A Big Geo Data Query Framework to Correlate Open Data with Social Network Geotagged Posts

2017 ◽  
pp. 185-203 ◽  
Author(s):  
Gloria Bordogna ◽  
Steven Capelli ◽  
Giuseppe Psaila
Keyword(s):  
Social Network ◽  
Open Data ◽  
Data Query

scholarly journals Modeling Analytical Streams for Social Business Intelligence

2018 ◽  
Author(s):  
Indira Lanza-Cruz ◽  
Rafael Berlanga ◽  
María José Aramburu
Keyword(s):  
Social Network ◽  
Business Intelligence ◽  
Semantic Representation ◽  
Social Indicators ◽  
Open Data ◽  
Multidimensional Analysis ◽  
Social Business ◽  
Strategic Goals ◽  
Real Scenario ◽  
Definition Of

Social Business Intelligence (SBI) enables companies to capture strategic information from public social networks. Contrary to traditional Business Intelligence (BI), SBI has to face the high dynamicity of both the social network contents and the company analytical requests, as well as the enormous amount of noisy data. Effective exploitation of these continuous sources of data requires efficient processing of the streamed data to be semantically shaped into insightful facts. In this paper, we propose a multidimensional formalism to represent and evaluate social indicators directly from fact streams derived in turn from social network data. This formalism relies on two main aspects: the semantic representation of facts via Linked Open Data and the support of OLAP-like multidimensional analysis models. Contrary to traditional BI formalisms, we start the process by modeling the required social indicators according to the strategic goals of the company. From these specifications, all the required fact streams are modeled and deployed to trace the indicators. The main advantages of this approach are the easy definition of on-demand social indicators, and the treatment of changing dimensions and metrics through streamed facts. We demonstrate its usefulness by introducing a real scenario user case in the automotive sector.

scholarly journals Social network open data revealing for identification of citizens activities on urban environment objects

2021 ◽  
Vol 193 ◽  
pp. 4-12
Author(s):  
Sergey A. Mityagin ◽  
Ilya Yakimuk ◽  
Olga Tikhonova ◽  
Stanislav Sobolevsky
Keyword(s):  
Social Network ◽  
Urban Environment ◽  
Open Data

Semantic network edges: a human-machine approach to represent typed relations in social networks

2015 ◽  
Vol 19 (1) ◽  
pp. 71-81 ◽  
Author(s):  
M. Cristina Pattuelli ◽  
Matthew Miller
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Semantic Network ◽  
Contextual Information ◽  
Open Data ◽  
Entity Recognition ◽  
Semantic Web Technologies ◽  
Data Set ◽  
Content Type ◽  
External Data

Purpose – The purpose of this paper is to describe a novel approach to the development and semantic enhancement of a social network to support the analysis and interpretation of digital oral history data from jazz archives and special collections. Design/methodology/approach – A multi-method approach was applied including automated named entity recognition and extraction to create a social network, and crowdsourcing techniques to semantically enhance the data through the classification of relations and the integration of contextual information. Linked open data standards provided the knowledge representation technique for the data set underlying the network. Findings – The study described here identifies the challenges and opportunities of a combination of a machine and a human-driven approach to the development of social networks from textual documents. The creation, visualization and enrichment of a social network are presented within a real-world scenario. The data set from which the network is based is accessible via an application programming interface and, thus, shareable with the knowledge management community for reuse and mash-ups. Originality/value – This paper presents original methods to address the issue of detecting and representing semantic relationships from text. Another element of novelty is in that it applies semantic web technologies to the construction and enhancement of the network and underlying data set, making the data readable across platforms and linkable with external data sets. This approach has the potential to make social networks dynamic and open to integration with external data sources.

scholarly journals Extraction Patterns to Derive Social Networks from Linked Open Data Using SPARQL

