Relevant bibliographies by topics / Virtual knowledge graphs (VKGs)

Academic literature on the topic 'Virtual knowledge graphs (VKGs)'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Virtual knowledge graphs (VKGs)"

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Xiao, Guohui, Linfang Ding, Benjamin Cogrel, and Diego Calvanese. "Virtual Knowledge Graphs: An Overview of Systems and Use Cases." Data Intelligence 1, no. 3 (June 2019): 201–23. http://dx.doi.org/10.1162/dint_a_00011.

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In this paper, we present the virtual knowledge graph (VKG) paradigm for data integration and access, also known in the literature as Ontology-based Data Access. Instead of structuring the integration layer as a collection of relational tables, the VKG paradigm replaces the rigid structure of tables with the flexibility of graphs that are kept virtual and embed domain knowledge. We explain the main notions of this paradigm, its tooling ecosystem and significant use cases in a wide range of applications. Finally, we discuss future research directions.
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Baclawski, Ken, Michael Bennett, Gary Berg-Cross, Todd Schneider, Ravi Sharma, Janet Singer, and Ram D. Sriram. "Ontology summit 2020 communiqué: Knowledge graphs." Applied Ontology 16, no. 2 (April 27, 2021): 229–47. http://dx.doi.org/10.3233/ao-210249.

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An increasing amount of data is now available from public and private sources. Furthermore, the types, formats, and number of sources of data are also increasing. Techniques for extracting, storing, processing, and analyzing such data have been developed in the last few years for managing this bewildering variety based on a structure called a knowledge graph. Industry has devoted a great deal of effort to the development of knowledge graphs, and knowledge graphs are now critical to the functions of intelligent virtual assistants such as Siri, Alexa, and Google Assistant. The goal of the Ontology Summit 2020 was to understand not only what knowledge graphs are but also where they originated, why they are so popular, the current issues, and their future prospects. The summit sessions examined many examples of knowledge graphs and surveyed the relevant standards that exist and are in development for knowledge graphs. The purpose of this Communiqué is to summarize our understanding from the Summit in order to foster research and development of knowledge graphs.
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Ding, Linfang, Guohui Xiao, Albulen Pano, Claus Stadler, and Diego Calvanese. "Towards the next generation of the LinkedGeoData project using virtual knowledge graphs." Journal of Web Semantics 71 (November 2021): 100662. http://dx.doi.org/10.1016/j.websem.2021.100662.

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Kurniawan, Kabul, Andreas Ekelhart, Elmar Kiesling, Dietmar Winkler, Gerald Quirchmayr, and A. Min Tjoa. "VloGraph: A Virtual Knowledge Graph Framework for Distributed Security Log Analysis." Machine Learning and Knowledge Extraction 4, no. 2 (April 11, 2022): 371–96. http://dx.doi.org/10.3390/make4020016.

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The integration of heterogeneous and weakly linked log data poses a major challenge in many log-analytic applications. Knowledge graphs (KGs) can facilitate such integration by providing a versatile representation that can interlink objects of interest and enrich log events with background knowledge. Furthermore, graph-pattern based query languages, such as SPARQL, can support rich log analyses by leveraging semantic relationships between objects in heterogeneous log streams. Constructing, materializing, and maintaining centralized log knowledge graphs, however, poses significant challenges. To tackle this issue, we propose VloGraph—a distributed and virtualized alternative to centralized log knowledge graph construction. The proposed approach does not involve any a priori parsing, aggregation, and processing of log data, but dynamically constructs a virtual log KG from heterogeneous raw log sources across multiple hosts. To explore the feasibility of this approach, we developed a prototype and demonstrate its applicability to three scenarios. Furthermore, we evaluate the approach in various experimental settings with multiple heterogeneous log sources and machines; the encouraging results from this evaluation suggest that the approach can enable efficient graph-based ad-hoc log analyses in federated settings.
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Zhang, Yunhao, Jun Zhu, Qing Zhu, Yakun Xie, Weilian Li, Lin Fu, Junxiao Zhang, and Jianmei Tan. "The construction of personalized virtual landslide disaster environments based on knowledge graphs and deep neural networks." International Journal of Digital Earth 13, no. 12 (June 4, 2020): 1637–55. http://dx.doi.org/10.1080/17538947.2020.1773950.

