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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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Somerville, Mary M., Anita Mirjamdotter, Edmond Harjizi, et al. "Curating knowledge, creating change:: University Knowledge Center, Kosovo national transition." IFLA Journal 46, no. 2 (2020): 151–62. http://dx.doi.org/10.1177/0340035219883897.
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A collaborative system design initiative at the University for Business and Technology in Kosovo aims to make local knowledge visible and to enhance local knowledge creation, within the university and throughout the country. Since its inception in 2015, design activities aimed to activate systems through modeling the global knowledge landscape, technology enabled systems, and human activity processes. Within the framework of Informed Systems, application of Informed Learning Theory and Information Experience Design (IXD) guided prototyping systems that informed building an institutional repository named the UBT Knowledge Center. The knowledge vision anticipates that sustained curation, organization, discovery, access, and usage processes will accelerate academic engagement, national development, and global visibility, over time and with practice to further theory-to-practice and practice-to-theory.
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Esteva, Maria, Weijia Xu, Nevan Simone, Kartik Nagpal, Amit Gupta, and Moriba Jah. "Synchronic Curation for Assessing Reuse and Integration Fitness of Multiple Data Collections." International Journal of Digital Curation 17, no. 1 (2022): 11. http://dx.doi.org/10.2218/ijdc.v17i1.847.
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Data driven applications often require using data integrated from different, large, and continuously updated collections. Each of these collections may present gaps, overlapping data, have conflicting information, or complement each other. Thus, a curation need is to continuously assess if data from multiple collections are fit for integration and reuse. To assess different large data collections at the same time, we present the Synchronic Curation (SC) framework. SC involves processing steps to map the different collections to a unifying data model that represents research problems in a scientific area. The data model, which includes the collections' provenance and a data dictionary, is implemented in a graph database where collections are continuously ingested and can be queried. SC has a collection analysis and comparison module to track updates, and to identify gaps, changes, and irregularities within and across collections. Assessment results can be accessed interactively through a web-based interactive graph. In this paper we introduce SC as an interdisciplinary enterprise, and illustrate its capabilities through its implementation in ASTRIAGraph, a space sustainability knowledge system.
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Huvila, Isto. "Improving the usefulness of research data with better paradata." Open Information Science 6, no. 1 (2022): 28–48. http://dx.doi.org/10.1515/opis-2022-0129.
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Abstract Considerable investments have been made in Europe and worldwide for developing research data infrastructures. Instead of a general lack of data about data, it has become apparent that a pivotal factor that drastically constrains data use is the absence of contextual knowledge about how data was created and how it has been curated and used. This applies especially to many branches of social science and humanities research, where data is highly heterogeneous, both by its kind (e.g. being qualitative, quantitative, naturalistic, purposefully created) and origins (e.g. being historical/contemporary, from different contexts and geographical places). The problem is that there may be enough metadata (data about data) but there is too little paradata (data on the processes of its creation, curation and use). The aim of this position paper is to draw attention 1) to the need for a better and more systematic understanding and documentation of the contexts of creation, curation and use of research data to make it useful and usable for researchers and other potential users in the future, and 2) to specific obstacles that make the capturing of this particular type of metadata, known as paradata, especially difficult. Failing to understand what information about the creation, curation and use of research data is needed and how to capture enough of that information risks that the currently collected vast amounts of research data become useless in the future.
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Johnson, Andrew. "The DataQ Story." Bulletin of the Association for Information Science and Technology 42, no. 5 (2016): 38–40. http://dx.doi.org/10.1002/bul2.2016.1720420509.
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EDITOR'S SUMMARYLongstanding frustration about the single‐purpose use of valuable data led author Andrew Johnson to a career in librarianship, a focus on scientific data curation and, ultimately, creation with colleague Megan Bresnahan of the DataQ project. Johnson's concerns about research data management being a silo within the profession were overcome with the realization that many librarians, especially those at smaller institutions, saw the need for effective curation but felt untrained and unequipped to deal with it. DataQ was established as a service and resource for library personnel to ask questions on data curation and learn through expanding group knowledge. Launched in August 2015, the project draws on a team of 15 editors and additional support members and has been widely and positively received. Funding by the Institute of Museum and Library Services, the Greater Western Library Alliance and others will help expand the effort to provide an important service to the library community.
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KERTKEIDKACHORN, Natthawut, and Ryutaro ICHISE. "An Automatic Knowledge Graph Creation Framework from Natural Language Text." IEICE Transactions on Information and Systems E101.D, no. 1 (2018): 90–98. http://dx.doi.org/10.1587/transinf.2017swp0006.
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Beagrie, Neil. "Digital Curation for Science, Digital Libraries, and Individuals." International Journal of Digital Curation 1 (December 2, 2008): 3–16. http://dx.doi.org/10.2218/ijdc.v1i1.2.
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The creation, management and use of digital materials are of increasing importance for a wide range of activities. Much of the knowledge base and intellectual assets of institutions and individuals are now in digital form. The term digital curation is increasingly being used for the actions needed to add value to and maintain these digital assets over time for current and future generations of users. The paper explores this emerging field of digital curation as an area of inter-disciplinary research and practice, and the trends which are influencing its development. It analyses the genesis of the term and how traditional roles relating to digital assets are in transition. Finally it explores some of the drivers for curation ranging from trends such as exponential growth in digital information, to "life-caching", digital preservation, the Grid and new opportunities for publishing, sharing, and re-using data. It concludes that significant effort needs to be put into developing a persistent information infrastructure for digital materials and into developing the digital curation skills of researchers and information professionals. Without this, current investment in digitisation and digital content will only secure short-term rather than lasting benefits.
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Ryen, Vetle, Ahmet Soylu, and Dumitru Roman. "Building Semantic Knowledge Graphs from (Semi-)Structured Data: A Review." Future Internet 14, no. 5 (2022): 129. http://dx.doi.org/10.3390/fi14050129.
