The Semantic Knowledge Graph: A Compact, Auto-Generated Model for Real-Time Traversal and Ranking of any Relationship within a Domain

Grainger, Trey; AlJadda, Khalifeh; Korayem, Mohammed; Smith, Andries

doi:10.1109/dsaa.2016.51

Cited by 18 publications

(5 citation statements)

References 13 publications

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“…The Semantic Knowledge Graph is a novel knowledge representation and discovery model, which can dynamically describe and discover variant relationships among any combination of entities in a specific domain. It achieves this by the method of materializing nodes and edges in an intuitive graphical representation of a specific knowledge domain [55]. Compared with the above, this type of data organization and knowledge representation can effectively combine the advantages of expressive capability and efficient computability.…”

Section: Semantic Knowledge Graph Based On Semantic Networkmentioning

confidence: 99%

Remote Sensing Image Interpretation With Semantic Graph-Based Methods: A Survey

Sun

Dustdar

Ranjan

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the significant improvements in Earth observation (EO) technologies, remote sensing (RS) data exhibit the typical characteristics of Big Data. Propelled by the powerful feature extraction capabilities of intelligent algorithms, remote sensing image interpretation has drawn remarkable attention and achieved progress. However, the semantic relationship and domain knowledge hidden in massive RS images have not been fully exploited. To the best of our knowledge, a comprehensive review of recent achievements regarding semantic graph-based methods for comprehension and interpretation of remote sensing images is still lacking. Specifically, this article discusses the main challenges of remote sensing image interpretation and presents a systematic survey of typical semantic graph-based methodologies for RS knowledge representation and understanding, including the Ontology Model, Geo-Information Tupu, and Semantic Knowledge Graph. Furthermore, we categorize and summarize how the existing technologies address different challenges in RS image interpretation based on semantic graph-based methods, which indicates that the semantic information about potential relationships and prior knowledge of variant RS targets are central to the solution. In addition, a case study of RS geological interpretation based on the semantic knowledge graph is demonstrated to show the promising capability of intelligent RS image interpretation. Finally, the future directions are discussed for further research.

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Section: Semantic Knowledge Graph Based On Semantic Networkmentioning

confidence: 99%

Remote Sensing Image Interpretation With Semantic Graph-Based Methods: A Survey

Sun

Dustdar

Ranjan

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In recent years, domain knowledge graphs for a single industry have emerged continuously. Grainger et al [6] automatically and dynamically build domain semantic networks based on corpus data. Pan et al [7] use the linked data of large organizations to build and deploy industry-oriented data.…”

Section: Related Workmentioning

confidence: 99%

Metro Traffic Flow Prediction via Knowledge Graph and Spatiotemporal Graph Neural Network

Wang

Yuan

et al. 2022

Journal of Advanced Transportation

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Existing traffic flow prediction methods generally only consider the spatiotemporal characteristics of traffic flow. However, in addition to the spatiotemporal characteristics, the interference of various external factors needs to be considered in traffic flow prediction, including severe weather, major events, traffic control, and metro failures. The current research still cannot fully use the information contained in these external factors. To address this issue, we propose a novel metro traffic flow prediction method (KGR-STGNN) based on knowledge graph representation learning. We construct a knowledge graph that stores factors related to metro traffic networks. Through the knowledge graph representation learning technology, we can learn the influence representation of external factors from the traffic knowledge graph, which can better incorporate the influence of external factors into the prediction model based on the spatiotemporal graph neural network. Experimental results demonstrate the effectiveness of our proposed model.

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“…Semantic Knowledge Graphs are used recently as powerful representation of entities and relations between them (Paulheim, 2017). Often knowledge graphs are generated automatically from semistructured resources or from the documents underpinning various ontologies, through terms/words they contain, by a combination of linguistic and statistical methods (Grainger et al, 2016).…”

Section: Semantic Modelmentioning

confidence: 99%

Risk Factors Extraction from Clinical Texts based on Linked Open Data

Boytcheva

Angelova

Angelov

2019

Proceedings - Natural Language Processing in a Deep Learning World

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This paper presents experiments in risk factors analysis based on clinical texts enhanced with Linked Open Data (LOD). The idea is to determine whether a patient has risk factors for a specific disease analyzing only his/her outpatient records. A semantic graph of "meta-knowledge" about a disease of interest is constructed, with integrated multilingual terms (labels) of symptoms, risk factors etc. coming from Wikidata, PubMed, Wikipedia and MESH, and linked to clinical records of individual patients via ICD-10 codes. Then a predictive model is trained to foretell whether patients are at risk to develop the disease of interest. The testing was done using outpatient records from a nation-wide repository available for the period 2011-2016. The results show improvement of the overall performance of all tested algorithms (kNN, Naïve Bayes, Tree, Logistic regression, ANN), when the clinical texts are enriched with LOD resources.

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The Semantic Knowledge Graph: A Compact, Auto-Generated Model for Real-Time Traversal and Ranking of any Relationship within a Domain

Abstract: Abstract-This paper describes a new kind of knowledge representation and mining system which we are calling the Se-

Cited by 18 publications

References 13 publications

Remote Sensing Image Interpretation With Semantic Graph-Based Methods: A Survey

Remote Sensing Image Interpretation With Semantic Graph-Based Methods: A Survey

Metro Traffic Flow Prediction via Knowledge Graph and Spatiotemporal Graph Neural Network

Risk Factors Extraction from Clinical Texts based on Linked Open Data

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