Relevant bibliographies by topics / Knowledge graph creation and curation

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Knowledge graph creation and curation'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Knowledge graph creation and curation"

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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 (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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Weikum, Gerhard, Xin Luna Dong, Simon Razniewski, and Fabian Suchanek. "Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases." Foundations and Trends® in Databases 10, no. 2-4 (2021): 108–490. http://dx.doi.org/10.1561/1900000064.

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Stocker, Markus, Allard Oelen, Mohamad Yaser Jaradeh, et al. "FAIR scientific information with the Open Research Knowledge Graph." FAIR Connect 1, no. 1 (2023): 19–21. http://dx.doi.org/10.3233/fc-221513.

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The Open Research Knowledge Graph is an infrastructure for the production, curation, publication and use of FAIR scientific information. Its mission is to shape a future scholarly publishing and communication where the contents of scholarly articles are FAIR research data.
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Schwartz, David, and Dov Te'eni. "AI for Knowledge Creation, Curation, and Consumption in Context." Journal of the Association for Information Systems 25, no. 1 (2024): 37–47. http://dx.doi.org/10.17705/1jais.00862.

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Malik, Khalid Mahmood, Madan Krishnamurthy, Mazen Alobaidi, Maqbool Hussain, Fakhare Alam, and Ghaus Malik. "Automated domain-specific healthcare knowledge graph curation framework: Subarachnoid hemorrhage as phenotype." Expert Systems with Applications 145 (May 2020): 113120. http://dx.doi.org/10.1016/j.eswa.2019.113120.

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Khan, Arijit. "Knowledge Graphs Querying." ACM SIGMOD Record 52, no. 2 (2023): 18–29. http://dx.doi.org/10.1145/3615952.3615956.

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Knowledge graphs (KGs) such as DBpedia, Freebase, YAGO, Wikidata, and NELL were constructed to store large-scale, real-world facts as (subject, predicate, object) triples - that can also be modeled as a graph, where a node (a subject or an object) represents an entity with attributes, and a directed edge (a predicate) is a relationship between two entities. Querying KGs is critical in web search, question answering (QA), semantic search, personal assistants, fact checking, and recommendation. While significant progress has been made on KG construction and curation, thanks to deep learning recently we have seen a surge of research on KG querying and QA. The objectives of our survey are two-fold. First, research on KG querying has been conducted by several communities, such as databases, data mining, semantic web, machine learning, information retrieval, and natural language processing (NLP), with different focus and terminologies; and also in diverse topics ranging from graph databases, query languages, join algorithms, graph patterns matching, to more sophisticated KG embedding and natural language questions (NLQs). We aim at uniting different interdisciplinary topics and concepts that have been developed for KG querying. Second, many recent advances on KG and query embedding, multimodal KG, and KG-QA come from deep learning, IR, NLP, and computer vision domains. We identify important challenges of KG querying that received less attention by graph databases, and by the DB community in general, e.g., incomplete KG, semantic matching, multimodal data, and NLQs. We conclude by discussing interesting opportunities for the data management community, for instance, KG as a unified data model and vector-based query processing.
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Croset, Samuel, Joachim Rupp, and Martin Romacker. "Flexible data integration and curation using a graph-based approach." Bioinformatics 32, no. 6 (2015): 918–25. http://dx.doi.org/10.1093/bioinformatics/btv644.

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Abstract Motivation: The increasing diversity of data available to the biomedical scientist holds promise for better understanding of diseases and discovery of new treatments for patients. In order to provide a complete picture of a biomedical question, data from many different origins needs to be combined into a unified representation. During this data integration process, inevitable errors and ambiguities present in the initial sources compromise the quality of the resulting data warehouse, and greatly diminish the scientific value of the content. Expensive and time-consuming manual curation is then required to improve the quality of the information. However, it becomes increasingly difficult to dedicate and optimize the resources for data integration projects as available repositories are growing both in size and in number everyday. Results: We present a new generic methodology to identify problematic records, causing what we describe as ‘data hairball’ structures. The approach is graph-based and relies on two metrics traditionally used in social sciences: the graph density and the betweenness centrality. We evaluate and discuss these measures and show their relevance for flexible, optimized and automated data curation and linkage. The methodology focuses on information coherence and correctness to improve the scientific meaningfulness of data integration endeavors, such as knowledge bases and large data warehouses. Contact: samuel.croset@roche.com Supplementary information: Supplementary data are available at Bioinformatics online.
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Abueed, Raed A. I., and Mehmet Aga. "Sustainable Knowledge Creation and Corporate Outcomes: Does Corporate Data Governance Matter?" Sustainability 11, no. 20 (2019): 5575. http://dx.doi.org/10.3390/su11205575.

