Semantic Web最新文献

{"title":"Wikidata subsetting: Approaches, tools, and evaluation","authors":"Seyed Amir Hosseini Beghaeiraveri, J. E. Labra Gayo, A. Waagmeester, Ammar Ammar, Carolina Gonzalez, D. Slenter, Sabah Ul-Hasan, E. Willighagen, Fiona McNeill, A. Gray","doi":"10.3233/sw-233491","DOIUrl":"https://doi.org/10.3233/sw-233491","url":null,"abstract":"Wikidata is a massive Knowledge Graph (KG), including more than 100 million data items and nearly 1.5 billion statements, covering a wide range of topics such as geography, history, scholarly articles, and life science data. The large volume of Wikidata is difficult to handle for research purposes; many researchers cannot afford the costs of hosting 100 GB of data. While Wikidata provides a public SPARQL endpoint, it can only be used for short-running queries. Often, researchers only require a limited range of data from Wikidata focusing on a particular topic for their use case. Subsetting is the process of defining and extracting the required data range from the KG; this process has received increasing attention in recent years. Specific tools and several approaches have been developed for subsetting, which have not been evaluated yet. In this paper, we survey the available subsetting approaches, introducing their general strengths and weaknesses, and evaluate four practical tools specific for Wikidata subsetting – WDSub, KGTK, WDumper, and WDF – in terms of execution performance, extraction accuracy, and flexibility in defining the subsets. Results show that all four tools have a minimum of 99.96% accuracy in extracting defined items and 99.25% in extracting statements. The fastest tool in extraction is WDF, while the most flexible tool is WDSub. During the experiments, multiple subset use cases have been defined and the extracted subsets have been analyzed, obtaining valuable information about the variety and quality of Wikidata, which would otherwise not be possible through the public Wikidata SPARQL endpoint.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"71 s1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139153989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An ontology of 3D environment where a simulated manipulation task takes place (ENVON)","authors":"Yingshen Zhao, Arkopaul Sarkar, Linda Elmhadhbi, Mohamed-Hedi Karray, P. Fillatreau, B. Archimède","doi":"10.3233/sw-233460","DOIUrl":"https://doi.org/10.3233/sw-233460","url":null,"abstract":"Thanks to the advent of robotics in shopfloor and warehouse environments, control rooms need to seamlessly exchange information regarding the dynamically changing 3D environment to facilitate tasks and path planning for the robots. Adding to the complexity, this type of environment is heterogeneous as it includes both free space and various types of rigid bodies (equipment, materials, humans etc.). At the same time, 3D environment-related information is also required by the virtual applications (e.g., VR techniques) for the behavioral study of CAD-based product models or simulation of CNC operations. In past research, information models for such heterogeneous 3D environments are often built without ensuring connection among different levels of abstractions required for different applications. For addressing such multiple points of view and modelling requirements for 3D objects and environments, this paper proposes an ontology model that integrates the contextual, topologic, and geometric information of both the rigid bodies and the free space. The ontology provides an evolvable knowledge model that can support simulated task-related information in general. This ontology aims to greatly improve interoperability as a path planning system (e.g., robot) and will be able to deal with different applications by simply updating the contextual semantics related to some targeted application while keeping the geometric and topological models intact by leveraging the semantic link among the models.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"308 ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139173378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NeuSyRE: Neuro-symbolic visual understanding and reasoning framework based on scene graph enrichment","authors":"M. J. Khan, John G. Breslin, Edward Curry","doi":"10.3233/sw-233510","DOIUrl":"https://doi.org/10.3233/sw-233510","url":null,"abstract":"Exploring the potential of neuro-symbolic hybrid approaches offers promising avenues for seamless high-level understanding and reasoning about visual scenes. Scene Graph Generation (SGG) is a symbolic image representation approach based on deep neural networks (DNN) that involves predicting objects, their attributes, and pairwise visual relationships in images to create scene graphs, which are utilized in downstream visual reasoning. The crowdsourced training datasets used in SGG are highly imbalanced, which results in biased SGG results. The vast number of possible triplets makes it challenging to collect sufficient training samples for every visual concept or relationship. To address these challenges, we propose augmenting the typical data-driven SGG approach with common sense knowledge to enhance the expressiveness and autonomy of visual understanding and reasoning. We present a loosely-coupled neuro-symbolic visual understanding and reasoning framework that employs a DNN-based pipeline for object detection and multi-modal pairwise