scholarly journals Constructing high-fidelity phenotype knowledge graphs for infectious diseases with a fine-grained semantic information model: development and usability study (Preprint)

10.2196/26892 ◽  
2021 ◽  
Author(s):  
Lizong Deng ◽  
Luming Chen ◽  
Tao Yang ◽  
Mi Liu ◽  
Shicheng Li ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Semantic Information ◽  
Model Development ◽  
Information Model ◽  
High Fidelity ◽  
Usability Study ◽  
Fine Grained ◽  
Knowledge Graphs

scholarly journals Correction: Constructing High-Fidelity Phenotype Knowledge Graphs for Infectious Diseases With a Fine-Grained Semantic Information Model: Development and Usability Study (Preprint)

2021 ◽  
Author(s):  
Lizong Deng ◽  
Luming Chen ◽  
Tao Yang ◽  
Mi Liu ◽  
Shicheng Li ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Semantic Information ◽  
Model Development ◽  
Information Model ◽  
High Fidelity ◽  
Usability Study ◽  
Systems Medicine ◽  
Medical Sciences ◽  
Fine Grained ◽  
Knowledge Graphs

UNSTRUCTURED In “Constructing High-Fidelity Phenotype Knowledge Graphs for Infectious Diseases With a Fine-Grained Semantic Information Model: Development and Usability Study” (J Med Internet Res 2021;23(6):e26892) the authors noted one error. The institution name of affiliation “Suzhou Institute of Systems Medicine” was not correct. It should be corrected from “Suzhou Institute of Systems Medicine” to “Center of Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College; Suzhou Institute of Systems Medicine”

scholarly journals Constructing high-fidelity phenotype knowledge graphs with a fine-grained semantic information model (Preprint)

2021 ◽  
Author(s):  
Lizong Deng ◽  
Luming Chen ◽  
Tao Yang ◽  
Mi Liu ◽  
Shicheng Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Infectious Diseases ◽  
Clinical Guidelines ◽  
Semantic Information ◽  
Information Model ◽  
Coarse Grained ◽  
Hybrid Strategy ◽  
Fine Grained ◽  
Knowledge Graphs ◽  
Attribute Value

BACKGROUND Phenotypes characterize clinical manifestations of disease, which provide important information for diagnosis. Therefore, constructing phenotype knowledge graphs of disease is valuable to the development of artificial intelligence in medicine. However, phenotype knowledge graphs in current knowledge bases such as WikiData and DBpedia are coarse-grained knowledge graphs, because they only consider core concepts of phenotypes but neglects details (attributes) associated with phenotypes. OBJECTIVE To characterize details of disease phenotypes in clinical guidelines, we proposed a fine-grained semantic information model named PhenoSSU (Semantic Structured Unit of Phenotypes). METHODS PhenoSSU is an "entity-attribute-value" model by its very nature, which aims to capture full semantics underlying phenotype descriptions with a series of attributes and values. 193 clinical guidelines of infectious diseases from Wikipedia were selected as the study corpus, and 12 attributes from SNOMED-CT were introduced into the PhenoSSU model based on co-occurrences of phenotype concepts and attribute values. The expressive power of the PhenoSSU model was evaluated by analyzing whether a PhenoSSU instance could capture full semantic underlying the corresponding phenotype description. To automatically construct fine-grained phenotype knowledge graphs, A hybrid strategy that firstly recognized phenotype concepts with the MetaMap tool and then predicted attribute values of phenotypes with machine learning classifiers was developed. RESULTS Fine-grained phenotype knowledge graphs of 193 infectious diseases were manually constructed with the BRAT annotation tool. It was found that the PhenoSSU model could precisely represent 89.5% (3757/4020) of phenotype descriptions in clinical guidelines. By comparison, other information models such as the Clinical Element Model and the HL7 FHIR model could only capture full semantics underlying 48.4% and 21.8% of phenotype descriptions, respectively. The hybrid strategy achieved an F1-score of 0.732 for the subtask of phenotype concept recognition and an average weighted accuracy of 0.776 for the subtask of attribute value prediction. CONCLUSIONS PhenoSSU is an effective information model for the precise representation of phenotype knowledge in clinical guidelines, and machine learning can be used to improve efficiency for constructing PhenoSSU-based knowledge graphs. Our work will potentially benefit knowledge-based systems for diagnosis.