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Debruyne, Christophe, Gary Munnelly, Lynn Kilgallon, Declan O’Sullivan, and Peter Crooks. "Creating a Knowledge Graph for Ireland’s Lost History: Knowledge Engineering and Curation in the Beyond 2022 Project." Journal on Computing and Cultural Heritage 15, no. 2 (June 30, 2022): 1–25. http://dx.doi.org/10.1145/3474829.

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The Beyond 2022 project aims to create a virtual archive by digitally reconstructing and digitizing historical records lost in a catastrophic fire which consumed items in the Public Record Office of Ireland in 1922. The project is developing a knowledge graph (KG) to facilitate information retrieval and discovery over the reconstructed items. The project decided to adopt Semantic Web technologies to support its distributed KG and reasoning. In this article, we present our approach to KG generation and management. We elaborate on how we help historians contribute to the KG (via a suite of spreadsheets) and its ontology. We furthermore demonstrate how we use named graphs to store different versions of factoids and their provenance information and how these are serviced in two different endpoints. Modeling data in this manner allows us to acknowledge that history is, to some extent, subjective and different perspectives can exist in parallel. The construction of the KG is driven by competency questions elicited from subject matter experts within the consortium. We avail of CIDOC-CRM as our KG’s foundation, though we needed to extend this ontology with various qualifiers (types) and relations to support the competency questions. We illustrate how one can explore the KG to gain insights and answer questions. We conclude that CIDOC-CRM provides an adequate, albeit complex, foundation for the KG and that named graphs and Linked Data principles are a suitable mechanism to manage sets of factoids and their provenance.
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Beden, Sadeer, Qiushi Cao, and Arnold Beckmann. "SCRO: A Domain Ontology for Describing Steel Cold Rolling Processes towards Industry 4.0." Information 12, no. 8 (July 29, 2021): 304. http://dx.doi.org/10.3390/info12080304.

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This paper introduces the Steel Cold Rolling Ontology (SCRO) to model and capture domain knowledge of cold rolling processes and activities within a steel plant. A case study is set up that uses real-world cold rolling data sets to validate the performance and functionality of SCRO. This includes using the Ontop framework to deploy virtual knowledge graphs for data access, data integration, data querying, and condition-based maintenance purposes. SCRO is evaluated using OOPS!, the ontology pitfall detection system, and feedback from domain experts from Tata Steel.
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Palha, Sônia. "Students learning with interactive virtual math: an exploratory study in the classroom." Ensino e Tecnologia em Revista 1, no. 1 (September 6, 2017): 80. http://dx.doi.org/10.3895/etr.v1n1.5990.

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Interactive Virtual Math (IVM) is a visualization tool to support secondary school students’ learning of graphs by dynamic events. In the prototype version students construct a graph and try to improve it themselves and with the feedback of the tool. In a small-scale experiment, which involved four classes at secondary and tertiary education and their mathematics teachers we investigated how the students used the tool in the classroom. In this study we focus on the students learning experience and the results are expected to provide knowledge and directions for further development of the tool. The corpus data consists of self-reported questionnaires and lessons observations. One main finding is that students, at different school levels, find the tool useful to construct or improve graphical representations and it can help to get a better understanding of the subject. The tool features that helped students most were the self-construction of the graphs and to get feedback about their own graph at the end. Other findings are that the students can work independently with the tool and we know more about the tool features that are attractive or need to be improved.
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Varanka, Dalia E. "A Prototype Geospatial Knowledge Graph for National Topographic Mapping." Abstracts of the ICA 2 (October 9, 2020): 1. http://dx.doi.org/10.5194/ica-abs-2-40-2020.

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Abstract. Knowledge graphs (KG) are a virtual layer connecting disparate databases into an interoperable framework. Though the application of KGs for enterprises are increasing, geospatial KG design is not common. This presentation describes U.S. Geological Survey (USGS) research to build KGs for integrating geospatial and non-spatial attribute semantics of topographic data. Those geographic information system databases are composed of various feature types and metadata attributes organized various themes and stored in different data formats, such as geodatabases, flat-file spreadsheets, and raster images. The system being created tests two research objectives: 1) the feasibility of semantic technology approaches for geospatial data within the context of national topographic data and 2) the contribution to building a body of knowledge about system architecture for geospatial ontologies and linked open data. This presentation discusses the context of topographic data semantics, the problem and aims of building the system, and the integrated KG framework. The basic workflow and operations of the system architecture consisting of open-source software are described. The architecture modifies existing software with unique solutions such as performing GeoSPAQL queries with Postgres, a relational table datastore, and a map interface with extensions to support linked data queries as browseable graphs. As public spatial data infrastructure, the system is made available as a Docker Container on GitHub.
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Ronzhin, Folmer, Maria, Brattinga, Beek, Lemmens, and van’t Veer. "Kadaster Knowledge Graph: Beyond the Fifth Star of Open Data." Information 10, no. 10 (October 9, 2019): 310. http://dx.doi.org/10.3390/info10100310.