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Knowledge graphs have, for the past decade, been a hot topic both in public and private domains, typically used for large-scale integration and analysis of data using graph-based data models. One of the central concepts in this area is the Semantic Web, with the vision of providing a well-defined meaning to information and services on the Web through a set of standards. Particularly, linked data and ontologies have been quite essential for data sharing, discovery, integration, and reuse. In this paper, we provide a systematic literature review on knowledge graph creation from structured and semi-structured data sources using Semantic Web technologies. The review takes into account four prominent publication venues, namely, Extended Semantic Web Conference, International Semantic Web Conference, Journal of Web Semantics, and Semantic Web Journal. The review highlights the tools, methods, types of data sources, ontologies, and publication methods, together with the challenges, limitations, and lessons learned in the knowledge graph creation processes.
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Bravo, Maricela, Darinel González-Villarreal, José A. Reyes-Ortiz, and Leonardo D. Sánchez-Martínez. "Modularization Method to Reuse Medical Knowledge Graphs." Applied Sciences 12, no. 22 (2022): 11816. http://dx.doi.org/10.3390/app122211816.
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During the creation and integration of a health care system based on medical knowledge graphs, it is necessary to review and select the vocabularies and definitions that best fit the information requirements of the system being developed. This implies the reuse of medical knowledge graphs; however, full importation of knowledge graphs is not a tractable solution in terms of memory requirements. In this paper, we present a modularization-based method for knowledge graph reuse. A case study of graph reuse is presented by transforming the original model into a lighter one.
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Cui, Licong, Ferdinand Dhombres, and Jean Charlet. "Knowledge Representation and Management: Notable Contributions in 2021." Yearbook of Medical Informatics 31, no. 01 (2022): 236–40. http://dx.doi.org/10.1055/s-0042-1742523.
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Objectives: To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2021. Methods: Following the International Medical Informatics Association (IMIA) Yearbook guidelines, a comprehensive and standardized review of the biomedical informatics literature was performed to select the best KRM papers published in 2021, based on PubMed queries. Results: A total of 1,231 publications were retrieved from PubMed. We nominated 15 candidate best papers, and four of them were finally selected as the best papers in the KRM section. The topics covered by these papers include knowledge graph, ontology development, ontology alignment, and the International Classification of Diseases. Conclusion: In the KRM best paper selection for 2021, the candidate best papers covered a wider spectrum of topics compared to the last year’s significant focus on ontology curation. In particular, ontology development for specific domains (e.g., Alzheimer’s disease, infectious diseases, bioethics) has received the most attention.
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Charlet, Jean, and Licong Cui. "Knowledge Representation and Management 2022: Findings in Ontology Development and Applications." Yearbook of Medical Informatics 32, no. 01 (2023): 225–29. http://dx.doi.org/10.1055/s-0043-1768747.
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Objectives: To select, present, and summarize the best papers in 2022 for the Knowledge Representation and Management (KRM) section of the International Medical Informatics Association (IMIA) Yearbook. Methods: We conducted PubMed queries and followed the IMIA Yearbook guidelines for performing biomedical informatics literature review to select the best papers in KRM published in 2022. Results: We retrieved 1,847 publications from PubMed. We nominated 15 candidate best papers, and two of them were finally selected as the best papers in the KRM section. The topics covered by the candidate papers include ontology and knowledge graph creation, ontology applications, ontology quality assurance, ontology mapping standard, and conceptual model. Conclusions: In the KRM best paper selection for 2022, the candidate best papers encompassed a broad range of topics, with ontology and knowledge graph creation remaining a considerable research focus.
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Zhang, Ying, Susan Xue, and Zhaohui Xue. "From collection curation to knowledge creation: Exploring new roles of academic librarians in digital humanities research." Journal of Academic Librarianship 47, no. 2 (2021): 102324. http://dx.doi.org/10.1016/j.acalib.2021.102324.
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Harland, Robert, Antonia Liguori, and Gareth Cole. "Developing a Digital Archive for Symbolic Resources in Urban Environments - the Latina Project." International Journal of Digital Curation 12, no. 2 (2018): 136–45. http://dx.doi.org/10.2218/ijdc.v12i2.511.
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The project described in this paper was funded to establish the foundation for a digital archival resource for researchers interested in the way people interact with urban environments through graphic communications. The research was internally funded by Loughborough University as part of its Research Challenge Programme and involved two members of academic staff and two library staff.[1] Two PhD students also participated.
 The archive consists of a small number of images and will act as a proof of concept, not only for this project but also for current and future funding applications. It is hoped that an extended archive will be useful not only to visual communication researchers, but also historians, architects, town planners and others. This paper will describe the data collection process, the challenges facing the project team in data curation and data documentation, and the creation of the pilot archive.
 The creation of the archive posed challenges for both the researchers and Library staff. For the researchers:
 
 
 Choosing a small number of images as a discrete collection but which also demonstrated the utility of the project to other disciplinary areas;
 
 
 Acquiring the necessary knowledge and skills to enable good curation and usability of the digital objects, e.g. file formats, metadata creation;
 
 
 Understanding what the technical solution enabled and where compromises would have to be made.
 
 
 For library staff:
 
 
 Demonstrating the utility of the Data Repository;
 
 
 Understanding the intellectual background to the project and the purpose of the Data Archive within the project;
 
 
 Clearly explaining the purpose of metadata and documentation.
 
 
 The Latina Project has demonstrated the value of a true partnership between the academic community and the professional services. All parties involved have learnt from the creation of the pilot archive and their practices have evolved. For example, it has made the researchers think more carefully about data curation questions and the professional services staff identify more closely with the research purposes for data creation. By working together so closely and sharing ideas from our different perspectives we have also identified potential technical developments which could be explored in future projects. All members of the group hope that the relationships built during this project will continue through other projects. [1] Academic staff: Drs Harland and Liguori. Library staff: Gareth Cole and Barbara Whetnall.