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It has been recognized that data curation and governance can equip firms with the capability to generate sustainable knowledge. However, the antecedent and consequences of sustainable knowledge creation have not been systematically explored. The model in this study describes how sustainable knowledge creation enhances corporate information transparency, innovation, and financial and market performance. In addition, we also show how corporate data governance fosters sustainable knowledge creation among corporations listed in the Amman Stock exchange. Using survey data from (n = 180) publicly listed corporations and a judgmental sampling technique, we applied partial least squares structural equation modeling (PLS–SEM). Results from PLS–SEM show that corporate data governance is a predictor for sustainable knowledge creation, and sustainable knowledge creation is also a predictor for corporate information transparency and innovative, financial, and market performance. The study offers guidelines for corporate managers to effectively manage and use corporate data responsibly to attain sustainable knowledge creation which in turn results in greater corporate performance and desired outcomes. Implications for practice and theory are discussed.
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Luttmer, Janosch, Mostafa Kandel, Dominik Ehring, and Arun Nagarajah. "Automatic knowledge graph creation from engineering standards using the example of formulas." Proceedings of the Design Society 4 (May 2024): 423–32. http://dx.doi.org/10.1017/pds.2024.45.

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AbstractEngineering standards are an important source of knowledge in product development. Despite the increasing digitalisation, the provision and usage of standards is characterised by lots of manual steps. This research paper aims at applying automatic knowledge graph creation in the domain of engineering standards to enable machine-actionable standards. For this, a formula knowledge graph ontology as well as suitable information extraction techniques are developed. The concept is validated using the example of DIN ISO 281, showing the overall capability of automatic knowledge graph creation.
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Qin, Pengda, Xin Wang, Wenhu Chen, Chunyun Zhang, Weiran Xu, and William Yang Wang. "Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8673–80. http://dx.doi.org/10.1609/aaai.v34i05.6392.

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Large-scale knowledge graphs (KGs) are shown to become more important in current information systems. To expand the coverage of KGs, previous studies on knowledge graph completion need to collect adequate training instances for newly-added relations. In this paper, we consider a novel formulation, zero-shot learning, to free this cumbersome curation. For newly-added relations, we attempt to learn their semantic features from their text descriptions and hence recognize the facts of unseen relations with no examples being seen. For this purpose, we leverage Generative Adversarial Networks (GANs) to establish the connection between text and knowledge graph domain: The generator learns to generate the reasonable relation embeddings merely with noisy text descriptions. Under this setting, zero-shot learning is naturally converted to a traditional supervised classification task. Empirically, our method is model-agnostic that could be potentially applied to any version of KG embeddings, and consistently yields performance improvements on NELL and Wiki dataset.
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Dissertations / Theses on the topic "Knowledge graph creation and curation"

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Ahmadi, Naser. "A framework for the continuous curation of a knowledge base system." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS320.