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After more than a decade, the supply-driven approach to publishing public (open) data has resulted in an ever-growing number of data silos. Hundreds of thousands of datasets have been catalogued and can be accessed at data portals at different administrative levels. However, usually, users do not think in terms of datasets when they search for information. Instead, they are interested in information that is most likely scattered across several datasets. In the world of proprietary in-company data, organizations invest heavily in connecting data in knowledge graphs and/or store data in data lakes with the intention of having an integrated view of the data for analysis. With the rise of machine learning, it is a common belief that governments can improve their services, for example, by allowing citizens to get answers related to government information from virtual assistants like Alexa or Siri. To provide high-quality answers, these systems need to be fed with knowledge graphs. In this paper, we share our experience of constructing and using the first open government knowledge graph in the Netherlands. Based on the developed demonstrators, we elaborate on the value of having such a graph and demonstrate its use in the context of improved data browsing, multicriteria analysis for urban planning, and the development of location-aware chat bots.
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Book chapters on the topic "Virtual knowledge graphs (VKGs)"

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Kalaycı, Elem Güzel, Irlan Grangel González, Felix Lösch, Guohui Xiao, Anees ul-Mehdi, Evgeny Kharlamov, and Diego Calvanese. "Semantic Integration of Bosch Manufacturing Data Using Virtual Knowledge Graphs." In Lecture Notes in Computer Science, 464–81. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62466-8_29.

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Vassiliades, Alexandros, Nick Bassiliades, Filippos Gouidis, and Theodore Patkos. "A Knowledge Retrieval Framework for Household Objects and Actions with External Knowledge." In Semantic Systems. In the Era of Knowledge Graphs, 36–52. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59833-4_3.

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Abstract In the field of domestic cognitive robotics, it is important to have a rich representation of knowledge about how household objects are related to each other and with respect to human actions. In this paper, we present a domain dependent knowledge retrieval framework for household environments which was constructed by extracting knowledge from the VirtualHome dataset (http://virtual-home.org). The framework provides knowledge about sequences of actions on how to perform human scaled tasks in a household environment, answers queries about household objects, and performs semantic matching between entities from the web knowledge graphs DBpedia, ConceptNet, and WordNet, with the ones existing in our knowledge graph. We offer a set of predefined SPARQL templates that directly address the ontology on which our knowledge retrieval framework is built, and querying capabilities through SPARQL. We evaluated our framework via two different user evaluations.
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Hussein, Hassan, Allard Oelen, Oliver Karras, and Sören Auer. "KGMM - A Maturity Model for Scholarly Knowledge Graphs Based on Intertwined Human-Machine Collaboration." In From Born-Physical to Born-Virtual: Augmenting Intelligence in Digital Libraries, 253–69. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21756-2_21.

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McTear, Michael, Kristiina Jokinen, Mohnish Dubey, Gérard Chollet, Jérôme Boudy, Christophe Lohr, Sonja Dana Roelen, Wanja Mössing, and Rainer Wieching. "Empowering Well-Being Through Conversational Coaching for Active and Healthy Ageing." In Lecture Notes in Computer Science, 257–65. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09593-1_21.

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AbstractWith life expectancy growing rapidly over the past century, societies are being increasingly faced with a need to find smart living solutions for elderly care and active ageing. The e-VITA project, which is a joint European (H2020) and Japanese (MIC) funded project, is based on an innovative approach to virtual coaching that addresses the crucial domains of active and healthy ageing. In this paper we describe the role of spoken dialogue technology in the project. Requirements for the virtual coach were elicited through a process of participatory design in workshops, focus groups, and living labs, and a number of use cases were identified for development using the open-source RASA framework. Knowledge Graphs are used as a shared representation within the system, enabling an integration of multimodal data, context, and domain knowledge.
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Diviacco, Paolo, and Alessandro Busato. "Maps, Graphs, and Annotations as Boundary Objects in Knowledge Networks, Distributed Cognition, and Collaborative E-Research." In Collaborative Knowledge in Scientific Research Networks, 387–408. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6567-5.ch019.

