Distributed dynamic semantic query and reasoning for graph data

2021 ◽  
Author(s):  
Xingyu Peng ◽  
Ying Pan
Keyword(s):  
Dynamic Semantic ◽  
Graph Data ◽  
Semantic Query

Getting to know a new protocol in hypertension care

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Mona Lundin
Keyword(s):  
Blood Pressure ◽  
Digital Technology ◽  
Interaction Analysis ◽  
Medical Discourse ◽  
Graphical Form ◽  
Graph Data ◽  
Average Blood Pressure ◽  
Infrastructure Design

This study explores the use of a new protocol in hypertension care, in which continuous patient-generated data reported through digital technology are presented in graphical form and discussed in follow-up consultations with nurses. This protocol is part of an infrastructure design project in which patients and medical professionals are co-designers. The approach used for the study was interaction analysis, which rendered possible detailed in situ examination of local variations in how nurses relate to the protocol. The findings show three distinct engagements: (1) teasing out an average blood pressure, (2) working around the protocol and graph data and (3) delivering an analysis. It was discovered that the graphical representations structured the consultations to a great extent, and that nurses mostly referred to graphs that showed blood pressure values, which is a measurement central to the medical discourse of hypertension. However, it was also found that analysis of the data alone was not sufficient to engage patients: nurses' invisible and inclusion work through eliciting patients' narratives played an important role here. A conclusion of the study is that nurses and patients both need to be more thoroughly introduced to using protocols based on graphs for more productive consultations to be established. 

Automatic Ontology Learning from Multiple Knowledge Sources of Text

2018 ◽  
Vol 14 (2) ◽  
pp. 1-21 ◽  
Author(s):  
B Sathiya ◽  
T.V. Geetha
Keyword(s):  
Knowledge Base ◽  
Web Pages ◽  
Knowledge Sources ◽  
Statistical Measure ◽  
Ontology Learning ◽  
Web Page ◽  
Semantic Query ◽  
Probabilistic Knowledge ◽  
Discovery Algorithms ◽  
Different Sources

The prime textual sources used for ontology learning are a domain corpus and dynamic large text from web pages. The first source is limited and possibly outdated, while the second is uncertain. To overcome these shortcomings, a novel ontology learning methodology is proposed to utilize the different sources of text such as a corpus, web pages and the massive probabilistic knowledge base, Probase, for an effective automated construction of ontology. Specifically, to discover taxonomical relations among the concept of the ontology, a new web page based two-level semantic query formation methodology using the lexical syntactic patterns (LSP) and a novel scoring measure: Fitness built on Probase are proposed. Also, a syntactic and statistical measure called COS (Co-occurrence Strength) scoring, and Domain and Range-NTRD (Non-Taxonomical Relation Discovery) algorithms are proposed to accurately identify non-taxonomical relations(NTR) among concepts, using evidence from the corpus and web pages.

Multi-Fuzzy-Objective Graph Pattern Matching with Big Graph Data

2019 ◽  
Vol 30 (4) ◽  
pp. 24-40
Author(s):  
Lei Li ◽  
Fang Zhang ◽  
Guanfeng Liu
Keyword(s):  
Pattern Matching ◽  
Particle Swarm Optimization Algorithm ◽  
Nsga Ii ◽  
Specific Domain ◽  
Graph Pattern Matching ◽  
Graph Data ◽  
Graph Pattern ◽  
Big Graph ◽  
Complex Relationships ◽  
Pattern Graph

Big graph data is different from traditional data and they usually contain complex relationships and multiple attributes. With the help of graph pattern matching, a pattern graph can be designed, satisfying special personal requirements and locate the subgraphs which match the required pattern. Then, how to locate a graph pattern with better attribute values in the big graph effectively and efficiently becomes a key problem to analyze and deal with big graph data, especially for a specific domain. This article introduces fuzziness into graph pattern matching. Then, a genetic algorithm, specifically an NSGA-II algorithm, and a particle swarm optimization algorithm are adopted for multi-fuzzy-objective optimization. Experimental results show that the proposed approaches outperform the existing approaches effectively.

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