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Liu, Kai, Fei Wang, Zhaoyun Ding, Sheng Liang, Zhengfei Yu, and Yun Zhou. "Recent Progress of Using Knowledge Graph for Cybersecurity." Electronics 11, no. 15 (2022): 2287. http://dx.doi.org/10.3390/electronics11152287.
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In today’s dynamic complex cyber environments, Cyber Threat Intelligence (CTI) and the risk of cyberattacks are both increasing. This means that organizations need to have a strong understanding of both their internal CTI and their external CTI. The potential for cybersecurity knowledge graphs is evident in their ability to aggregate and represent knowledge about cyber threats, as well as their ability to manage and reason with that knowledge. While most existing research has focused on how to create a full knowledge graph, how to utilize the knowledge graph to tackle real-world industrial difficulties in cyberattack and defense situations is still unclear. In this article, we give a quick overview of the cybersecurity knowledge graph’s core concepts, schema, and building methodologies. We also give a relevant dataset review and open-source frameworks on the information extraction and knowledge creation job to aid future studies on cybersecurity knowledge graphs. We perform a comparative assessment of the many works that expound on the recent advances in the application scenarios of cybersecurity knowledge graph in the majority of this paper. In addition, a new comprehensive classification system is developed to define the linked works from 9 core categories and 18 subcategories. Finally, based on the analyses of existing research issues, we have a detailed overview of various possible research directions.
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Lister, Allyson L., Matthew Pocock, and Anil Wipat. "Integration of constraints documented in SBML, SBO, and the SBML Manual facilitates validation of biological models." Journal of Integrative Bioinformatics 4, no. 3 (2007): 252–63. http://dx.doi.org/10.1515/jib-2007-80.
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Abstract The creation of quantitative, simulatable, Systems Biology Markup Language (SBML) models that accurately simulate the system under study is a time-intensive manual process that requires careful checking. Currently, the rules and constraints of model creation, curation, and annotation are distributed over at least three separate documents: the SBML schema document (XSD), the Systems Biology Ontology (SBO), and the “Structures and Facilities for Model Definition” document. The latter document contains the richest set of constraints on models, and yet it is not amenable to computational processing. We have developed a Web Ontology Language (OWL) knowledge base that integrates these three structure documents, and that contains a representative sample of the information contained within them. This Model Format OWL (MFO) performs both structural and constraint integration and can be reasoned over and validated. SBML Models are represented as individuals of OWL classes, resulting in a single computationally amenable resource for model checking. Knowledge that was only accessible to humans is now explicitly and directly available for computational approaches. The integration of all structural knowledge for SBML models into a single resource creates a new style of model development and checking.
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Knez, Timotej, and Slavko Žitnik. "Event-Centric Temporal Knowledge Graph Construction: A Survey." Mathematics 11, no. 23 (2023): 4852. http://dx.doi.org/10.3390/math11234852.
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Textual documents serve as representations of discussions on a variety of subjects. These discussions can vary in length and may encompass a range of events or factual information. Present trends in constructing knowledge bases primarily emphasize fact-based common sense reasoning, often overlooking the temporal dimension of events. Given the widespread presence of time-related information, addressing this temporal aspect could potentially enhance the quality of common-sense reasoning within existing knowledge graphs. In this comprehensive survey, we aim to identify and evaluate the key tasks involved in constructing temporal knowledge graphs centered around events. These tasks can be categorized into three main components: (a) event extraction, (b) the extraction of temporal relationships and attributes, and (c) the creation of event-based knowledge graphs and timelines. Our systematic review focuses on the examination of available datasets and language technologies for addressing these tasks. An in-depth comparison of various approaches reveals that the most promising results are achieved by employing state-of-the-art models leveraging large pre-trained language models. Despite the existence of multiple datasets, a noticeable gap exists in the availability of annotated data that could facilitate the development of comprehensive end-to-end models. Drawing insights from our findings, we engage in a discussion and propose four future directions for research in this domain. These directions encompass (a) the integration of pre-existing knowledge, (b) the development of end-to-end systems for constructing event-centric knowledge graphs, (c) the enhancement of knowledge graphs with event-centric information, and (d) the prediction of absolute temporal attributes.
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Stephens, Miari Taina. "Black Feminist Organizing and Caribbean Cyberfeminisms in Puerto Rico." Open Cultural Studies 6, no. 1 (2022): 147–66. http://dx.doi.org/10.1515/culture-2022-0149.
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Abstract This article centers Black feminist organizing in Puerto Rico, highlighting social media as a tool for racial and gender justice. Collaboration between Puerto Rican feminist organizations on social media platforms amplifies their on-the-ground work and demands. Mapping Caribbean Cyberfeminisms (2016) theorizes Caribbean cyberfeminisms as “knowledge-producing spaces of political thought and action” online by Caribbean feminists. I argue that through content creation and curation, reposting and sharing, commenting and captioning, broadcasting live, Black feminist collectives, organizations and projects in Puerto Rico use digital and virtual technologies to extend their Black feminist organizing and collaboration, building a Caribbean cyberfeminist network in the process.
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Pfitzner, Fabian, Alexander Braun, and André Borrmann. "From data to knowledge: Construction process analysis through continuous image capturing, object detection, and knowledge graph creation." Automation in Construction 164 (August 2024): 105451. http://dx.doi.org/10.1016/j.autcon.2024.105451.
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Comunità, Marco, Andrea Gerino, Veranika Lim, and Lorenzo Picinali. "Design and Evaluation of a Web- and Mobile-Based Binaural Audio Platform for Cultural Heritage." Applied Sciences 11, no. 4 (2021): 1540. http://dx.doi.org/10.3390/app11041540.