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Les graphes de connaissances centrés sur les entités sont de plus en plus populaires pour recueillir des informations sur les entités. Les schémas des KG sont complexes, avec de nombreux types et prédicats différents pour définir les entités et leurs relations. Ces KG contiennent des connaissances spécifiques à un domaine, mais pour tirer le maximum de ces données, il faut comprendre la structure et les schémas du KG. Leurs données comprennent des entités et leurs types sémantiques pour un domaine spécifique. En outre, les propriétés des entités et les relations entre les entités sont stockées. En raison de l'émergence de nouveaux faits et entités et de l'existence de déclarations invalides, la création et la maintenance des KG est un processus sans fin. Dans cette thèse, nous présentons d'abord une approche destinée à créer un KG dans le domaine de l'audit en faisant correspondre des documents de différents niveaux. Nous introduisons ensuite des méthodes pour la curation continue des KGs. Nous présentons un algorithme pour la fouille des règles conditionnelles et l'appliquons sur de grands KGs. Ensuite, nous décrivons RuleHub, un corpus extensible de règles pour les KGs publiques qui fournit des fonctionnalités pour l'archivage et la récupération des règles. Nous proposons également des méthodes pour l'exploitation des règles logiques dans deux applications différentes: l'apprentissage de règles souples à des modèles de langage pré-entraînés (RuleBert) et la vérification explicable des faits (ExpClaim)<br>Entity-centric knowledge graphs (KGs) are becoming increasingly popular for gathering information about entities. The schemas of KGs are semantically rich, with many different types and predicates to define the entities and their relationships. These KGs contain knowledge that requires understanding of the KG’s structure and patterns to be exploited. Their rich data structure can express entities with semantic types and relationships, oftentimes domain-specific, that must be made explicit and understood to get the most out of the data. Although different applications can benefit from such rich structure, this comes at a price. A significant challenge with KGs is the quality of their data. Without high-quality data, the applications cannot use the KG. However, as a result of the automatic creation and update of KGs, there are a lot of noisy and inconsistent data in them and, because of the large number of triples in a KG, manual validation is impossible. In this thesis, we present different tools that can be utilized in the process of continuous creation and curation of KGs. We first present an approach designed to create a KG in the accounting field by matching entities. We then introduce methods for the continuous curation of KGs. We present an algorithm for conditional rule mining and apply it on large graphs. Next, we describe RuleHub, an extensible corpus of rules for public KGs which provides functionalities for the archival and the retrieval of rules. We also report methods for using logical rules in two different applications: teaching soft rules to pre-trained language models (RuleBert) and explainable fact checking (ExpClaim)
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Oshurko, Ievgeniia. "Knowledge representation and curation in hierarchies of graphs." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEN024.

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L'extraction automatique des intuitions et la construction de modèles computationnels à partir de connaissances sur des systèmes complexes repose largement sur le choix d'une représentation appropriée. Ce travail s'efforce de construire un cadre adapté pour la représentation de connaissances fragmentées sur des systèmes complexes et sa curation semi-automatisé.Un système de représentation des connaissances basé sur des hiérarchies de graphes liés à l'aide d'homomorphismes est proposé. Les graphes individuels représentent des fragments de connaissances distincts et les homomorphismes permettent de relier ces fragments. Nous nous concentrons sur la conception de mécanismes mathématiques,basés sur des approches algébriques de la réécriture de graphes, pour la transformation de graphes individuels dans des hiérarchies qui maintient des relations cohérentes entre eux.De tels mécanismes fournissent une piste d'audit transparente, ainsi qu'une infrastructure pour maintenir plusieurs versions des connaissances.La théorie développée est appliquée à la conception des schémas pour les bases de données orientée graphe qui fournissent des capacités de co-évolution schémas-données.Ensuite, cette théorie est utilisée dans la construction du cadre KAMI, qui permet la curation des connaissances sur la signalisation dans les cellules. KAMI propose des mécanismes pour une agrégation semi-automatisée de faits individuels sur les interactions protéine-protéine en corpus de connaissances, la réutilisation de ces connaissances pour l'instanciation de modèles de signalisation dans différents contextes cellulaires et la génération de modèles exécutables basés sur des règles<br>The task of automatically extracting insights or building computational models fromknowledge on complex systems greatly relies on the choice of appropriate representation.This work makes an effort towards building a framework suitable for representation offragmented knowledge on complex systems and its semi-automated curation---continuouscollation, integration, annotation and revision.We propose a knowledge representation system based on hierarchies of graphs relatedwith graph homomorphisms. Individual graphs situated in such hierarchies representdistinct fragments of knowledge and the homomorphisms allow relating these fragments.Their graphical structure can be used efficiently to express entities and their relations. Wefocus on the design of mathematical mechanisms, based on algebraic approaches to graphrewriting, for transformation of individual graphs in hierarchies that maintain consistentrelations between them. Such mechanisms provide a transparent audit trail, as well as aninfrastructure for maintaining multiple versions of knowledge.We describe how the developed theory can be used for building schema-aware graphdatabases that provide schema-data co-evolution capabilities. The proposed knowledgerepresentation framework is used to build the KAMI (Knowledge Aggregation and ModelInstantiation) framework for curation of cellular signalling knowledge. The frameworkallows for semi-automated aggregation of individual facts on protein-protein interactionsinto knowledge corpora, reuse of this knowledge for instantiation of signalling models indifferent cellular contexts and generation of executable rule-based models
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Books on the topic "Knowledge graph creation and curation"

