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Performance benchmark on semantic web repositories for spatially explicit knowledge graph applications / Wenwen Li in Computers, Environment and Urban Systems, vol 98 (December 2022)
Titre : Performance benchmark on semantic web repositories for spatially explicit knowledge graph applications Type de document : Article/Communication Auteurs : Wenwen Li, Auteur ; Sizhe Wang, Auteur ; Sheng wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101884 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données
[Termes IGN] base de données relationnelles
[Termes IGN] entrepôt de données
[Termes IGN] ontologie
[Termes IGN] RDF
[Termes IGN] référentiel sémantique
[Termes IGN] requête spatiale
[Termes IGN] réseau sémantique
[Termes IGN] SPARQL
[Termes IGN] stockage de données
[Termes IGN] test de performance
[Termes IGN] web sémantiqueRésumé : (auteur) Knowledge graph has become a cutting-edge technology for linking and integrating heterogeneous, cross-domain datasets to address critical scientific questions. As big data has become prevalent in today's scientific analysis, semantic data repositories that can store and manage large knowledge graph data have become critical in successfully deploying spatially explicit knowledge graph applications. This paper provides a comprehensive evaluation of the popular semantic data repositories and their computational performance in managing and providing semantic support for spatial queries. There are three types of semantic data repositories: (1) triple store solutions (RDF4j, Fuseki, GraphDB, Virtuoso), (2) property graph databases (Neo4j), and (3) an Ontology-Based Data Access (OBDA) approach (Ontop). Experiments were conducted to compare each repository's efficiency (e.g., query response time) in handling geometric, topological, and spatial-semantic related queries. The results show that Virtuoso achieves the overall best performance in both non-spatial and spatial-semantic queries. The OBDA solution, Ontop, has the second-best query performance in spatial and complex queries and the best storage efficiency, requiring the least data-to-RDF conversion efforts. Other triple store solutions suffer from various issues that cause performance bottlenecks in handling spatial queries, such as inefficient memory management and lack of proper query optimization. Numéro de notice : A2022-720 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101884 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101884 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101654
in Computers, Environment and Urban Systems > vol 98 (December 2022) . - n° 101884[article]
Titre : Stop-and-move sequence expressions over semantic trajectories Type de document : Article/Communication Auteurs : Yenier Torres Izquierdo, Auteur ; Grettel Monteagudo Garcia, Auteur ; Marco A. Casanova, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 793 - 818 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] appariement sémantique
[Termes IGN] exploration de données
[Termes IGN] image Flickr
[Termes IGN] information sémantique
[Termes IGN] intelligence artificielle
[Termes IGN] langage de requête
[Termes IGN] RDF
[Termes IGN] SPARQLRésumé : (auteur) Stop-and-move semantic trajectories are segmented trajectories where the stops and moves are semantically enriched with additional data. A query language for semantic trajectory datasets has to include selectors for stops or moves based on their enrichments and sequence expressions that define how to match the results of selectors with the sequence the semantic trajectory defines. This article addresses the problem of searching semantic trajectories, using stop-and-move sequence expressions. The article first proposes a formal framework to define semantic trajectories and introduces stop-and-move sequence expressions, with well-defined syntax and semantics, which act as an expressive query language for semantic trajectories. Then, it describes a concrete semantic trajectory model in RDF, defines SPARQL stop-and-move sequence expressions and discusses strategies to compile such expressions into SPARQL queries. Lastly, the article specifies user-friendly keyword search expressions over semantic trajectories based on the use of keywords to specify stop-and-move queries, and the adoption of terms with predefined semantics to compose sequence expressions. It then shows how to compile such keyword search expressions into SPARQL queries. Finally, it provides a proof-of-concept experiment over a semantic trajectory dataset constructed with user-generated content from Flickr, combined with Wikipedia data. Numéro de notice : A2021-270 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1793157 Date de publication en ligne : 20/07/2020 En ligne : https://doi.org/10.1080/13658816.2020.1793157 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97328
in International journal of geographical information science IJGIS > vol 35 n° 4 (April 2021) . - pp 793 - 818[article]
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 : Mining the semantic Web for OWL axioms Titre original : Fouille du Web sémantique à la recherche d'axiomes OWL Type de document : Thèse/HDR Auteurs : Thu Huong Nguyen, Auteur ; Andrea Tettamanzi, Directeur de thèse Editeur : Nice : Université Côte d'Azur Année de publication : 2021 Importance : 175 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat présentée en vue de l’obtention du grade de docteur en Informatique de l’Université Côte d’AzurLangues : Français (fre) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] algorithme génétique