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PlugSonic is a suite of web- and mobile-based applications for the curation and experience of 3D interactive soundscapes and sonic narratives in the cultural heritage context. It was developed as part of the PLUGGY EU project (Pluggable Social Platform for Heritage Awareness and Participation) and consists of two main applications: PlugSonic Sample, to edit and apply audio effects, and PlugSonic Soundscape, to create and experience 3D soundscapes for headphones playback. The audio processing within PlugSonic is based on the Web Audio API and the 3D Tune-In Toolkit, while the mobile exploration of soundscapes in a physical space is obtained using Apple’s ARKit. The main goal of PlugSonic is technology democratisation; PlugSonic users—whether cultural institutions or citizens—are all given the instruments needed to create, process and experience 3D soundscapes and sonic narratives; without the need for specific devices, external tools (software and/or hardware), specialised knowledge or custom development. The aims of this paper are to present the design and development choices, the user involvement processes as well as a final evaluation conducted with inexperienced users on three tasks (creation, curation and experience), demonstrating how PlugSonic is indeed a simple, effective, yet powerful tool.
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Faisal, Douglas Raevan, Fariz Darari, Muhammad Ilham Al Ghifari, et al. "A Hybrid Virtual Assistant for Legal Domain Based on Information Retrieval and Knowledge Graphs." Jurnal Ilmu Komputer dan Informasi 16, no. 2 (2023): 125–40. http://dx.doi.org/10.21609/jiki.v16i2.1152.
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Virtual assistants have gained popularity across various domains, including the legal field, where they serve to offer guidance and aid in the form of law retrieval. In this research, our aim is to develop a legal virtual assistant that combines knowledge graphs (KGs) and information retrieval (IR) techniques. This hybrid approach allows us to provide accurate answers extracted from structured interconnected data while simultaneously cater to a diverse range of legal inquiries. We categorize these inquiries into a few distinct use cases: definition lookup, law component lookup, sanctions, and domain knowledge. Our system encompasses a chatbot platform, knowledge graph querying, and information retrieval. Specifically, we construct a VA system over a legal knowledge graph pertaining to the Indonesian Act concerning Manpower or Labor (UU Ketenagakerjaan) and the Indonesian Act concerning the Creation of Jobs (UU Cipta Kerja). This marks the creation of the first legal virtual assistant in the Indonesian context that combines KG and IR methodologies. To evaluate the effectiveness of our prototype system, we conduct tests using a variety of labor law-related questions, ranging in difficulty. The integration of knowledge graphs and information retrieval proves to significantly improve the support provided for a wide range of potential applications in the legal field.
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Zhao, Mingxiong, Han Wang, Jin Guo, et al. "Construction of an Industrial Knowledge Graph for Unstructured Chinese Text Learning." Applied Sciences 9, no. 13 (2019): 2720. http://dx.doi.org/10.3390/app9132720.
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The industrial 4.0 era is the fourth industrial revolution and is characterized by network penetration; therefore, traditional manufacturing and value creation will undergo revolutionary changes. Artificial intelligence will drive the next industrial technology revolution, and knowledge graphs comprise the main foundation of this revolution. The intellectualization of industrial information is an important part of industry 4.0, and we can efficiently integrate multisource heterogeneous industrial data and realize the intellectualization of information through the powerful semantic association of knowledge graphs. Knowledge graphs have been increasingly applied in the fields of deep learning, social network, intelligent control and other artificial intelligence areas. The objective of this present study is to combine traditional NLP (natural language processing) and deep learning methods to automatically extract triples from large unstructured Chinese text and construct an industrial knowledge graph in the automobile field.
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Lavrischeva, E. M. "The Theory Graph Modeling and Programming Systems from Module Elements to the Application Areas." Computer and Information Science 12, no. 4 (2019): 20. http://dx.doi.org/10.5539/cis.v12n4p20.
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The mathematical basics of graph modeling and paradigm programming of applied systems (AS) are presented. The vertices of graph are been the functional elements of the systems and the arcs define the connections between them. The graph is represented by an adjacency and reach ability matrix. A number of graph of program structures and their representation by mathematical operations (unions, connections, differences, etc.) are shown. Given the characteristics of graph structures, complexes, units, and systems created from the modules of the graph. The method of modeling the system on the graph of modules, which describe in the programming languages (LP) and calling them with operations (link, assembling, building, etc.). The standard of configuration (2012) Assembly of heterogeneous software elements in AS of different fields of knowledge is made. Brief descriptions of modern and future programming paradigms for formal theoretical creation of systems from service-components for Internet in the near future are given.
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Turki, Houcemeddine, Mohamed Ali Hadj Taieb, Thomas Shafee, et al. "Representing COVID-19 information in collaborative knowledge graphs: The case of Wikidata." Semantic Web 13, no. 2 (2022): 233–64. http://dx.doi.org/10.3233/sw-210444.
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Information related to the COVID-19 pandemic ranges from biological to bibliographic, from geographical to genetic and beyond. The structure of the raw data is highly complex, so converting it to meaningful insight requires data curation, integration, extraction and visualization, the global crowdsourcing of which provides both additional challenges and opportunities. Wikidata is an interdisciplinary, multilingual, open collaborative knowledge base of more than 90 million entities connected by well over a billion relationships. It acts as a web-scale platform for broader computer-supported cooperative work and linked open data, since it can be written to and queried in multiple ways in near real time by specialists, automated tools and the public. The main query language, SPARQL, is a semantic language used to retrieve and process information from databases saved in Resource Description Framework (RDF) format. Here, we introduce four aspects of Wikidata that enable it to serve as a knowledge base for general information on the COVID-19 pandemic: its flexible data model, its multilingual features, its alignment to multiple external databases, and its multidisciplinary organization. The rich knowledge graph created for COVID-19 in Wikidata can be visualized, explored, and analyzed for purposes like decision support as well as educational and scholarly research.
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Welty, Chris, Lora Aroyo, Flip Korn, Sara M. McCarthy, and Shubin Zhao. "Rapid Instance-Level Knowledge Acquisition for Google Maps from Class-Level Common Sense." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 9 (October 4, 2021): 143–54. http://dx.doi.org/10.1609/hcomp.v9i1.18947.