scholarly journals Modeling the Compaction Characteristics of Fine-Grained Soils Blended with Tire-Derived Aggregates

2021 ◽  
Vol 13 (14) ◽  
pp. 7737
Author(s):  
Amin Soltani ◽  
Mahdieh Azimi ◽  
Brendan C. O’Kelly
Keyword(s):  
Energy Levels ◽  
Model Development ◽  
Dry Mass ◽  
Unit Weight ◽  
Power Functions ◽  
Compaction Characteristics ◽  
Practical Applications ◽  
Fine Grained ◽  
Practical Procedure ◽  
Fine Grained Soil

This study aims at modeling the compaction characteristics of fine-grained soils blended with sand-sized (0.075–4.75 mm) recycled tire-derived aggregates (TDAs). Model development and calibration were performed using a large and diverse database of 100 soil–TDA compaction tests (with the TDA-to-soil dry mass ratio ≤ 30%) assembled from the literature. Following a comprehensive statistical analysis, it is demonstrated that the optimum moisture content (OMC) and maximum dry unit weight (MDUW) for soil–TDA blends (across different soil types, TDA particle sizes and compaction energy levels) can be expressed as universal power functions of the OMC and MDUW of the unamended soil, along with the soil to soil–TDA specific gravity ratio. Employing the Bland–Altman analysis, the 95% upper and lower (water content) agreement limits between the predicted and measured OMC values were, respectively, obtained as +1.09% and −1.23%, both of which can be considered negligible for practical applications. For the MDUW predictions, these limits were calculated as +0.67 and −0.71 kN/m3, which (like the OMC) can be deemed acceptable for prediction purposes. Having established the OMC and MDUW of the unamended fine-grained soil, the empirical models proposed in this study offer a practical procedure towards predicting the compaction characteristics of the soil–TDA blends without the hurdles of performing separate laboratory compaction tests, and thus can be employed in practice for preliminary design assessments and/or soil–TDA optimization studies.

scholarly journals Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks

2020 ◽  
Vol 34 (05) ◽  
pp. 8074-8081
Author(s):  
Pavan Kapanipathi ◽  
Veronika Thost ◽  
Siva Sankalp Patel ◽  
Spencer Whitehead ◽  
Ibrahim Abdelaziz ◽  
...  
Keyword(s):  
Language Processing ◽  
Prediction Accuracy ◽  
Semantic Information ◽  
Training Data ◽  
Personalized Pagerank ◽  
External Knowledge ◽  
Convolutional Networks ◽  
Textual Entailment ◽  
Knowledge Graphs ◽  
Textual Content

Textual entailment is a fundamental task in natural language processing. Most approaches for solving this problem use only the textual content present in training data. A few approaches have shown that information from external knowledge sources like knowledge graphs (KGs) can add value, in addition to the textual content, by providing background knowledge that may be critical for a task. However, the proposed models do not fully exploit the information in the usually large and noisy KGs, and it is not clear how it can be effectively encoded to be useful for entailment. We present an approach that complements text-based entailment models with information from KGs by (1) using Personalized PageRank to generate contextual subgraphs with reduced noise and (2) encoding these subgraphs using graph convolutional networks to capture the structural and semantic information in KGs. We evaluate our approach on multiple textual entailment datasets and show that the use of external knowledge helps the model to be robust and improves prediction accuracy. This is particularly evident in the challenging BreakingNLI dataset, where we see an absolute improvement of 5-20% over multiple text-based entailment models.