relationship prediction for scene graph generation and leverages common sense knowledge in heterogenous knowledge graphs to enrich scene graphs for improved downstream reasoning. A comprehensive evaluation is performed on multiple standard datasets, including Visual Genome and Microsoft COCO, in which the proposed approach outperformed the state-of-the-art SGG methods in terms of relationship recall scores, i.e. Recall@K and mean Recall@K, as well as the state-of-the-art scene graph-based image captioning methods in terms of SPICE and CIDEr scores with comparable BLEU, ROGUE and METEOR scores. As a result of enrichment, the qualitative results showed improved expressiveness of scene graphs, resulting in more intuitive and meaningful caption generation using scene graphs. Our results validate the effectiveness of enriching scene graphs with common sense knowledge using heterogeneous knowledge graphs. This work provides a baseline for future research in knowledge-enhanced visual understanding and reasoning. The source code is available at https://github.com/jaleedkhan/neusire.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"68 11","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139004636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using semantic story maps to describe a territory beyond its map","authors":"Valentina Bartalesi, Gianpaolo Coro, Emanuele Lenzi, Nicolò Pratelli, Pasquale Pagano, Francesco Felici, Michele Moretti, Gianluca Brunori","doi":"10.3233/sw-233485","DOIUrl":"https://doi.org/10.3233/sw-233485","url":null,"abstract":"<h4><span>Abstract</span></h4><p>The paper presents the Story Map Building and Visualizing Tool (SMBVT) that allows users to create story maps within a collaborative environment and a usable Web interface. It is entirely open-source and published as a free-to-use solution. It uses Semantic Web technologies in the back-end system to represent stories through a reference ontology for representing narratives. It builds up a user-shared semantic knowledge base that automatically interconnects all stories and seamlessly enables collaborative story building. Finally, it operates within an Open-Science oriented e-Infrastructure, which enables data and information sharing within communities of narrators, and adds multi-tenancy, multi-user, security, and access-control facilities. SMBVT represents narratives as a network of spatiotemporal events related by semantic relations and standardizes the event descriptions by assigning internationalized resource identifiers (IRIs) to the event <i>components</i>, i.e., the entities that take part in the event (e.g., persons, objects, places, concepts). The tool automatically saves the collected knowledge as a Web Ontology Language (OWL) graph and openly publishes it as Linked Open Data. This feature allows connecting the story events to other knowledge bases. To evaluate and demonstrate our tool, we used it to describe the Apuan Alps territory in Tuscany (Italy). Based on a user-test evaluation, we assessed the tool’s effectiveness at building story maps and the ability of the produced story to describe the territory beyond the map.</p>","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"33 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138689467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sem@ K: Is my knowledge graph embedding model semantic-aware?","authors":"Nicolas Hubert, Pierre Monnin, Armelle Brun, Davy Monticolo","doi":"10.3233/sw-233508","DOIUrl":"https://doi.org/10.3233/sw-233508","url":null,"abstract":"<h4><span>Abstract</span></h4><p>Using knowledge graph embedding models (KGEMs) is a popular approach for predicting links in knowledge graphs (KGs). Traditionally, the performance of KGEMs for link prediction is assessed using rank-based metrics, which evaluate their ability to give high scores to ground-truth entities. However, the literature claims that the KGEM evaluation procedure would benefit from adding supplementary dimensions to assess. That is why, in this paper, we extend our previously introduced metric Sem@<i>K</i> that measures the capability of models to predict valid entities w.r.t. domain and range constraints. In particular, we consider a broad range of KGs and take their respective characteristics into account to propose different versions of Sem@<i>K</i>. We also perform an extensive study to qualify the abilities of KGEMs as measured by our metric. Our experiments show that Sem@<i>K</i> provides a new perspective on KGEM quality. Its joint analysis with rank-based metrics offers different conclusions on the predictive power of models. Regarding Sem@<i>K</i>, some KGEMs are inherently better than others, but this semantic superiority is not indicative of their performance w.r.t. rank-based metrics. In this work, we generalize conclusions about the relative performance of KGEMs w.r.t. rank-based and semantic-oriented metrics at the level of families of models. The joint analysis of the aforementioned metrics gives more insight into the peculiarities of each model. This work paves the way for a more comprehensive evaluation of KGEM adequacy for specific downstream tasks.