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Successful knowledge graphs (KGs) solved the historical knowledge acquisition bottleneck by supplanting an expert focus with a simple, crowd-friendly one: KG nodes represent popular people, places, organizations, etc., and the graph arcs represent common sense relations like affiliations, locations, etc. Techniques for more general, categorical, KG curation do not seem to have made the same transition: the KG research community is still largely focused on methods that belie the common-sense characteristics of successful KGs. In this paper, we propose a simple approach to acquiring and reasoning with class-level attributes from the crowd that represent broad common sense associations between categories. We pick a very real industrial-scale data set and problem: how to augment an existing knowledge graph of places and products with associations between them indicating the availability of the products at those places, which would enable a KG to provide answers to questions like, "Where can I buy milk nearby?" This problem has several practical challenges, not least of which is that only 30% of physical stores (i.e. brick & mortar stores) have a website, and fewer list their product inventory, leaving a large acquisition gap to be filled by methods other than information extraction (IE). Based on a KG-inspired intuition that a lot of the class-level pairs are part of people's general common sense, e.g. everyone knows grocery stores sell milk and don't sell asphalt, we acquired a mixture of instance- and class- level pairs (e.g. , , resp.) from a novel 3-tier crowdsourcing method, and demonstrate the scalability advantages of the class-level approach. Our results show that crowdsourced class-level knowledge can provide rapid scaling of knowledge acquisition in this and similar domains, as well as long-term value in the KG.
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Dorch, Bertil Fabricius, Jakob Povl Holck, Kaare Lund Rasmussen, and Majken Brahe Ellegaard Christensen. "Making Tycho Brahe’s Sky Accessible to Future Astronomers." Proceedings of the International Astronomical Union 13, S349 (2018): 510–15. http://dx.doi.org/10.1017/s1743921319000693.
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AbstractCan we make a copy of Tycho’s “De Nova Stella” that can in fact survive a nova? At first, this may seem at best a nerdish, if not distinctly foolish question. However, it is also both a technological and a philosophical question: in fact, answering questions like this is linked to both technical, physical and sociological problems related to the long-term preservation and curation of objects from current and past civilizations.The undertaking presented here is two-fold: Firstly, we report on the results from a state-of the art short-term project, in which we have digitized and analysed three well-known rare books Pertain ing to astronomical observations by Tycho Brahe on the island of Hven. The project led to the creation of free e-books, enabling open access to the sky as recorded by Tycho.Secondly, we discuss some long-term issues related to the digital and physical preservation of scientific knowledge and heritage in general, exemplified by e.g. the works by Tycho.Future work includes further physical analysis of the books and fragments, a systematic extraction and digitization of the astronomical observations, digital curation and dissemination, as well as research into the possibility of creating representations and replicas of the works, durable on extremely long time-scales.
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Zardini Lacedelli, Stefania, Fabiana Fazzi, Chiara Zanetti, and Giacomo Pompanin. "FROM “EXHIBITION” TO “LABORATORY”: RETHINKING CURATORIAL PRACTICES THROUGH A DIGITAL EXPERIMENTAL PROJECT. THE CASE STUDY OF #DOLOMITESMUSEUM - LABORATORY OF STORIES." Herança 6, no. 1 (2023): 199–224. http://dx.doi.org/10.52152/heranca.v6i1.680.
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Digital curation is one of the most experimental areas of museum practice introduced by the digital revolution. A whole new generation of cultural experiences are emerging from the interaction with online spaces and social media platforms, encouraging museums to explore new curatorial approaches. In this paper, we will show how a digital experimental project led to the rethinking of the exhibition format into a more collaborative, interdisciplinary and laboratory approach to museum curation. The case study analysed is Laboratory of Stories, a dynamic participatory archive which was co-designed with a vibrant community of heritage professionals, local communities, and lovers of the Dolomites during a three-year project. An evaluation study combining different qualitative and quantitative methods was implemented to explore the nature of this curatorial practice. A series of key findings emerged from the study: the introduction of new narrative styles and interdisciplinary perspectives; the involvement of different communities in the curatorial process; the shift from a tangible, object-based, to a more intangible, open, and dialogic interpretation of heritage and of knowledge making. The findings show how the ‘laboratory’ metaphor can help museums to embrace the challenges of a participatory, post-digital society, suggesting a novel approach where the co-creation of narratives is at the heart of the curatorial practice.
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Lacedelli, Stefania, Fabiana Fazzi, Chiara Zanetti, and Giacomo Pompanin. "FROM “EXHIBITION” TO “LABORATORY”: RETHINKING CURATORIAL PRACTICES THROUGH A DIGITAL EXPERIMENTAL PROJECT. THE CASE STUDY OF #DOLOMITESMUSEUM - LABORATORY OF STORIES." Herança 6, no. 1 (2023): 199–224. http://dx.doi.org/10.29073/heranca.v6i1.680.
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Digital curation is one of the most experimental areas of museum practice introduced by the digital revolution. A whole new generation of cultural experiences are emerging from the interaction with online spaces and social media platforms, encouraging museums to explore new curatorial approaches. In this paper, we will show how a digital experimental project led to the rethinking of the exhibition format into a more collaborative, interdisciplinary and laboratory approach to museum curation. The case study analysed is Laboratory of Stories, a dynamic participatory archive which was co-designed with a vibrant community of heritage professionals, local communities, and lovers of the Dolomites during a three-year project. An evaluation study combining different qualitative and quantitative methods was implemented to explore the nature of this curatorial practice. A series of key findings emerged from the study: the introduction of new narrative styles and interdisciplinary perspectives; the involvement of different communities in the curatorial process; the shift from a tangible, object-based, to a more intangible, open, and dialogic interpretation of heritage and of knowledge making. The findings show how the ‘laboratory’ metaphor can help museums to embrace the challenges of a participatory, post-digital society, suggesting a novel approach where the co-creation of narratives is at the heart of the curatorial practice.