[Termes IGN] données ouvertes
[Termes IGN] exploration de données
[Termes IGN] logique floue
[Termes IGN] ontologie
[Termes IGN] OWL
[Termes IGN] RDF
[Termes IGN] théorie des possibilités
[Termes IGN] web des données
[Termes IGN] web sémantiqueIndex. décimale : THESE Thèses et HDR Résumé : (auteur) In the Semantic Web era, Linked Open Data (LOD) is its most successful implementation, which currently contains billions of RDF (Resource Data Framework) triples derived from multiple, distributed, heterogeneous sources. The role of a general semantic schema, represented as an ontology, is essential to ensure the correctness and consistency in LOD and make it possible to infer implicit knowledge by reasoning. The growth of LOD creates an opportunity for the discovery of
ontological knowledge from its raw RDF data itself to enrich relevant knowledge bases. In this work, we aim at discovering schema-level knowledge in the form of axioms encoded in OWL (Ontology Web Language) from RDF data. The approaches to automated generation of the axioms from recorded RDF facts on the Web may be regarded as a case of inductive reasoning and ontology learning. The instances, represented by RDF triples, play the role of specific observations, from which axioms can be extracted by generalization. Based on the insight that discovering new knowledge is essentially an evolutionary, whereby hypotheses are generated by some heuristic mechanism and then tested against the available evidence, so that only the best hypotheses survive, we propose a model applying Grammatical Evolution, one type of evolutionary algorithm, to mine OWL axioms from an RDF data repository. In addition, we specialize the model for the specific problem of learning OWL class disjointness axioms, along with the experiments performed on DBpedia, one of the prominent examples of LOD. Furthermore, we use different axiom scoring functions based on possibility theory, which are well-suited to the open world assumption scenario of LOD, to evaluate the quality of discovered axioms. Specifically, we proposed a set of measures to build objective functions based on single-objective and multi-objective models, respectively. Finally, in order to validate it, the performance of our approach is evaluated against subjective and objective benchmarks, and is also compared to the main state-of-the-art systems.Note de contenu : 1- Introduction
2- Foundation
3- Literature review
4- Learning OWL axioms from RDF data
5- Axiom evaluation
6- Grammatical evolution models toward class disjointness axiom discovery
7- A multi-objective GE approach to class disjointness axioms discovery
8- Conclusions & perspectivesNuméro de notice : 28614 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/SOCIETE NUMERIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Côte d'Azur : 2021 Organisme de stage : I3S DOI : sans En ligne : https://hal.science/tel-03406784/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99492
Titre : A framework for connecting two interoperability universes: OGC Web Feature Services and Linked Data Type de document : Article/Communication Auteurs : Luis Vilches-Blazquez, Auteur ; Jhonny Saavedra, Auteur Année de publication : 2019 Article en page(s) : pp 22 - 47 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] biodiversité
[Termes IGN] cadre conceptuel
[Termes IGN] données localisées
[Termes IGN] données multisources
[Termes IGN] GML
[Termes IGN] interopérabilité
[Termes IGN] partage de données localisées
[Termes IGN] RDF
[Termes IGN] regroupement de données
[Termes IGN] web des données
[Termes IGN] Web Feature Service
[Termes IGN] web sémantiqueRésumé : (auteur) Diverse studies have shown that about 80% of all available data are related to a spatial location. Most of these geospatial data are available as structured and semi‐structured datasets, and often use distinct data models, are encoded using ad‐hoc vocabularies, and sometimes are being published in non‐standard formats. Hence, these data are isolated within silos and cannot be shared and integrated across organizations and communities. Spatial Data Infrastructures (SDIs) have emerged and contributed to significantly enhance data discovery and accessibility based on OGC (Open Geospatial Consortium) Web services. However, finding, accessing, and using data disseminated through SDIs are still difficult for non‐expert users. Overcoming the current geospatial data challenges involves adopting the best practices to expose, share, and integrate data on the Web, that is, Linked Data. In this article, we have developed a framework for generating, enriching, and exploiting geospatial Linked Data from multiple and heterogeneous geospatial data sources. This proposal allows connecting two interoperability universes (SDIs, more specifically Web Feature Services, WFS, and Semantic Web technologies), which is evaluated through a study case in the (geo)biodiversity domain. Numéro de notice : A2019-089 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12496 Date de publication en ligne : 28/11/2018 En ligne : https://doi.org/10.1111/tgis.12496 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92236
in Transactions in GIS > vol 23 n° 1 (February 2019) . - pp 22 - 47[article]