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Scientific communities tend to speciate in tribes that evolve their culture so that after some time they do not have the same understanding of the same terms or concepts. Tacit knowledge, that is the knowledge that cannot be expressed or formalized, complicates this even more. Since we cannot fully know what a concept refers to, we would have limitations in developing IT systems to support collaborative and distributed cognition activities and knowledge networks trying to map knowledge between paradigms. To address this, the authors propose to switch to another perspective where knowledge is kept implicit and the referential communication function (iconic signification) is exploited instead. In this perspective, they develop a specific virtual research environment named COLLA-ANT, which, albeit being still a prototype and needing more use cases, has proved successful in addressing the above-mentioned issues.
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Liu, Dou, Claudia Alessandra Libbi, and Delaram Javdani Rikhtehgar. "What Would You Like to Visit Next? – Using a Knowledge-Graph Driven Museum Guide in a Virtual Exhibition." In HHAI2022: Augmenting Human Intellect. IOS Press, 2022. http://dx.doi.org/10.3233/faia220215.

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Conversational agents have been recently incorporated into Virtual Heritage to provide more immersive and interactive user experience. However, existing chatbot guides lack the capacity to leverage the rich background knowledge graphs (KGs) to provide better interactions between visitors and cultural collections. In this paper, we present a KG driven conversational museum guide that answers visitor’s questions and recommend relevant art objects in a virtual exhibition, while modelling user interest to offer personalised information and guidance.
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Lima, Salvador, and José Moreira. "A Semantic Framework for Touristic Information Systems." In Cases on Open-Linked Data and Semantic Web Applications, 132–55. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2827-4.ch007.

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The Web is a crucial means for the dissemination of touristic information. However, most touristic information resources are stored directly in Web pages or in relational databases that are accessible through ad-hoc Web applications, and the use of automated processes to search, extract and interpret information can hardly be implemented. The Semantic Web technologies, aiming at representing the background knowledge about Web resources in a computational way, can be an important contribution to the development of such automated processes. This chapter introduces the concept of touristic object, giving special attention to the representation of temporal, spatial, and thematic knowledge. It also proposes a three-layered architecture for the representation of touristic objects in the Web. The central part is the domain layer, defining a Semantic Model for Tourism (SeMoT) to describe concepts, relationships, and constraints using ontologies. The data layer supports the mapping of touristic information in relational databases into Resource Description Framework (RDF) virtual graphs following the SeMoT specification. The application layer deals with the integration of information from different data sources into a unified knowledge model, offering a common vocabulary to describe touristic information resources. Finally, we also show how to use this framework for planning touristic itineraries.
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Gross, Alan G., and Joseph E. Harmon. "Overcoming the Obstacles to Internet Exploitation." In The Internet Revolution in the Sciences and Humanities. Oxford University Press, 2016. http://dx.doi.org/10.1093/oso/9780190465926.003.0012.

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The Internet presents an opportunity for the sciences and humanities to transform the generation, communication, and evaluation of new knowledge. Indeed, the elite scientific journals are already reinventing the traditional research article via the Internet. Its methods are being communicated by a combination of video demon­stration and verbal description, its gist, not only by verbal, but by visual abstracts, video abstracts, summaries for the general reader, and podcasts. Its contents take advantage of the computer screen; its results are communicated by multicomponent computer-generated images in color, videos of events in the laboratory or simulations of the natural world, graphs that automatically turn into tables and vice versa, maps displayed so that the viewer can zoom in and out, and 3D interactive images. Links are sending readers to a wealth of supplementary material: data, images, related readings. Community response to articles is being captured in new ways. Innovative processes for the evaluation of proposed new knowledge, before and after publica­tion, are being developed and adopted. Upon publication and even before, articles and the data in them are becoming part of virtual archives that give new meaning to “body of knowledge.” See Video 7.1 [ ]. Researchers are inviting commentary from the professional community as their data are generated; they are posting data and images online that others are free to use—with appropriate attribution, of course. Enthusiastic amateurs or the simply curious in large numbers are once again able to actively participate in scientific research projects. For the humanities, the Internet is no less promising. Film scholars are inter­posing film clips in their critique of classic films. Historians are including videos of historical events or computerized recreations, as well as reproductions of key documents of historical interest such as court testimony and reproductions of handwritten letters. Art and architectural historians are displaying interactive 3D reconstructions of sculptures and buildings and historical sites.
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Kohl, Linus, Fazel Ansari, and Wilfried Sihn. "A Modular Federated Learning Architecture for Integration of AI-enhanced Assistance in Industrial Maintenance - A novel architecture for enhancing industrial maintenance management systems in the automotive and semiconductor industry." In Competence development and learning assistance systems for the data-driven future, 229–42. Goto Verlag, 2021. http://dx.doi.org/10.30844/wgab_2021_14.