Information ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 361 ◽  
Author(s):  
Raji Ghawi ◽  
Jürgen Pfeffer
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Social Relations ◽  
World Wide ◽  
Open Data ◽  
Linked Open Data ◽  
The World ◽  
Description Framework ◽  
Resource Description

Linked Open Data (LOD) refers to freely available data on the World Wide Web that are typically represented using the Resource Description Framework (RDF) and standards built on it. LOD is an invaluable resource of information due to its richness and openness, which create new opportunities for many areas of application. In this paper, we address the exploitation of LOD by utilizing SPARQL queries in order to extract social networks among entities. This enables the application of de-facto techniques from Social Network Analysis (SNA) to study social relations and interactions among entities, providing deep insights into their latent social structure.

scholarly journals Dealing with Pure New User Cold-Start Problem in Recommendation System Based on Linked Open Data and Social Network Features

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Usha Yadav ◽  
Neelam Duhan ◽  
Komal Kumar Bhatia
Keyword(s):  
Social Network ◽  
Recommendation System ◽  
Research Work ◽  
Recommendation Systems ◽  
Open Data ◽  
Computation Time ◽  
Cold Start ◽  
Linked Open Data ◽  
Hybrid Recommendation ◽  
Cold Start Problem

Preferring accuracy over computation time or vice versa is very challenging in the context of recommendation systems, which encourages many researchers to opt for hybrid recommendation systems. Currently, researchers are trying hard to produce correct and accurate recommendations by suggesting the use of ontology, but the lack of techniques renders to take its full advantage. One of the major issues in recommender systems bothering many researchers is pure new user cold-start problem which arises due to the absence of information in the system about the new user. Linked Open Data (LOD) initiative sets standards for interoperability among cross domains and has gathered enormous amount of data over the past years, which provides various ways by which recommender system’s performance can be improved by enriching user’s profile with relevant features. This research work focuses on solving pure new user cold-start problem by building user’s profile based on LOD, collaborative features, and social network-based features. Here, a new approach is devised to compute item similarity based on ontology, thus predicting the rating of nonrated item. A modified method to calculate user’s similarity based on collaborative features to deal with other issues such as accuracy and computation time is also proposed. The empirical results and comparative analysis of the proposed hybrid recommendation system dictate its better performance specifically for providing solution to pure new user cold-start problem.

Experience of using of sport social network open data for Moscow marathon 2017 results analysis

2019 ◽  
Vol 1 ◽  
pp. 80-88
Author(s):  
Aleksandr V. Melekhov ◽  
◽  
Marya A. Melekhova ◽  
Keyword(s):  
Social Network ◽  
Open Data

scholarly journals Understanding public interest and needs in health policies through the application of social network analysis on a governmental Facebook fan page

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Hsiang-Min Huang ◽  
Ching-Ju Chiu
Keyword(s):  
Health Promotion ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Public Interest ◽  
Open Data ◽  
Physical And Mental Health ◽  
Term Care ◽  
Public Response ◽  
The Social

Abstract Background This study analyzed the interactions between agencies, policies, and the interest of the public using a social network analysis. Methods Open data on the 2017 Facebook fan page of the Ministry of Health and Welfare (MoHW) in Taiwan, including 18,193 messages, were analyzed by conducting a social network analysis, NodeXL (Network Overview, Discovery and Exploration for Excel), creating visualized explorations using size volumes to present the degree of strength between agencies and policies to further calculate the network centrality indicators of agencies and policies. Results Agencies of the “Social and Family Affairs Administration” and “Health Promotion Administration” contributed the most policy posts. The policy of “Physical and mental health promotion” entailed the most agencies to be involved. The “Department of Nursing and Health Care” received the largest public response, for which “Long-term care” received the most public interest. Conclusions A social network analysis of fan page of Taiwan’s top level health government agency can reveal the government’s most emphasized core policies, the strength of correlations between agencies and policies, and provide an understanding of public interest toward the policies.

Sign in / Sign up

Export Citation Format

Share Document