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Chen, Ying Dong, Yong Jun Wei, Jun Feng Zhang, Rong Guo Chen, Jiong Xie, and Zhen Chen. "The Application Practice of Geography Information Technology in Popular Science Area." Advanced Materials Research 468-471 (February 2012): 3012–19. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.3012.
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In this paper, it mainly research on the application of geography information technology in the popular science educational fields. It applies the space-time attribute and the graph visualization technology into the popular science knowledge creation, establishes the space-time information model of the popular science knowledge, and combines with the digital earth technology to make information of popular science to be expressed and exhibited in the method of graphical visually. It improves the accessibility of the popular science knowledge, and realizes an new method to popular science knowledge.
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Philp, Jude. "The more beautiful and gorgeous birds of British New Guinea." Memoirs of the Queensland Museum - Culture 13 (2022): 75–108. http://dx.doi.org/10.17082/j.2205-3239.13.1.2022.2022-03.
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This chapter describes the ways that collections were made in British New Guinea during Sir William MacGregor’s tenure (1888–1898) through a focus on the people employed in field collecting for bird specimens. It makes explicit the involvement of local peoples and other specialist collectors living in the region at the time. The creation of the collections was thus through two knowledge systems: that of western science and those of British New Guinean peoples. The influence of locals and other people is evident in the shape of the collection and the kinds of birds acquired. I argue that the curation of the bird specimens at Queensland Museum divorced specimens from the human contexts which contributed to reinforcing the hierarchical colonial structure. Despite this, examination of the collection’s composition shows the focus on ‘beautiful and gorgeous’ species which emphasises the knowledge of British New Guinea’s people and reveals the larger and more complex sphere of social relations that were a feature of MacGregor’s tenure.
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Shan, Shubing, and Buyang Cao. "Follow a guide to solve urban problems: the creation and application of urban knowledge graph." IET Software 11, no. 3 (2017): 126–34. http://dx.doi.org/10.1049/iet-sen.2016.0189.
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Li, Boyang, Stephen Lee-Urban, and Mark Riedl. "Toward Autonomous Crowd-Powered Creation of Interactive Narratives." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 8, no. 2 (2021): 20–25. http://dx.doi.org/10.1609/aiide.v8i2.12531.
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Interactive narrative is a form of storytelling that adapts to actions performed by users who assume the roles of story characters. To date, interactive narratives are built by hand. In this paper, we introduce Scheherazade, an intelligent system that automatically creates an interactive narrative about any topic from crowdsourced narratives. Our system leverages the experience and creativity of humans by crowdsourcing a corpus of linear narrative examples. It then constructs an executable plot graph, which is a knowledge structure that defines the legal space of an interactive narrative, by learning the plot events, execution precedence, and event separations. We demonstrate the system can successfully construct an interactive narrative based on noisy human input.
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Stylianou, Nikolaos, Danai Vlachava, Ioannis Konstantinidis, Nick Bassiliades, and Vassilios Peristeras. "Doc2KG." International Journal on Semantic Web and Information Systems 18, no. 1 (2022): 1–20. http://dx.doi.org/10.4018/ijswis.295552.
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Document Management Systems (DMS) are used for decades to store large amounts of information in textual form. Their technology paradigm is based on storing vast quantities of textual information enriched with metadata to support searchability. However, this exhibits limitations as it treats textual information as black box and is based exclusively on user-created metadata, a process that suffers from quality and completeness shortcomings. The use of knowledge graphs in DMS can substantially improve searchability, providing the ability to link data and enabling semantic searching. Recent approaches focus on either creating knowledge graphs from document collections or updating existing ones. In this paper, we introduce Doc2KG (Document-to-Knowledge-Graph), an intelligent framework that handles both creation and real-time updating of a knowledge graph, while also exploiting domain-specific ontology standards. We use DIAVGEIA (clarity), an award winning Greek open government portal, as our case-study and discuss new capabilities for the portal by implementing Doc2KG.
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Franke, Marco, Klaus-Dieter Thoben, and Beate Ehrhardt. "The Faceted and Exploratory Search for Test Knowledge." Information 14, no. 1 (2023): 45. http://dx.doi.org/10.3390/info14010045.
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Heterogeneous test processes concerning test goals and test script languages are an integral part of mechatronic systems development in supply chains. Here, test cases are written in a multitude of different test script languages. The translation between test script languages is possible, a joint understanding and a holistic view of the mechatronic system as a system under test is only achieved in the minds of experienced test engineers. This joined-up information is called test knowledge and is the key input for test automation and in turn, it is essential for reducing the cost of product development. Persisted test knowledge enables the search for patterns semi-automatically without reading countless test cases and enables the auto-completion of essential parts of test cases. In this paper, we developed a knowledge graph that aggregates all the test knowledge automatically and integrates it into the test processes. We derived an explorative search that simplifies the test case creation. For that purpose, a corresponding user-friendly query language, and unidirectional translation capabilities were developed that translates a test case into a graph tailored to the target audience of test engineers. We demonstrated the usage and impact of this approach by evaluating it on test cases from aircraft cabin doors.
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Anandakrishnan, Manju, Karen E. Ross, Chuming Chen, Vijay Shanker, Julie Cowart, and Cathy H. Wu. "KSFinder—a knowledge graph model for link prediction of novel phosphorylated substrates of kinases." PeerJ 11 (October 6, 2023): e16164. http://dx.doi.org/10.7717/peerj.16164.