</p>","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"27 15 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138689409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differential privacy and SPARQL","authors":"C. Buil-Aranda, Jorge Lobo, Federico Olmedo","doi":"10.3233/sw-233474","DOIUrl":"https://doi.org/10.3233/sw-233474","url":null,"abstract":"Differential privacy is a framework that provides formal tools to develop algorithms to access databases and answer statistical queries with quantifiable accuracy and privacy guarantees. The notions of differential privacy are defined independently of the data model and the query language at steak. Most differential privacy results have been obtained on aggregation queries such as counting or finding maximum or average values, and on grouping queries over aggregations such as the creation of histograms. So far, the data model used by the framework research has typically been the relational model and the query language SQL. However, effective realizations of differential privacy for SQL queries that required joins had been limited. This has imposed severe restrictions on applying differential privacy in RDF knowledge graphs and SPARQL queries. By the simple nature of RDF data, most useful queries accessing RDF graphs will require intensive use of joins. Recently, new differential privacy techniques have been developed that can be applied to many types of joins in SQL with reasonable results. This opened the question of whether these new results carry over to RDF and SPARQL. In this paper we provide a positive answer to this question by presenting an algorithm that can answer counting queries over a large class of SPARQL queries that guarantees differential privacy, if the RDF graph is accompanied with semantic information about its structure. We have implemented our algorithm and conducted several experiments, showing the feasibility of our approach for large graph databases. Our aim has been to present an approach that can be used as a stepping stone towards extensions and other realizations of differential privacy for SPARQL and RDF.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"8 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138980226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"QALD-10 – The 10th challenge on question answering over linked data","authors":"Ricardo Usbeck, Xiongliang Yan, A. Perevalov, Longquan Jiang, Julius Schulz, Angelie Kraft, Cedric Möller, Junbo Huang, Jan Reineke, Axel-Cyrille Ngonga Ngomo, Muhammad Saleem, Andreas Both","doi":"10.3233/sw-233471","DOIUrl":"https://doi.org/10.3233/sw-233471","url":null,"abstract":"Knowledge Graph Question Answering (KGQA) has gained attention from both industry and academia over the past decade. Researchers proposed a substantial amount of benchmarking datasets with different properties, pushing the development in this field forward. Many of these benchmarks depend on Freebase, DBpedia, or Wikidata. However, KGQA benchmarks that depend on Freebase and DBpedia are gradually less studied and used, because Freebase is defunct and DBpedia lacks the structural validity of Wikidata. Therefore, research is gravitating toward Wikidata-based benchmarks. That is, new KGQA benchmarks are created on the basis of Wikidata and existing ones are migrated. We present a new, multilingual, complex KGQA benchmarking dataset as the 10th part of the Question Answering over Linked Data (QALD) benchmark series. This corpus formerly depended on DBpedia. Since QALD serves as a base for many machine-generated benchmarks, we increased the size and adjusted the benchmark to Wikidata and its ranking mechanism of properties. These measures foster novel KGQA developments by more demanding benchmarks. Creating a benchmark from scratch or migrating it from DBpedia to Wikidata is non-trivial due to the complexity of the Wikidata knowledge graph, mapping issues between different languages, and the ranking mechanism of properties using qualifiers. We present our creation strategy and the challenges we faced that will assist other researchers in their future work. Our case study, in the form of a conference challenge, is accompanied by an in-depth analysis of the created benchmark.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"44 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139219641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A semantic framework for condition monitoring in Industry 4.0 based on evolving knowledge bases","authors":"Franco Giustozzi, Julien Saunier, Cecilia Zanni-Merk","doi":"10.3233/sw-233481","DOIUrl":"https://doi.org/10.3233/sw-233481","url":null,"abstract":"In Industry 4.0, factory assets and machines are equipped with sensors that collect data for effective condition monitoring. This is a difficult task since it requires the integration and processing of heterogeneous data from different sources, with different temporal resolutions and underlying meanings. Ontologies have emerged as a pertinent method to deal with data integration and to represent manufacturing knowledge in a machine-interpretable way through the construction of semantic models. Ontologies are used to structure knowledge in knowledge bases, which also contain instances and information about these data. Thus, a knowledge base provides a sort of virtual representation of the different elements involved in a manufacturing process. Moreover, the monitoring of industrial processes depends on the dynamic context of their execution. Under these circumstances, the semantic model must provide a way to represent this evolution in order to represent in which situation(s) a resource is in during