Nathalie Aussenac-Gilles, Directeur de thèse Editeur : Toulouse : Université de Toulouse 2 Jean Jaurès Année de publication : 2021 Importance : 154 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse, Délivré par l'Université Toulouse 2 - Jean Jaurès, Spécialité Informatique et TélécommunicationsLangues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse diachronique
[Termes IGN] détection de changement
[Termes IGN] image satellite
[Termes IGN] métadonnées
[Termes IGN] observation de la Terre
[Termes IGN] occupation du sol
[Termes IGN] ontologie
[Termes IGN] représentation des connaissances
[Termes IGN] réseau sémantique
[Termes IGN] web sémantique
[Termes IGN] zone d'intérêtIndex. décimale : THESE Thèses et HDR Résumé : (auteur) La détection de changements à partir d’images satellitaires d’observation de la Terre est une tâche utile pour surveiller des évolutions naturelles ou liées à l’activité humaines, mais aussi l’impact d’événements ponctuels (incendies, inondations, etc.). L’utilisation d’apprentissage automatique pour détecter les zones de changements produit des résultats de plus en plus précis, sans toutefois fournir d’information sur la nature ou la cause de ces changements. Pour répondre à ce type de besoin, notre thèse vise à développer des solutions fondées sur des vocabulaires formels et des ontologies, sur la représentation des connaissances, l'annotation et l'intégration sémantique de méta-données associées aux images satellitaires et plus généralement aux données géolocalisées. En effet, les techniques de sémantisation sont un moyen d’offrir une interprétation intelligente des données. Le cas d’étude retenu pour illustrer l’apport de cette approche est le suivi des changements à différents pas de temps et différentes échelles de restitution. L'objectif est d'enrichir les métadonnées issues des flux d'images en leur associant des catégories conceptuelles qui leur donnent du sens, de les coupler à des pré-traitements qui répondent aux exigences spécifiques à l’étude des changements et de les intégrer à d'autres informations géographiques disponibles (données climatiques et météorologiques, démographiques, etc. suivant les besoins). La thèse vise donc à montrer l'apport des métadonnées sémantiques pour la surveillance des changements, mais aussi à évaluer le passage à l'échelle des techniques de sémantisation, notamment de la représentation des données dans les entrepôts sémantiques. Nous proposons un processus qui gère le cycle complet de génération et exploitation de graphes de connaissances à partir de rasters issus de la télédétection et de données issues de l’open data. Les caractéristiques innovantes de ce processus sont les suivantes : i. Un algorithme permettant l’identification automatique de régions d’intérêts (ROI) associées à des valeurs similaires d’un indicateur calculé à partir d’une image satellite, et assurant ainsi un découpage géographique précis comme référence pour l’intégration de données. ii. Une approche orientée sémantique pour la génération de graphes de connaissances depuis différentes sources. Note de contenu : 1- Introduction
2- Des images satellitaires à l’étude des changements sur la Terre à l’aide du Web sémantique
3- Etat de l’art
4- Proposition
5- Expérimentations
6- Conclusion et perspectivesNuméro de notice : 15269 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique et Télécommunications : Toulouse 2 : 2021 Organisme de stage : MEthodes et ingénierie des Langues, des Ontologies et du DIscours MELODI (IRIT) DOI : sans En ligne : https://hal.science/tel-03618363v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100692
Titre : Spatial dataset search: Building a dedicated knowledge graph Type de document : Article/Communication Auteurs : Mehdi Zrhal , Auteur ; Bénédicte Bucher , Auteur ; Marie-Dominique Van Damme , Auteur ; Fayçal Hamdi , Auteur Editeur : AGILE Alliance Année de publication : 2021 Projets : 1-Pas de projet / Conférence : AGILE 2021, 24th AGILE Conference on Geographic Information Science 19/07/2021 22/07/2021 Aurora Colorado - Etats-Unis OA Proceedings Importance : 5 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données
[Termes IGN] découverte de connaissances
[Termes IGN] données massives
[Termes IGN] données ouvertes
[Termes IGN] graphe
[Termes IGN] INSPIRE
[Termes IGN] jeu de données localisées
[Termes IGN] précision sémantique
[Termes IGN] recherche d'information géographique
[Termes IGN] requête spatiale
[Termes IGN] réseau sémantique
[Termes IGN] ressources web
[Termes IGN] service web géographique
[Termes IGN] terminologie
[Termes IGN] web des données