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Artificial Intelligence (AI) plays an increasingly important role for the implementation and failure-free operation of Cyber-Physical Production Systems (CPPS). Recent market studies show that investment in AI-enhanced maintenance is increasing as one of the most important use cases of Industry 4.0. AI systems enable the improvement of various Key Performance Indicators (KPI), ultimately leading to a reduction in costs and optimizing plant management in smart factories. At the same time, manufacturing enterprises in diverse sectors have very high expectations from any kind of AI solution comparing to conventional solutions. Today manufacturing enterprises use only a quarter of their data and therefore leave an enormous, untapped potential. The use of Text Mining (TM) realizes the untapped value of existing unstructured or semi-structured textual data. This paper presents a transferable and scalable architecture for a cognitive maintenance system of a human-centered assistance system that enables holistic sensing of the environment by using physical and virtual sensors. By focusing on generalizability, scalability, adaptability, reliability, and user acceptance, a novel architecture for cognitive maintenance system is proposed. The so called ARCHIE, Architecture for a Cognitive Maintenance System, addresses common challenges in the application of AI systems in the industrial environment. Human-centered cognitive systems aim to automate manufacturing processes and assist workers in their cognitive tasks. This can be achieved by using the untapped potential of combining unstructured and structured data in order to extract hidden knowledge. ARCHIE aims at realizing an AI-enhanced approach for a human-centered assistance system. ARCHIE incorporates physical and virtual sensors that capture machine states, parameters, human knowledge, and skills to optimize relevant KPIs. This includes a reduction in documentation time, Mean Time Between Failures (MTBF) and Mean Failure Detection Time (MFDT), as well as an increase in uptime, leading ultimately to an improved Overall Equipment Efficiency (OEE). These improvements are enabled by the combined use of AI in the form of TM, Federated Learning and Knowledge Graphs. In the presented use-case from the automotive industry, a reduction in MFDT below 60min by 97.3% and an increase in OEE by 5.3% was achieved. In the Semiconductor industry, the partial application of ARCHIE allows the querying of competence distributions based on a given maintenance task, enabling automated allocation of maintenance technicians and trend analyses. Generalizability, scalability, adaptability, reliability, and user acceptance were also evaluated in the use cases presented.
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Conference papers on the topic "Virtual knowledge graphs (VKGs)"

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Kurniawan, Kabul, Andreas Ekelhart, Elmar Kiesling, Dietmar Winkler, Gerald Quirchmayr, and A. Min Tjoa. "Virtual Knowledge Graphs for Federated Log Analysis." In ARES 2021: The 16th International Conference on Availability, Reliability and Security. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3465481.3465767.

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Weigand, Maximilian, and Alexander Fay. "Creating Virtual Knowledge Graphs from Software-Internal Data." In IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2022. http://dx.doi.org/10.1109/iecon49645.2022.9969051.

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Wu, Jiantao, Fabrizio Orlandi, Declan O'Sullivan, and Soumyabrata Dev. "Publishing Climate Data as Linked Data Via Virtual Knowledge Graphs." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9884226.

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Axelsson, Nils, and Gabriel Skantze. "Using knowledge graphs and behaviour trees for feedback-aware presentation agents." In IVA '20: ACM International Conference on Intelligent Virtual Agents. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3383652.3423884.

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Calvanese, Diego, Linfang Ding, Alessandro Mosca, and Guohui Xiao. "Realizing Ontology-based Reusable Interfaces for Data Access via Virtual Knowledge Graphs." In CHItaly '21: 14th Biannual Conference of the Italian SIGCHI Chapter. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3464385.3464744.

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Arslan, Muhammad, and Christophe Cruz. "Modeling virtual knowledge graphs using relevant news data by NLP methods for business analysis." In 2022 17th International Conference on Emerging Technologies (ICET). IEEE, 2022. http://dx.doi.org/10.1109/icet56601.2022.10004674.