the execution of its tasks to support decision making. This paper proposes a semantic framework to address the evolution of knowledge bases for condition monitoring in Industry 4.0. To this end, firstly we propose a semantic model (the COInd4 ontology) for the manufacturing domain that represents the resources and processes that are part of a factory, with special emphasis on the context of these resources and processes. Relevant situations that combine sensor observations with domain knowledge are also represented in the model. Secondly, an approach that uses stream reasoning to detect these situations that lead to potential failures is introduced. This approach enriches data collected from sensors with contextual information using the proposed semantic model. The use of stream reasoning facilitates the integration of data from different data sources, different temporal resolutions as well as the processing of these data in real time. This allows to derive high-level situations from lower-level context and sensor information. Detecting situations can trigger actions to adapt the process behavior, and in turn, this change in behavior can lead to the generation of new contexts leading to new situations. These situations can have different levels of severity, and can be nested in different ways. Dealing with the rich relations among situations requires an efficient approach to organize them. Therefore, we propose a method to build a lattice, ordering those situations depending on the constraints they rely on. This lattice represents a road-map of all the situations that can be reached from a given one, normal or abnormal. This helps in decision support, by allowing the identification of the actions that can be taken to correct the abnormality avoiding in this way the interruption of the manufacturing processes. Finally, an industrial application scenario for the proposed approach is described.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134947158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"INK: Knowledge graph representation for efficient and performant rule mining","authors":"Bram Steenwinckel, Filip De Turck, Femke Ongenae","doi":"10.3233/sw-233495","DOIUrl":"https://doi.org/10.3233/sw-233495","url":null,"abstract":"Semantic rule mining can be used for both deriving task-agnostic or task-specific information within a Knowledge Graph (KG). Underlying logical inferences to summarise the KG or fully interpretable binary classifiers predicting future events are common results of such a rule mining process. The current methods to perform task-agnostic or task-specific semantic rule mining operate, however, a completely different KG representation, making them less suitable to perform both tasks or incorporate each other’s optimizations. This also results in the need to master multiple techniques for both exploring and mining rules within KGs, as well losing time and resources when converting one KG format into another. In this paper, we use INK, a KG representation based on neighbourhood nodes of interest to mine rules for improved decision support. By selecting one or two sets of nodes of interest, the rule miner created on top of the INK representation will either mine task-agnostic or task-specific rules. In both subfields, the INK miner is competitive to the currently state-of-the-art semantic rule miners on 14 different benchmark datasets within multiple domains.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135898174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LinkedDataOps:quality oriented end-to-end geospatial linked data production governance","authors":"Beyza Yaman, Kevin Thompson, Fergus Fahey, Rob Brennan","doi":"10.3233/sw-233293","DOIUrl":"https://doi.org/10.3233/sw-233293","url":null,"abstract":"This work describes the application of semantic web standards to data quality governance of data production pipelines in the architectural, engineering, and construction (AEC) domain for Ordnance Survey Ireland (OSi). It illustrates a new approach to data quality governance based on establishing a unified knowledge graph for data quality measurements across a complex, heterogeneous, quality-centric data production pipeline. It provides the first comprehensive formal mappings between semantic models of data quality dimensions defined by the four International Organization for Standardization (ISO) and World Wide Web Consortium (W3C) data quality standards applied by different tools and stakeholders. It provides an approach to uplift rule-based data quality reports into quality metrics suitable for aggregation and end-to-end analysis. Current industrial practice tends towards stove-piped, vendor-specific and domain-dependent tools to process data quality observations however there is a lack of open techniques and methodologies for combining quality measurements derived from different data quality standards to provide end-to-end data quality reporting, root cause analysis or visualisation. This work demonstrated that it is effective to use a knowledge graph and semantic web standards to unify distributed data quality monitoring in an organisation and present the results in an end-to-end data dashboard in a data quality standards-agnostic fashion for the Ordnance Survey Ireland data publishing pipeline.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135395686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}