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de, Moor Aldo, Lex Wilfried, and Ganter Bernhard, eds. Conceptual structures for knowledge creation and communication: 11th International Conference on Conceptual Structures, ICCS 2003, Dresden, Germany, July 21-25, 2003 : proceedings. Springer, 2003.

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Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases. Now Publishers, 2021.

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(Editor), Aldo de Moor, Wilfried Lex (Editor), and Bernhard Ganter (Editor), eds. Conceptual Structures for Knowledge Creation and Communication: 11th International Conference on Conceptual Structures, ICCS 2003, Dresden, Germany, July ... (Lecture Notes in Computer Science). Springer, 2003.

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Book chapters on the topic "Knowledge graph creation and curation"

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Georgoudi, Amalia, Nikolaos Stylianou, Ioannis Konstantinidis, et al. "Towards Knowledge Graph Creation from Greek Governmental Documents." In Advances and Trends in Artificial Intelligence. Theory and Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36819-6_26.

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Vinasco-Alvarez, Diego. "Leveraging Standards in Model-Centric Geospatial Knowledge Graph Creation." In The Semantic Web: ESWC 2022 Satellite Events. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11609-4_37.

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Shinde, Shreya, Shubhangi Gaherwar, Avani Sathe, Malvika Menon, and Sheetal Barekar. "Creation of Knowledge Graph for Client Complaint Management System." In Data Management, Analytics and Innovation. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2934-1_2.

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Lodhi, Aminah Bilal, Muhammad Abdullah Bilal, Hafiz Syed Muhammad Bilal, et al. "PNRG: Knowledge Graph-Driven Methodology for Personalized Nutritional Recommendation Generation." In Digital Health Transformation, Smart Ageing, and Managing Disability. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43950-6_20.

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AbstractChronic Diseases are a prevalent problem that affects millions of people worldwide. It is a prevalent health condition that requires careful diet and medication management and preventing chronic diseases. Traditional approaches to nutritional recommendation generation often rely on generic guidelines and population-based data, which may not account for individual dietary needs and preferences variations. In this paper, we propose a knowledge graph driven methodology for generating highly personalized nutritional recommendations that leverage the power of knowledge graphs to integrate and analyze complex data about an individual's health, lifestyle, and dietary habits. Our methodology employs a multi-step process that includes data collection and curation, knowledge graph construction, and personalized recommendation generation. We illustrate the effectiveness of our approach through a case study in which we generate personalized nutritional recommendations for a sample individual based on their specific health and dietary goals.
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Jozashoori, Samaneh, David Chaves-Fraga, Enrique Iglesias, Maria-Esther Vidal, and Oscar Corcho. "FunMap: Efficient Execution of Functional Mappings for Knowledge Graph Creation." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62419-4_16.

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De Donato, Renato, Martina Garofalo, Delfina Malandrino, Maria Angela Pellegrino, Andrea Petta, and Vittorio Scarano. "QueDI: From Knowledge Graph Querying to Data Visualization." In Semantic Systems. In the Era of Knowledge Graphs. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59833-4_5.

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Abstract While Open Data (OD) publishers are spur in providing data as Linked Open Data (LOD) to boost innovation and knowledge creation, the complexity of RDF querying languages, such as SPARQL, threatens their exploitation. We aim to help lay users (by focusing on experts in table manipulation, such as OD experts) in querying and exploiting LOD by taking advantage of our target users’ expertise in table manipulation and chart creation. We propose QueDI (Query Data of Interest), a question-answering and visualization tool that implements a scaffold transitional approach to 1) query LOD without being aware of SPARQL and representing results by data tables; 2) once reached our target user comfort zone, users can manipulate and 3) visually represent data by exportable and dynamic visualizations. The main novelty of our approach is the split of the querying phase in SPARQL query building and data table manipulation. In this article, we present the QueDI operating mechanism, its interface supported by a guided use-case over DBpedia, and the evaluation of its accuracy and usability level.
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Jozashoori, Samaneh, and Maria-Esther Vidal. "MapSDI: A Scaled-Up Semantic Data Integration Framework for Knowledge Graph Creation." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33246-4_4.