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Background Aberrant protein kinase regulation leading to abnormal substrate phosphorylation is associated with several human diseases. Despite the promise of therapies targeting kinases, many human kinases remain understudied. Most existing computational tools predicting phosphorylation cover less than 50% of known human kinases. They utilize local feature selection based on protein sequences, motifs, domains, structures, and/or functions, and do not consider the heterogeneous relationships of the proteins. In this work, we present KSFinder, a tool that predicts kinase-substrate links by capturing the inherent association of proteins in a network comprising 85% of the known human kinases. We also postulate the potential role of two understudied kinases based on their substrate predictions from KSFinder. Methods KSFinder learns the semantic relationships in a phosphoproteome knowledge graph using a knowledge graph embedding algorithm and represents the nodes in low-dimensional vectors. A multilayer perceptron (MLP) classifier is trained to discern kinase-substrate links using the embedded vectors. KSFinder uses a strategic negative generation approach that eliminates biases in entity representation and combines data from experimentally validated non-interacting protein pairs, proteins from different subcellular locations, and random sampling. We assess KSFinder’s generalization capability on four different datasets and compare its performance with other state-of-the-art prediction models. We employ KSFinder to predict substrates of 68 “dark” kinases considered understudied by the Illuminating the Druggable Genome program and use our text-mining tool, RLIMS-P along with manual curation, to search for literature evidence for the predictions. In a case study, we performed functional enrichment analysis for two dark kinases - HIPK3 and CAMKK1 using their predicted substrates. Results KSFinder shows improved performance over other kinase-substrate prediction models and generalized prediction ability on different datasets. We identified literature evidence for 17 novel predictions involving an understudied kinase. All of these 17 predictions had a probability score ≥0.7 (nine at >0.9, six at 0.8–0.9, and two at 0.7–0.8). The evaluation of 93,593 negative predictions (probability ≤0.3) identified four false negatives. The top enriched biological processes of HIPK3 substrates relate to the regulation of extracellular matrix and epigenetic gene expression, while CAMKK1 substrates include lipid storage regulation and glucose homeostasis. Conclusions KSFinder outperforms the current kinase-substrate prediction tools with higher kinase coverage. The strategically developed negatives provide a superior generalization ability for KSFinder. We predicted substrates of 432 kinases, 68 of which are understudied, and hypothesized the potential functions of two dark kinases using their predicted substrates.
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Graham, Shawn, Donna Yates, and Ahmed El-Roby. "Investigating antiquities trafficking with generative pre-trained transformer (GPT)-3 enabled knowledge graphs: A case study." Open Research Europe 3 (June 20, 2023): 100. http://dx.doi.org/10.12688/openreseurope.16003.1.
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Background: There is a wide variety of potential sources from which insight into the antiquities trade could be culled, from newspaper articles to auction catalogues, to court dockets, to personal archives, if it could all be systematically examined. We explore the use of a large language model, GPT-3, to semi-automate the creation of a knowledge graph of a body of scholarship concerning the antiquities trade. Methods: We give GPT-3 a prompt guiding it to identify knowledge statements around the trade. Given GPT-3’s understanding of the statistical properties of language, our prompt teaches GPT-3 to append text to each article we feed it where the appended text summarizes the knowledge in the article. The summary is in the form of a list of subject, predicate, and object relationships, representing a knowledge graph. Previously we created such lists by manually annotating the source articles. We compare the result of this automatic process with a knowledge graph created from the same sources via hand. When such knowledge graphs are projected into a multi-dimensional embedding model using a neural network (via the Ampligraph open-source Python library), the relative positioning of entities implies the probability of a connection; the direction of the positioning implies the kind of connection. Thus, we can interrogate the embedding model to discover new probable relationships. The results can generate new insight about the antiquity trade, suggesting possible avenues of research. Results: We find that our semi-automatic approach to generating the knowledge graph in the first place produces comparable results to our hand-made version, but at an enormous savings of time and a possible expansion of the amount of materials we can consider. Conclusions: These results have implications for working with other kinds of archaeological knowledge in grey literature, reports, articles, and other venues via computational means.
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Koneru, Sai Dileep, David Rench McCauley, Michael C. Smith, David Guarrera, Jenn Robinson, and Sarah Rajtmajer. "The evolution of scientific literature as metastable knowledge states." PLOS ONE 18, no. 7 (2023): e0287226. http://dx.doi.org/10.1371/journal.pone.0287226.
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The problem of identifying common concepts in the sciences and deciding when new ideas have emerged is an open one. Metascience researchers have sought to formalize principles underlying stages in the life cycle of scientific research, understand how knowledge is transferred between scientists and stakeholders, and explain how new ideas are generated and take hold. Here, we model the state of scientific knowledge immediately preceding new directions of research as a metastable state and the creation of new concepts as combinatorial innovation. Through a novel approach combining natural language clustering and citation graph analysis, we predict the evolution of ideas over time and thus connect a single scientific article to past and future concepts in a way that goes beyond traditional citation and reference connections.
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Yang, Yangrui, Yaping Zhu, and Pengpeng Jian. "Application of Knowledge Graph in Water Conservancy Education Resource Organization under the Background of Big Data." Electronics 11, no. 23 (2022): 3913. http://dx.doi.org/10.3390/electronics11233913.
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The key to improving the readability and usage of educational resources is their orderly arrangement and integration. Knowledge graphs, which are a large-scale form of knowledge engineering, are an effective tool for managing and organizing educational resources. The water conservancy’s educational big data is separated into three tiers of objectives–courses–knowledge units based on the connotation level of self-directed learning. Combined with the idea of Outcome-based Education(OBE), the goal-oriented knowledge graph structure of water conservancy disciplines and graph creation method is proposed. The focus is the error accumulation problem brought about by the traditional relational extraction method of Named Entity Recognition based on rules or sequence labeling. We first complete this objective, and then the relationship classification is performed according to the water conservancy disciplines entity and relations joint extraction (WDERJE) model, on which the prompt mechanism design is based. Think of the entity-relationship extraction task as a sequence-to-sequence generation task, and take the structured extraction language to unify the coding entity extraction and relationship extraction structures. The evaluation results of the WDERJE model show that the F_0.5 value of each entity extraction is above 0.76, and the cumulative extraction relationship triple is nearly 180,000. The graph fully optimizes the organization and management of water conservancy education resources and effectively improves the readability and utilization rate of water conservancy teaching resources.
