scholarly journals A Biomedical Knowledge Graph System to Propose Mechanistic Hypotheses for Real-world Environmental Health Observations: Application (Preprint)

10.2196/26714 ◽  
2020 ◽  
Author(s):  
Karamarie Fecho ◽  
Chris Bizon ◽  
Frederick Miller ◽  
Shepherd Schurman ◽  
Charles Schmitt ◽  
...  
Keyword(s):  
Environmental Health ◽  
Real World ◽  
Knowledge Graph ◽  
Biomedical Knowledge ◽  
Graph System

scholarly journals A Biomedical Knowledge Graph System to Propose Mechanistic Hypotheses for Real-world Environmental Health Observations: Application (Preprint)

2020 ◽  
Author(s):  
Karamarie Fecho ◽  
Chris Bizon ◽  
Frederick Miller ◽  
Shepherd Schurman ◽  
Charles Schmitt ◽  
...  
Keyword(s):  
User Interface ◽  
Environmental Health ◽  
Survey Data ◽  
Real World ◽  
Knowledge Graph ◽  
Supporting Evidence ◽  
Biomedical Knowledge ◽  
Environmental Health Sciences ◽  
Immune Mediated ◽  
Answer Sets

BACKGROUND Knowledge graphs are a common form of knowledge representation in biomedicine and many other fields. We developed an open biomedical knowledge graph–based system termed Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways, or ROBOKOP. ROBOKOP consists of both a front-end user interface and a back-end knowledge graph. The ROBOKOP user interface allows users to posit questions and explore answer subgraphs. Users can also posit questions through direct Cypher query of the underlying knowledge graph, which currently contains roughly 6M nodes or biomedical entities and 140M edges or predicates describing the relationship between nodes, drawn from >30 curated data sources. OBJECTIVE We aimed to apply ROBOKOP to survey data on workplace exposures and immune-medicated diseases from the Environmental Polymorphisms Registry (EPR) within the National Institute of Environmental Health Sciences. METHODS We analyzed EPR survey data focused on immune-mediated diseases and identified 45 associations between chemical workplace exposures and immune-mediated diseases, as self-reported by study participants (N = 4574), with 20 associations significant at P < .05 after a false discovery rate connection. We then used ROBOKOP to: (1) validate the associations by determining whether plausible connections exist within the ROBOKOP knowledge graph; and (2) propose biological mechanisms that might explain them and serve as hypotheses for subsequent testing. We highlight three exemplar associations: carbon monoxide – multiple sclerosis; ammonia – asthma; and isopropanol – allergic disease. RESULTS ROBOKOP successfully returned answer sets for three queries that were posed in the context of the driving examples. The answer sets included potential intermediary genes, as well as supporting evidence that might explain the observed associations. CONCLUSIONS We demonstrate a real-world application of ROBOKOP to generate mechanistic hypotheses for associations between chemical workplace exposure and immune-mediates diseases. We expect that ROBOKOP will find broad application across many biomedical fields and other scientific disciplines due to its generalizability, speed to discovery and generation of mechanistic hypotheses, and open nature.

Biomedical Knowledge Graph Embeddings for Personalized Medicine

2021 ◽  
pp. 584-595
Author(s):  
Joana Vilela ◽  
Muhammad Asif ◽  
Ana Rita Marques ◽  
João Xavier Santos ◽  
Célia Rasga ◽  
...  
Keyword(s):  
Personalized Medicine ◽  
Knowledge Graph ◽  
Graph Embeddings ◽  
Biomedical Knowledge

scholarly journals Entity Alignment between Knowledge Graphs Using Attribute Embeddings

2019 ◽  
Vol 33 ◽  
pp. 297-304 ◽  
Author(s):  
Bayu Distiawan Trisedya ◽  
Jianzhong Qi ◽  
Rui Zhang
Keyword(s):  
Real World ◽  
Graph Embedding ◽  
Knowledge Bases ◽  
Knowledge Graph ◽  
World Knowledge ◽  
Large Numbers ◽  
Proposed Model ◽  
Alignment Task ◽  
Transitivity Rule ◽  
Knowledge Graphs

The task of entity alignment between knowledge graphs aims to find entities in two knowledge graphs that represent the same real-world entity. Recently, embedding-based models are proposed for this task. Such models are built on top of a knowledge graph embedding model that learns entity embeddings to capture the semantic similarity between entities in the same knowledge graph. We propose to learn embeddings that can capture the similarity between entities in different knowledge graphs. Our proposed model helps align entities from different knowledge graphs, and hence enables the integration of multiple knowledge graphs. Our model exploits large numbers of attribute triples existing in the knowledge graphs and generates attribute character embeddings. The attribute character embedding shifts the entity embeddings from two knowledge graphs into the same space by computing the similarity between entities based on their attributes. We use a transitivity rule to further enrich the number of attributes of an entity to enhance the attribute character embedding. Experiments using real-world knowledge bases show that our proposed model achieves consistent improvements over the baseline models by over 50% in terms of hits@1 on the entity alignment task.