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Jozashoori, Samaneh, Tatiana Novikova, and Maria-Esther Vidal. "Poster Paper Data Integration for Supporting Biomedical Knowledge Graph Creation at Large-Scale." In Lecture Notes in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-06016-9_9.

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Hasida, Kôiti. "Personal AI to Maximize the Value of Personal Data while Defending Human Rights and Democracy." In Knowledge and Digital Technology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-39101-9_13.

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AbstractThe combination of centralized AIs (CAIs; AIs based on centralized management of personal data (PD)) and the attention economy (AE) has given rise to digital Leninism and surveillance capitalism and spread misinformation and biases, threatening human rights and democracy and impairing value creation by PD. Worse still, it is impossible for humanity to jointly restrict CAIs and AE, because they create winners: surveillance capitalism and digital Leninism may eventually confer upon some companies and countries huge profits and power. Only decentralized management of PD (DMPD) can dissolve this problem. First, personal AIs (PAIs) based on DMPD can displace CAIs by letting service providers employ PAIs instead of CAIs in order to improve their own profits, as PAIs create much larger value than CAIs by fully utilizing a subject’s PD to more deeply and carefully intervene in their lives. Second, the optimal medium of the human-PAI interaction is a graph document (document with explicit semantic structures in the form of diagram or graph), which should facilitate verification and enhance diversity of information. Third, DMPD also enables decentralized governance of PAIs and other services to maximize their added value while well-balancing value distribution among individuals, businesses, and societies.
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Srivani, M., Abirami Murugappan, and T. Mala. "MathKnowTopic: Creation of a Unified Knowledge Graph-Based Topic Modeling from Mathematical Text Books." In Innovations in Computational Intelligence and Computer Vision. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2602-2_47.

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Conference papers on the topic "Knowledge graph creation and curation"

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Huaman, Elwin, and Dieter Fensel. "Knowledge Graph Curation: A Practical Framework." In IJCKG'21: The 10th International Joint Conference on Knowledge Graphs. ACM, 2021. http://dx.doi.org/10.1145/3502223.3502247.

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Lakshmi, Vaibhava R., Gerard Deepak, A. Santhanavijayan, and S. Radha. "Knowledge Graph Curation from Text Via Ontologies." In 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS). IEEE, 2022. http://dx.doi.org/10.1109/icaiss55157.2022.10010816.

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Berti-Equille, Laure. "ML-Based Knowledge Graph Curation: Current Solutions and Challenges." In WWW '19: The Web Conference. ACM, 2019. http://dx.doi.org/10.1145/3308560.3316522.

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Kalla, Aparna, R. Shailesh, S. Preetha, Snehal Chandra, and Sudeepa Roy. "Scientific Knowledge Graph Creation and Analysis." In 2023 IEEE 8th International Conference for Convergence in Technology (I2CT). IEEE, 2023. http://dx.doi.org/10.1109/i2ct57861.2023.10126238.

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Shinde, Omkar, and Anish Khobragade. "Knowledge Graph creation on Windows malwares and completion using knowledge graph embedding." In 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). IEEE, 2022. http://dx.doi.org/10.1109/icac3n56670.2022.10074567.

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Khalid, Mutahira, Hasan Ali Khattak, Arsalan Ahmad, and Syed Ahmad Chan Bukhari. "Explainable Prediction of Medical Codes through Automated Knowledge Graph Curation Framework." In 2022 19th International Bhurban Conference on Applied Sciences and Technology (IBCAST). IEEE, 2022. http://dx.doi.org/10.1109/ibcast54850.2022.9990551.