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Egami, Shusaku, Satoshi Nishimura, and Ken Fukuda. "A Framework for Constructing and Augmenting Knowledge Graphs using Virtual Space: Towards Analysis of Daily Activities." In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2021. http://dx.doi.org/10.1109/ictai52525.2021.00194.

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Goridkov, Nicole, Vivek Rao, Dixun Cui, Daniele Grandi, Ye Wang, and Kosa Goucher-Lambert. "Capturing Designers’ Experiential Knowledge in Scalable Representation Systems: A Case Study of Knowledge Graphs for Product Teardowns." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-90697.

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Abstract Knowledge collection, extraction, and organization are critical activities in all aspects of the engineering design process. However, it remains challenging to surface and organize design knowledge in a scalable and accessible manner given it often contains implicit or tacit dimensions that are difficult to capture. Knowledge graphs have been explored to address this issue but have been primarily semantic in nature in engineering design contexts, typically focusing on sharing explicit knowledge. In this work, we explore how knowledge graphs could offer a mechanism to organize experiential design knowledge and afford its use in complex queries. We develop a searchable knowledge graph based on data from a previous virtual product teardown activity with 23 professional designers. Examples of the underlying data within this corpus include descriptions of product components and their purpose as well as participant-determined relationships between these components. To structure the knowledge graph, we develop a schema that uses its constituent nodes and edges to represent design knowledge, relational information, and properties such as the node author’s discipline and the node’s function-behavior-structure classification. We propose and demonstrate two user-driven graph search types — intentional and exploratory — and four data-driven graph search methods, and illustrate through two extended examples their potential to reveal insights and patterns from teardown knowledge. These findings suggest that knowledge graphs can be a valuable approach to organizing and availing experiential design knowledge emerging from complex design activities.
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Pineda Becerril, Miguel De Nazareth, Omar García, Armando Aguilar, and Frida León. "Use of a Website and Virtual Laboratory for Teaching of Descriptive Statistics." In INNODOCT 2021. Valencia: Editorial Universitat Politècnica de València, 2021. http://dx.doi.org/10.4995/inn2021.2021.13490.

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Most of the statistical information in newspapers, magazines, business reports and other publications consists of data that are summarized and presented in a way that it is easy to read and understand. These summaries of data, which can be tabular, graphical or numerical are known as descriptive statistics. In addition, the presentations in tables and graphs to summarize data, numerical descriptive statistics are also used. Within this context, is developing a web page with a virtual laboratory of the themes of descriptive statistics, which proposes a study guide which aims to reorient and upgrade the approach that must address the study of statistical methods, awakening the topics that were developed so that cases raised to develop learning environments that would enable it to meet the knowledge and manipulate it. With this philosophy, applets, web sites with access to real data, software for free use and in general resources used in the web 2.0, referring to a second generation in the history of the web based on user communities, that foster collaboration and fast exchange of information between them. The technology allows us to enjoy the following principle of the use of the modern statistics. It is not as important to memorize formulas or perform complex arithmetic calculations by hand. One can instead focus on results with any type of technology, to give practical meaning to results through critical thinking. This has to make the students really have to make an effort to understand and interpret the results
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Penteado, Bruno Elias, Seiji Isotani, Ig Ibert Bittencourt, Rafael Ferreira Mello, and Ibsen Mateus Bittencourt. "An architecture for monitoring public educational policies based on big open linked data." In Simpósio Brasileiro de Informática na Educação. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sbie.2022.225738.

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Open government data (OGD) is increasingly being deployed in many countries. It is often considered a pillar of democracy by providing society with transparency and accountability for public policies and actions. However, the role of OGD in policy-making monitoring has been explored only marginally due to its complexity and inter-organizational boundaries. Big Open Linked Data (BOLD) is a recent technological approach where data from multiple institutions with different data governance policies must be integrated to achieve a common goal. In this approach, linked data is essential in providing tools and practices to merge and analyze these data. In this work, we present a BOLD infrastructure to support the monitoring of an educational public policy using a linked data approach. The design science research methodology (DSR-M) was followed in this study. We chose the PNE (National Education Plan) as an illustrative scenario since it sets out goals for national education at different levels and perspectives for ten years. We used its goals and indicators to elicit requirements and evaluate the architecture. As a result, we created an infrastructure based on virtual knowledge graphs at a municipality level and used it to generate detailed and transparent statistics on calculating the goals. Finally, we highlight some implications and future challenges of our approach.
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