Developing building information models (BIM) for building handover, operation and maintenance

2019 ◽  
Vol 17 (3) ◽  
pp. 301-316 ◽  
Author(s):  
Marjan Sadeghi ◽  
Jonathan Weston Elliott ◽  
Nick Porro ◽  
Kelly Strong
Keyword(s):  
Information Exchange ◽  
Model Development ◽  
Information Model ◽  
Content Type ◽  
Operation And Maintenance ◽  
Information Models ◽  
Building Information ◽  
Essential Input ◽  
Construction Operation

PurposeThis paper aims to represent the results of a case study to establish a building information model (BIM)-enabled workflow to capture and retrieve facility information to deliver integrated handover deliverables.Design/methodology/approachThe Building Handover Information Model (BHIM) framework proposed herein is contextualized given the Construction Operation Information Exchange (COBie) and the level of development schema. The process uses Autodesk Revit as the primary BIM-authoring tool and Dynamo as an add-in for extending Revit’s parametric functionality, BHIM validation, information retrieval and documentation in generating operation and maintenance (O&M) deliverables in the end-user requested format.FindingsGiven the criticality of semantics for model elements in the BHIM and for appropriate interoperability in BIM collaboration, each discipline should establish model development and exchange protocols that define the elements, geometrical and non-geometrical information requirements and acceptable software applications early in the design phase. In this case study, five information categories (location, specifications, warranty, maintenance instructions and Construction Specifications Institute MasterFormat division) were identified as critical for model elements in the BHIM for handover purposes.Originality/valueDesign- and construction-purposed BIM is a standard platform in collaborative architecture, engineering and construction practice, and the models are available for many recently constructed facilities. However, interoperability issues drastically restrict implementation of these models in building information handover and O&M. This study provides essential input regarding BIM exchange protocols and collaborative BIM libraries for handover purposes in collaborative BIM development.

Modeling and Mapping Implementation of Substation Knowledge Ontology Based on Protégé

2012 ◽  
Vol 198-199 ◽  
pp. 786-789
Author(s):  
Tie Feng Zhang ◽  
Shu Juan Han ◽  
Jian Wei Gu
Keyword(s):  
Information Sharing ◽  
Semantic Information ◽  
Information Model ◽  
Mapping Method ◽  
Basic Knowledge ◽  
Web Ontology Language ◽  
Ontology Language ◽  
Master Station ◽  
Knowledge Ontology

Based on the basic knowledge of ontology and protégé, and the deficiency of semantic expression in the IEC61850 and IEC61970 Standard, this paper puts forward a mapping method from SCL to CIM, adopting Web Ontology Language OWL to build the semantic information model of SCL and CIM of substation knowledge ontology. In substation model, this mapping method could solve the problem of information sharing and interoperation between digitized substation and dispatch master station, and lay a foundation for further research on fusion of the two standards.

scholarly journals A Comprehensive Review of Practical Issues for Interoperability Using the Common Information Model in Smart Grids

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1435
Author(s):  
Hyun Joong Kim ◽  
Chang Min Jeong ◽  
Jin-Man Sohn ◽  
Jhi-Young Joo ◽  
Vaibhav Donde ◽  
...  
Keyword(s):  
Energy Management ◽  
Information Exchange ◽  
Smart Grids ◽  
Management System ◽  
Semantic Information ◽  
Information Model ◽  
Energy Management System ◽  
Common Information ◽  
Common Information Model ◽  
The Common

Smart grids with interoperability improve grid reliability by collecting system information and transferring it to an energy management system and associated applications through a seamless end-to-end connection. To achieve interoperability, it is required to exchange the semantic information within the different domains. The international electrotechnical commission has established the Common Information Model (CIM) tool, which is a standard application programming interface for the exchange of semantic information in power systems. CIM provides a robust framework for accurate data sharing, merging, and transformation into reusable information. However, as CIM provides a basic framework for information exchange, various practical issues arise in establishing an energy management system capable of exchanging information using CIM. This paper aims to offer a comprehensive understanding by summarizing and categorizing the research on the practical use of CIM for interoperability in smart grids. Many papers are analyzed and the issues are classified into CIM extension, harmonization, and validation to address the issues that arise when establishing an integrated information exchange system.

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