scholarly journals Construction and applications of knowledge graph of porphyry copper deposits

2021 ◽  
Vol 44 (3) ◽  
pp. 204-218
Author(s):  
Yongzhang Zhou ◽  
Qianlong Zhang ◽  
Wenjie Shen ◽  
Fan Xiao ◽  
Yanlong Zhang ◽  
...  
Keyword(s):  
Porphyry Copper ◽  
Domain Ontology ◽  
Hangzhou Bay ◽  
Knowledge Graph ◽  
Construction Technology ◽  
Porphyry Copper Deposits ◽  
Metallogenic Province ◽  
Copper Deposits ◽  
Graph Language ◽  
Graph System

A knowledge graph is becoming popular due to its ability to describe the real world by using a graph language that can be understood by both humans and machines using computer technologies. A case study to construct the knowledge graph of porphyry copper deposits is presented in this paper. First of all, the raw text data is collected and integrated from selected porphyry copper deposits and porphyry-skarn copper deposits in the Qinzhou Bay – Hangzhou Bay metallogenic belt, South China. Second, the text's entities, relations, and attributes are labeled and extracted with reference to the conceptual model of porphyry copper deposits in the study area. The third, a knowledge graph of porphyry copper deposits, was constructed using Neo4j 4.3. The resulted knowledge graph of porphyry copper deposit has the basic functions of an application. Furthermore, as part of a planned integrated knowledge graph from a single deposit, through an upper-geared metallogenic series, to a high-top metallogenic province, the understanding from the present study may be extended to mineral resource prospectivity and assessment beyond today. The interrelationship between the earth system, the metallogenic system, the exploration system, and the prospectivity and assessment (ES-MS-ES-PS) should be completely understood, and a knowledge graph system for ES-MS-ES-PS is needed. The key scientific and technological problems for achieving the ES-MS-ES-PS knowledge graph system are included in the progressively relative system of the domain ontology and knowledge graph of ES-MS-ES-PS, the automatic construction technology of complicated ESMS-ES-PS domain ontology and knowledge graph, the self-evolution and complementary techniques for multi-modal correlation data embedding in the ES-MS-ES-PS knowledge graph, and the knowledge graph, big data mining and artificial intelligence based on ES-resource prospectivity, and assessment theory, and methods.

scholarly journals A Knowledge Graph System for the Maintenance of Coal Mine Equipment

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Guozhen Zhang ◽  
Xiangang Cao ◽  
Mengyuan Zhang
Keyword(s):  
Knowledge Management ◽  
Coal Mine ◽  
Large Scale ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Knowledge Graph ◽  
Equipment Maintenance ◽  
Data Set ◽  
Named Entity ◽  
Graph System

With the rapid development of coal mine intelligent technology, the complexity of coal mine equipment has been continuously improved and the equipment maintenance resources have been continuously enriched. The traditional coal mine equipment maintenance knowledge management technology can no longer meet the current needs of equipment maintenance knowledge management, and the problems of low utilization rate, poor interoperability, and serious loss of knowledge have gradually emerged. It is urgent to study new knowledge system construction and knowledge management application technology for large-scale coal mine equipment maintenance resources. Knowledge graph is a technical method to describe the relationship between things in the objective world by using a graph model. It can effectively solve the problem of knowledge dynamic mining and management under large-scale data. Therefore, this paper focuses on the establishment of a coal mine equipment maintenance knowledge graph system by using knowledge graph technology. The main research contents are as follows: Firstly, based on the current situation that there is no unified basic knowledge system in the field of coal mine equipment maintenance, this paper establishes the coal mine equipment maintenance ontology (CMEMO) to effectively solve the problem that there are no unified representation, integration, and sharing of coal mine equipment maintenance knowledge in this field and provide support for the construction of coal mine equipment maintenance knowledge graph. Then, aiming at the problem that the traditional named-entity recognition method has a poor recognition effect and relies too much on artificial feature design, this paper proposes a named-entity recognition model for coal mine equipment maintenance based on neural network (BERT-BiLSTM-CRF) and applies the model to the coal mine equipment maintenance data set for verification. The experimental results show that, under the same data set, the entity recognition effect of this model is more leading than that of other models. Finally, through demand analysis and architecture design, combined with the constructed ontology model of coal mine equipment maintenance field, the entity identification of coal mine equipment maintenance is completed based on the BERT-BiLSTM-CRF model and the Django application framework is used to build the coal mine equipment maintenance knowledge graph system to realize the functions of each module of the knowledge graph system.

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