Knowledge Graph Creation Research Articles

A Macroscopic Exploration of the Ideoscape on Exosomes for Bone Regeneration

Background: Exosomes, nanoscale extracellular vesicles, play a crucial role in tissue physiology and regeneration. This study uses infometric techniques to explore the structure of exosome-based tissue and bone regeneration research. Methods: We applied BERTopic, an advanced topic modeling algorithm, to a comprehensive corpus of the scientific literature on exosomes and tissue regeneration, identifying key themes such as stem cell studies, tissue healing, and regenerative applications, with orthopedics and dentistry emerging as dominant subfields. To further investigate the ‘ideoscape’, i.e., the conceptual landscape that maps how ideas, methods, and themes are interconnected across the field, we extracted significant concepts from abstracts using GPT 3.5 turbo and created knowledge graphs. Results: Our analysis revealed rapid growth in the field of dental stem cell regeneration, which has outpaced other bone regeneration topics by twofold. This analysis highlighted central themes such as periodontal stem cells and their cellular processes—proliferation, migration, and differentiation—along with their clinical applications. Our approach provided a clear visualization of the field’s intellectual structure, showing how emerging topics are interconnected. Our findings offer a comprehensive view of the evolving trends in exosome-based bone regeneration, revealing not only the most active research areas but also gaps and opportunities for further investigation. Conclusions: This study exemplifies the utility of combining topic modeling with knowledge graph creation to map research trends, offering a flexible and largely automated tool for researchers to explore the vast bodies of literature and guide future research directions.

MeDaX: A Knowledge Graph on FHIR.

In Germany, the standard format for exchange of clinical care data for research is HL7 FHIR. Graph databases (GDBs), well suited for integrating complex and heterogeneous data from diverse sources, are currently gaining traction in the medical field. They provide a versatile framework for data analysis which is generally challenging for raw FHIR-formatted data. For generation of a knowledge graph (KG) for clinical research data, we tested different extract-transform-load (ETL) approaches to convert FHIR into graph format. We designed a generalised ETL process and implemented a prototypic pipeline for automated KG creation and ontological structuring. The MeDaX-KG prototype is built from synthetic patient data and currently serves internal testing purposes. The presented approach is easy to customise to expand to other data types and formats.

Ontology design facilitating Wikibase integration — and a worked example for historical data

Wikibase – which is the software underlying Wikidata – is a powerful platform for knowledge graph creation and management. However, it has been developed with a crowd-sourced knowledge graph creation scenario in mind, which in particular means that it has not been designed for use case scenarios in which a tightly controlled high-quality schema, in the form of an ontology, is to be imposed, and indeed, independently developed ontologies do not necessarily map seamlessly to the Wikibase approach. In this paper, we provide the key ingredients needed in order to combine traditional ontology modeling with use of the Wikibase platform, namely a set of axiom patterns that bridge the paradigm gap, together with usage instructions and a worked example for historical data.

From data to knowledge: Construction process analysis through continuous image capturing, object detection, and knowledge graph creation

Persistent issues of schedule deviations and cost overruns within large construction projects aggravate the construction industry’s global productivity concerns. However, how holistic, data-oriented methods can effectively be leveraged for investigating project performance and identifying potential bottlenecks during the construction phase remains unanswered. Our research addresses this issue with a novel approach encompassing data acquisition, object detection, geometric projection, and graph-based linking. Image data, continuously captured by crane-camera systems, gets transformed into higher-level information using an end-to-end deep learning-based pipeline that covers the detection of specific on-site objects and integrates it in a knowledge graph. The knowledge graph facilitates extracting precise construction metrics, identifying spatiotemporal irregularities, like work hotspots characterized by high activity and intensive work concentrations, but also phases with low activity. The proposed method improves learning from past construction data, aiding stakeholders and inspiring further research into real-time monitoring, predictive analytics, and data-integrated decision-making systems to reshape construction practices.

Automatic knowledge graph creation from engineering standards using the example of formulas

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.

Empowering the SDM-RDFizer tool for scaling up to complex knowledge graph creation pipelines1

The significant increase in data volume in recent years has prompted the adoption of knowledge graphs as valuable data structures for integrating diverse data and metadata. However, this surge in data availability has brought to light challenges related to standardization, interoperability, and data quality. Knowledge graph creation faces complexities from large data volumes, data heterogeneity, and high duplicate rates. This work addresses these challenges and proposes data management techniques to scale up the creation of knowledge graphs specified using the RDF Mapping Language (RML). These techniques are integrated into SDM-RDFizer, transforming it into a two-fold solution designed to address the complexities of generating knowledge graphs. Firstly, we introduce a reordering approach for RML triples maps, prioritizing the evaluation of the most selective maps first to reduce memory usage. Secondly, we employ an RDF compression strategy, along with optimized data structures and novel operators, to prevent the generation of duplicate RDF triples and optimize the execution of RML operators. We assess the performance of SDM-RDFizer through established benchmarks. The evaluation showcases the effectiveness of SDM-RDFizer compared to state-of-the-art RML engines, emphasizing the benefits of our techniques. Furthermore, the paper presents real-world projects where SDM-RDFizer has been utilized, providing insights into the advantages of declaratively defining knowledge graphs and efficiently executing these specifications using this engine.