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Potoniec, Jedrzej. "Mining Cardinality Restrictions in OWL." Foundations of Computing and Decision Sciences 45, no. 3 (2020): 195–216. http://dx.doi.org/10.2478/fcds-2020-0011.
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AbstractWe present an approach to mine cardinality restriction axioms from an existing knowledge graph, in order to extend an ontology describing the graph. We compare frequency estimation with kernel density estimation as approaches to obtain the cardinalities in restrictions. We also propose numerous strategies for filtering obtained axioms in order to make them more available for the ontology engineer. We report the results of experimental evaluation on DBpedia 2016-10 and show that using kernel density estimation to compute the cardinalities in cardinality restrictions yields more robust results that using frequency estimation. We also show that while filtering is of limited usability for minimum cardinality restrictions, it is much more important for maximum cardinality restrictions. The presented findings can be used to extend existing ontology engineering tools in order to support ontology construction and enable more efficient creation of knowledge-intensive artificial intelligence systems.
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Holt, Marilyn E., Kathleen F. Mittendorf, Michele LeNoue-Newton, et al. "My Cancer Genome: Coevolution of Precision Oncology and a Molecular Oncology Knowledgebase." JCO Clinical Cancer Informatics, no. 5 (September 2021): 995–1004. http://dx.doi.org/10.1200/cci.21.00084.
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PURPOSE The My Cancer Genome (MCG) knowledgebase and resulting website were launched in 2011 with the purpose of guiding clinicians in the application of genomic testing results for treatment of patients with cancer. Both knowledgebase and website were originally developed using a wiki-style approach that relied on manual evidence curation and synthesis of that evidence into cancer-related biomarker, disease, and pathway pages on the website that summarized the literature for a clinical audience. This approach required significant time investment for each page, which limited website scalability as the field advanced. To address this challenge, we designed and used an assertion-based data model that allows the knowledgebase and website to expand with the field of precision oncology. METHODS Assertions, or computationally accessible cause and effect statements, are both manually curated from primary sources and imported from external databases and stored in a knowledge management system. To generate pages for the MCG website, reusable templates transform assertions into reconfigurable text and visualizations that form the building blocks for automatically updating disease, biomarker, drug, and clinical trial pages. RESULTS Combining text and graph templates with assertions in our knowledgebase allows generation of web pages that automatically update with our knowledgebase. Automated page generation empowers rapid scaling of the website as assertions with new biomarkers and drugs are added to the knowledgebase. This process has generated more than 9,100 clinical trial pages, 18,100 gene and alteration pages, 900 disease pages, and 2,700 drug pages to date. CONCLUSION Leveraging both computational and manual curation processes in combination with reusable templates empowers automation and scalability for both the MCG knowledgebase and MCG website.
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Piletski, I. I., M. Р. Batura, and L. Y. Shylin. "Graph technologies in an intelligent system of complex analysis of data from Internet sources." Doklady BGUIR 18, no. 5 (2020): 89–97. http://dx.doi.org/10.35596/1729-7648-2020-18-5-89-97.
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The purpose of the work outlined in the article is to review and demonstrate the use of graph technologies for deep data analysis. The first part of the article discusses the Intelligent System for the Comprehensive Analysis of Internet Sources Data and its possible directions for its further development. This system is a multi-purpose cluster using technologies for constructing a knowledge graph, methods and models of machine learning for in-depth analysis of data from Internet sources (for example, scientific publications, social networks, media). The purpose of the analysis is to identify the most important publications in a certain area (for example, in robotics, space research, healthcare, in the social sphere), thematic analysis of these publications, to identify the leader of a scientific direction and to predict trends in the development of directions and interaction of groups of people. When developing this system, we utilized probabilistic machine learning algorithms and methods for constructing and maintaining a graph model of the social network of authors and their publications, determining the rating of a particular author, determining the topics of publications and classifying them by areas of knowledge. The basis for the creation of intelligent applications is graph technology, which allows you to make predictions that are more accurate. The combined application of methods and algorithms of machine learning with graph technologies allows you to get hidden dependencies and perform predictive analysis of information, get answers in real time, and implement artificial intelligence algorithms. Methods of collaboration with graph technologies and a learning machine (for example, using neural networks) are based on graph embedding. This technology allows you to perform a comprehensive, deep and intelligent analysis of information. At the end of the article, there are analytical reports obtained using graph technologies in the Intelligent System for Complex Analysis of Internet Sources Data.
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Quaeghebeur, Erik, Sebastian Sanchez Perez-Moreno, and Michiel B. Zaaijer. "WESgraph: a graph database for the wind farm domain." Wind Energy Science 5, no. 1 (2020): 259–84. http://dx.doi.org/10.5194/wes-5-259-2020.
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Abstract. The construction and management of a wind farm involve many disciplines. It is hard for a single designer or developer to keep an overview of all the relevant concepts, models, and tools. Nevertheless, this is needed when performing integrated modeling or analysis. To help researchers keep this overview, we have created WESgraph (the Wind Energy System graph), a knowledge base for the wind farm domain, implemented as a graph database. It currently contains 1222 concepts and 1725 relations between them. This paper presents the structure of this graph database – content stored in nodes and the relationships between them – as a foundational ontology, which classifies the domain's concepts. This foundational ontology partitions the domain in two: a part describing physical aspects and a part describing mathematical and computational aspects. This paper also discusses a number of generally difficult cases that exist when adding content to such a knowledge base. This paper furthermore discusses the potential applications of WESgraph and illustrates its use for computation pathway discovery – the application that triggered its creation. It also contains a description of our practical experience with its design and use as well as our thoughts about the community use and management of this tool.