[Termes IGN] web sémantique géolocaliséRésumé : (auteur) A growing number of spatial datasets are published every year. These can usually be found in dedicated web portals with different structures and specificities. However, finding the dataset that fits user needs is a real challenge as prior knowledge of these portals is needed to retrieve it efficiently. In this article, we present the problem of spatial dataset search and how the use of a geographic Knowledge Graph could improve it. A proposed direction for future work, ex-tending these contributions, is then presented. Numéro de notice : C2021-008 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/agile-giss-2-43-2021 En ligne : https://doi.org/10.5194/agile-giss-2-43-2021 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97855 Spatial Linked Data in Europe: Report from Spatial Linked Data Session at Knowledge Graph in Action, October 6th, 2020, on-line conference / Bénédicte Bucher (February 2021)
Titre : Spatial Linked Data in Europe: Report from Spatial Linked Data Session at Knowledge Graph in Action, October 6th, 2020, on-line conference Type de document : Rapport Auteurs : Bénédicte Bucher , Auteur ; Erwin Folmer, Auteur ; Rob Brennan, Auteur ; Wouter Beek, Auteur ; Elio Hbeich, Auteur ; Falk Würriehausen, Auteur ; Lexi Rowland, Auteur ; Ricardo Alonso Maturana, Auteur ; Elena Alvarado, Auteur ; Raf Buyle, Auteur ; Pasquale Di Donato, Auteur Editeur : Dublin : European Spatial Data Research EuroSDR Année de publication : February 2021 Collection : EuroSDR official publication, ISSN 0257-0505 num. 73 Projets : 1-Pas de projet / Conférence : KiA 2020, Knowledge Graph in Action: DBpedia, Linked Geodata and Geo-information Integration 06/10/2020 06/10/2020 en ligne Colorado - Etats-Unis OA Proceedings Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] données localisées
[Termes IGN] réseau sémantique
[Termes IGN] SPARQL
[Termes IGN] web des donnéesRésumé : (éditeur) In 2020, the Knowledge Graph in Action (KGiA) online conference was organized as a joint event gathering three annual events with a common interest on producing, consolidating data, and supporting their joint reuse and different specific focuses within this common interest: the DBpedia day which more specifically focuses on advancing DBpedia, the EuroSDR Spatial Linked Data day which more specifically focuses on spatial linked data, and the EuroSDR VGI event which more specifically focuses on volunteered geographic information.
The event was organized around distinct parallel sessions dedicated to each event and joint plenary sessions. During plenary sessions, keynotes related to the modelling and the usage of spatial knowledge, in particular in the form of knowledge graphs, at the junction of these communities. Carsten Hoyer Click from the German Aerospace Center presented the design of a development of a distributed data infrastructure for energy systems analysis. Semantic Web techniques are used to interconnect data from different sources and prepare the integrated data layers needed for energy models. Peter Mooney from Maynooth University presented opportunities for more collaboration and geo-information integration between volunteered geographical information, the governmental agencies and the geospatial research communities. He insists on the complexity of data integration, which is always present even when flowcharts hide this complexity and on the semantics aspect being the more difficult to solve. Here the exploration of machine learning and artificial intelligence are the dominant trend. Marinos Kavouras from the National Technical University of Athens extended upon the need for our society to develop competences to interpret all the data available, in big quantities, to make sense of complex phenomena. He argues that space has been one of the strongest pivotal notions in semantically linking all kinds of data. Developing geospatial literacy skills is needed to empower people with a modern cartographic language, an indispensable communication and cognitive tool. Krzystof Janowicz from the University of California presented the application of knowledge graphs to address challenges at the interface between humans and their environment like for example crisis management. The information currently provided to end users is based on the integration of highly heterogeneous data from different fields of expertise and can lead to misinterpretation. Knowledge graphs and their technologies offer perspectives and lots of challenges still ahead to make data AIready at the level of individual statements instead of merely offering access to datasets, to provide additional contextual background information.