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Ajmani, Leah, Mo Houtti, Jasmine C. Foriest, Michael Ann Devito, Nicholas Vincent, and Isaac Johnson. "Epistemic Injustice in Online Communities: Unpacking the Values of Knowledge Creation and Curation within CSCW Applications." In CSCW '23: Computer Supported Cooperative Work and Social Computing. ACM, 2023. http://dx.doi.org/10.1145/3584931.3611280.

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M, Arun Kumar, Harsha Prada S, Caroline Dorathy Esther J, Sabareshwaran M. T, and Kavitha Devi M K. "Semantic Nexus: Knowledge Graph Creation from CrossNER Data using NLP." In 2023 International Conference on Advanced Computing & Communication Technologies (ICACCTech). IEEE, 2023. http://dx.doi.org/10.1109/icacctech61146.2023.00036.

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Pfitzner, Fabian, Alexander Braun, and André Borrmann. "Object Detection-Based Knowledge Graph Creation: Enabling Insight into Construction Processes." In ASCE International Conference on Computing in Civil Engineering 2023. American Society of Civil Engineers, 2024. http://dx.doi.org/10.1061/9780784485224.023.

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Xia, Liqiao, Pai Zheng, Yongshi Liang, Ge Zheng, and Zhengyang Ling. "Secure Co-Creation of Industrial Knowledge Graph: Graph Complement Method with Federated Learning and ChatGPT." In 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE). IEEE, 2023. http://dx.doi.org/10.1109/case56687.2023.10260382.

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Reports on the topic "Knowledge graph creation and curation"

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Taylor, Karen, Emily Moynihan, and Information Technology Laboratory (U S. ). Information Science and Knowledge Management Branch. The Forefront : A Review of ERDC Publications, Spring 2021. Engineer Research and Development Center (U.S.), 2020. http://dx.doi.org/10.21079/11681/40902.

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The Engineer Research and Development Center (ERDC) is the premier civil works engineering and environmental sciences research and development arm of the U.S. Army Corps of Engineers (USACE). As such, it partners with the Army, Department of Defense (DoD), federal agencies, and civilian organizations to help solve our Nation’s most challenging problems in civil and military engineering, geospatial sciences, water resources, and environmental sciences. A special government knowledge center, ERDC Information Technology Laboratory’s Information Science and Knowledge Management (ISKM) Branch is critical to ERDC’s mission, fulfilling research requirements by offering a variety of editing and library services to advance the creation, dissemination, and curation of ERDC and USACE research knowledge. Serving as the publishing authority for the ERDC, ISKM publishes all ERDC technical publications to the Digital Repository Knowledge Core, sends a copy to the Defense Technical Information Center (DTIC) and creates a press release about each publication on the ERDC website. The Forefront seeks to provide an additional mechanism for highlighting some of our technical publications to the ERDC, USACE, Army, and DoD communities. This publication also encourages those outside ERDC to contact us about using ERDC editing services. For more information regarding the reports highlighted in this publications or others that ERDC researchers’ have created, please contact the ISKM virtual reference desk at erdclibrary@ask-a-librarian.info or visit the ISKM’s online repository, Knowledge Core, at https://erdc-library.erdc.dren.mil/ .
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Shapovalov, Viktor B., Yevhenii B. Shapovalov, Zhanna I. Bilyk, Artem I. Atamas, Roman A. Tarasenko, and Vitaliy V. Tron. Centralized information web-oriented educational environment of Ukraine. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3251.

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The modern development of science and technology has provided high quantity of information. This information must be systemized and classified. For taxonomization of educational materials, it was proposed to use existing graph-generators and graph-visualizers of the TODOS IT platform. A separate aspect of the TODOS IT platform is the possibility of using a centralized web-oriented learning environment. Creation of the system and transdisciplinary knowledge is a problem of modern education, which can be solved by creating a centralized web-oriented educational environment. Using this approach is an important part of the learning process. Such a centralized web-oriented environment based on the ontological approach involves filling, adaptive educational services with information resources that reflect the conceptual system of a particular discipline. One of the systems providing not only collection of information but include its systemizing is centralized web-oriented educational environment based on Ontology4 system. Ontology 4 use elements of the TODOS. The paper presents specific developments of one centralized web-oriented educational environment can be used to teach different subjects such as biology, chemistry, Ukrainian language and literature, using the STEM approach.
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