Convolutional neural network based data interpretable framework for Alzheimer’s treatment planning

Alzheimer’s disease (AD) is a neurological disorder that predominantly affects the brain. In the coming years, it is expected to spread rapidly, with limited progress in diagnostic techniques. Various machine learning (ML) and artificial intelligence (AI) algorithms have been employed to detect AD using single-modality data. However, recent developments in ML have enabled the application of these methods to multiple data sources and input modalities for AD prediction. In this study, we developed a framework that utilizes multimodal data (tabular data, magnetic resonance imaging (MRI) images, and genetic information) to classify AD. As part of the pre-processing phase, we generated a knowledge graph from the tabular data and MRI images. We employed graph neural networks for knowledge graph creation, and region-based convolutional neural network approach for image-to-knowledge graph generation. Additionally, we integrated various explainable AI (XAI) techniques to interpret and elucidate the prediction outcomes derived from multimodal data. Layer-wise relevance propagation was used to explain the layer-wise outcomes in the MRI images. We also incorporated submodular pick local interpretable model-agnostic explanations to interpret the decision-making process based on the tabular data provided. Genetic expression values play a crucial role in AD analysis. We used a graphical gene tree to identify genes associated with the disease. Moreover, a dashboard was designed to display XAI outcomes, enabling experts and medical professionals to easily comprehend the prediction results.

Knowledge Representation and Management 2022: Findings in Ontology Development and Applications.

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. 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. 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. 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.

Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have been in use in a variety of intelligent medical applications. Thus, understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field. To this end, we offer an in-depth review of MKG in this work. Our research begins with the examination of four types of medical information sources, knowledge graph creation methodologies, and six major themes for MKG development. Furthermore, three popular models of reasoning from the viewpoint of knowledge reasoning are discussed. A reasoning implementation path (RIP) is proposed as a means of expressing the reasoning procedures for MKG. In addition, we explore intelligent medical applications based on RIP and MKG and classify them into nine major types. Finally, we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

Knowledge Graph Creation based on Ontology from Source-Code: The Case of C#

Knowledge Graph Creation based on Ontology from Source-Code: The Case of C#

Semi-Supervised Bootstrapped Syntax-Semantics-Based Approach for Agriculture Relation Extraction for Knowledge Graph Creation and Reasoning

Semi-Supervised Bootstrapped Syntax-Semantics-Based Approach for Agriculture Relation Extraction for Knowledge Graph Creation and Reasoning

Scaling up knowledge graph creation to large and heterogeneous data sources

RDF knowledge graphs (KG) are powerful data structures to represent factual statements created from heterogeneous data sources. KG creation is laborious and demands data management techniques to be executed efficiently. This paper tackles the problem of the automatic generation of KG creation processes declaratively specified; it proposes techniques for planning and transforming heterogeneous data into RDF triples following mapping assertions specified in the RDF Mapping Language (RML). Given a set of mapping assertions, the planner provides an optimized execution plan by partitioning and scheduling the execution of the assertions. First, the planner assesses an optimized number of partitions considering the number of data sources, type of mapping assertions, and the associations between different assertions. After providing a list of partitions and assertions that belong to each partition, the planner determines their execution order. A greedy algorithm is implemented to generate the partitions’ bushy tree execution plan. Bushy tree plans are translated into operating system commands that guide the execution of the partitions of the mapping assertions in the order indicated by the bushy tree. The proposed optimization approach is evaluated over state-of-the-art RML-compliant engines, and existing benchmarks of data sources and RML triples maps. Our experimental results suggest that the performance of the studied engines can be considerably improved, particularly in a complex setting with numerous triples maps and large data sources. As a result, engines that time out in complex cases are enabled to produce at least a portion of the KG applying the planner.

Comparison of biomedical relationship extraction methods and models for knowledge graph creation

Biomedical research is growing at such an exponential pace that scientists, researchers, and practitioners are no more able to cope with the amount of published literature in the domain. The knowledge presented in the literature needs to be systematized in such a way that claims and hypotheses can be easily found, accessed, and validated. Knowledge graphs can provide such a framework for semantic knowledge representation from literature. However, in order to build a knowledge graph, it is necessary to extract knowledge as relationships between biomedical entities and normalize both entities and relationship types. In this paper, we present and compare a few rule-based and machine learning-based (Naive Bayes, Random Forests as examples of traditional machine learning methods and DistilBERT, PubMedBERT, T5, and SciFive-based models as examples of modern deep learning transformers) methods for scalable relationship extraction from biomedical literature, and for the integration into the knowledge graphs. We examine how resilient are these various methods to unbalanced and fairly small datasets. Our experiments show that transformer-based models handle well both small (due to pre-training on a large dataset) and unbalanced datasets. The best performing model was the PubMedBERT-based model fine-tuned on balanced data, with a reported F1-score of 0.92. The distilBERT-based model followed with an F1-score of 0.89, performing faster and with lower resource requirements. BERT-based models performed better than T5-based generative models.

Building Semantic Knowledge Graphs from (Semi-)Structured Data: A Review

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.

Geotagging: Systematic Anatomization and Conceptual Model for POI Verification

People have been contributing large amount of data every day in Geographical Information Portal however to harness the real power of this tremendous amount of data, it must be managed efficiently. There are some challenges with this data, such as authentication and skewedness. Maximum public places even in a small village must be geotagged to provide better citizen centric services specifically in developing countries. Hence, it’s a need of hour to enrich the unique Indian GIS portal "Bhuvan" with Point of Interest (POI) data where one can find all necessary information. Although POI on Bhuvan is improving day by day however to overcome the challenges mentioned above is an important task. Therefore, a framework is required to expedite the tagging and authentication process of the tagged data in an efficient manner to exploit the power of POI data. In this paper, we have explored the available techniques to improve and verify the crowd sourced data and propose a conceptual model to accomplish the objective of verifying and managing the geotagged data to enrich the portal. A trust rank parameter is introduced to ensure the quality of the POI data. This will be calculated using multisource verification model using state of the art open source technologies available. This verified POI data can be used further in knowledge Graph creation to get better search facility.