The rest of this report concerns presentations and exchanges that took place during the EuroSDR Spatial Linked Data sessions. EuroSDR is a not for profit association established since 1953 for the purpose of applied research and innovation in spatial data provision, distribution and usage in Europe. It gathers national mapping agencies, research institutes, universities and industries. Its activity on Linked data has two main objectives : 1) assessing the value of this technology in addressing current challenges in spatial data provision, distribution and exploitation, 2) identifying new needs for spatial data provision and distribution that have emerged with this technology. This activity started in 2015 and is grounded on big events -like the KGiA conference-, smaller working sessions, and since 2019 a technical group. EuroSDR LD group gathers participants with an interest in Spatial Linked Data (SLD). SLD can be characterized as a domain of applied research and innovation at the overlap between Linked Data and spatial data. Its finality is data production, sharing and reuse on the Web to support decisions with a geographical characteristic. Space is an important dimension to interconnect different information and achieve the Linked Data vision, for example to valorise linked data of different domains if any spatial footprints can be added to associate them with a geographical context or to detect possible connections between different data not connected otherwise. Vice versa, graph based models are promising approaches to address some unsolved issues in spatial data infrastructures.
The section “National presentations” reports on updates presented by different agencies or partners on latest developments, focusing on a given territory. These developments are either in a prototype stage or were presented as fully operational applications.
The remaining sections report on more technology oriented presentations.
The section entitled “Interfacing more users with data and related technologies” present results and approaches oriented on the appropriation of data by potential users, despite possible silos created by the complexity of data technologies, including linked data, was addressed in several presentations. The self-service GIS vision presented by the Kadaster is to support the querying and exploitation of complex data by more users beyond the limited Geomatics Community. The tools developed by Triply, in particular a wizard, focus on giving access to the potential of Linked data to users who are no LD specialists thanks to user oriented interfaces. Besides, a well known usage of Knowledge Graphs is to improve user access to resources -as on Amazon, AirBnB, Google and other platforms, based on the modelling in a knowledge graph of important knowledge related to the resources and also related to the usage. This can be applied in particular to specific resources: the data themselves. The discovery of “fitted for use” datasets, especially spatial datasets is a pending issue given the wide range of users on the one hand, and the difficulty to broker and compare datasets potentially relevant on the other hand. A new EuroSDR initiative targeting the design of an open European Knowledge Graph of geographical digital assets was presented. It consists of the collaborative creation of an open Knowledge Graph about digital assets in Europe, based on the EuroSDR LD Group sandbox and EuroSDR community as a whole.
The last section reported on GeoSPARQL focused presentations. A key technology associated with Linked Data and the Knowledge Graph is GeoSPARQL. One presentation focused on requirements from the domain of buildings and on the type of spatial queries that should be addressed to 3D linked data. Another presentation concerned the GeoSPARQL benchmark on the EuroSDR sandbox.Note de contenu : 1- Introduction
2- National presentations on spatial linked data activities
3- Interfacing more users with data and related technologies
4- GeoSPARQL focused presentations
5- Discussion and perspectivesNuméro de notice : 17014 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Rapport nature-HAL : DirectOuvrColl/Actes DOI : sans En ligne : http://www.eurosdr.net/sites/default/files/uploaded_files/eurosdr_publication_nd [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98449 Conciliating perspectives from mapping agencies and web of data on successful European SDIs: toward a European geographic knowledge graph / Bénédicte Bucher in ISPRS International journal of geo-information, vol 9 n° 2 (February 2020)PermalinkPermalinkPermalinkPermalinkComputing and querying strict, approximate, and metrically refined topological relations in linked geographic data / Blake Regalia in Transactions in GIS, vol 23 n° 3 (June 2019)PermalinkDeeply integrating linked data with geographic information systems / Gengchen Mai in Transactions in GIS, vol 23 n° 3 (June 2019)PermalinkGeographic Knowledge Graph (GeoKG): A formalized geographic knowledge representation / Shu Wang in ISPRS International journal of geo-information, vol 8 n° 4 (April 2019)PermalinkGeoTxt: A scalable geoparsing system for unstructured text geolocation / Morteza Karimzadeh in Transactions in GIS, vol 23 n° 1 (February 2019)PermalinkA structural-lexical measure of semantic similarity for geo-knowledge graphs / Andrea Ballatore in ISPRS International journal of geo-information, vol 4 n°2 (June 2015)PermalinkThematic signatures for cleansing and enriching place-related linked data / Benjamin Adams in International journal of geographical information science IJGIS, vol 29 n° 4 (April 2015)Permalink