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Iterative Knowledge Extraction from Social Networks

Marco Brambilla, Stefano Ceri, Florian Daniel, Marco Di Giovanni, Andrea Mauri, Giorgia Ramponi
2018 Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18  
We propose a method for discovering emerging knowledge by extracting it from social content.  ...  Our method can run continuously or with periodic iterations, using the results as new seeds.  ...  In this paper we propose an iterative knowledge extraction method for discovering knowledge by extracting it from social content.  ... 
doi:10.1145/3184558.3191578 dblp:conf/www/0001CDG0R18 fatcat:h2spqp6j55cixmog6ugfivnbwa

D-MFCLMin: A New Algorithm for Extracting Frequent Conceptual Links from Social Networks

Hamid Tabatabaee
2017 International Journal of Advanced Computer Science and Applications  
Massive amounts of data in social networks have made researchers look for ways to display a summary of the information provided and extract knowledge from them.  ...  One of the new approaches to describe knowledge of the social network is through a concise structure called conceptual view.  ...  RELATED WORK Popular approaches of mining social networks have been proposed to extract different forms of knowledge from these networks.  ... 
doi:10.14569/ijacsa.2017.081240 fatcat:34uhjl2h3zaidmmnlsuq5lu2ma

Extracting Actionable Knowledge from Social Networks using Structural Features

Nasrin Kalanat, Eynollah Khanjari, Alireza Khanshan
2020 IEEE Access  
Actionable knowledge discovery is a field of study specifically developed for this matter. Existing methods rarely tackled the problem of extracting actionable knowledge from social networks.  ...  INDEX TERMS Social networks mining, action mining, actionable knowledge discovery, structural features, change propagation, change-awareness.  ...  knowledge from social networks.  ... 
doi:10.1109/access.2020.2983146 fatcat:pzmygpvgbrcedi3utq72csb6vm

Integrative visual analytics for suspicious behavior detection

Peter Bak, Christian Rohrdantz, Svenja Leifert, Christoph Granacher, Stefan Koch, Simon Butscher, Patrick Jungk, Daniel A. Keim
2009 2009 IEEE Symposium on Visual Analytics Science and Technology  
In the VAST Challenge 2009 suspicious behavior had to be detected applying visual analytics to heterogeneous data, such as network traffic, social network enriched with geo-spatial attributes, and finally  ...  This paper describes some of the awarded parts from our solution entry.  ...  Figure 2 : 2 Analytic process of investigating social network data. The iterative loop of information extraction enabled the user to guide the knowledge discovery process.  ... 
doi:10.1109/vast.2009.5334430 dblp:conf/ieeevast/BakRLGKBJK09 fatcat:7ov7upesm5bjbo464lkivzlpmu

Multi-Modal Bayesian Embeddings for Learning Social Knowledge Graphs [article]

Zhilin Yang, Jie Tang, William Cohen
2016 arXiv   pre-print
We study the extent to which online social networks can be connected to open knowledge bases. The problem is referred to as learning social knowledge graphs.  ...  ., social network users and knowledge concepts---in a shared latent topic space. Experiments on three datasets show that the proposed method clearly outperforms state-of-the-art methods.  ...  CountKG extracts knowledge concepts from social text D by referring to the knowledge concept set V k , and ranks the concepts by appearance frequency.  ... 
arXiv:1508.00715v2 fatcat:i7n473jgb5btrm4iqjcn5omsia

Who With Whom And How?

Stefan Siersdorfer, Philipp Kemkes, Hanno Ackermann, Sergej Zerr
2015 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management - CIKM '15  
However, existing approaches for extracting social networks from unstructured Web content do not scale well and are only feasible for small graphs.  ...  In this paper, we introduce novel methodologies for query-based search engine mining, enabling efficient extraction of social networks from large amounts of Web data.  ...  RELATED WORK There is a plethora of work on social network extraction from text and visual data.  ... 
doi:10.1145/2806416.2806582 dblp:conf/cikm/SiersdorferKAZ15 fatcat:3s7nr6zaxrc4bkzc6d23jnb5zi

MENTAL HEALTH ASSESSMENT OF SOCIAL NETWORK USERS BASED ON CONVOLUTIONAL NEURAL NETWORK

2020 REVISTA ARGENTINA DE CLINICA PSICOLOGICA  
The research results reflect the value of psychological knowledge in feature extraction through deep learning.  ...  In the age of the Internet, more and more users mention their mental health problems anonymously on social network sites.  ...  This research is also supported by the Humanity and Social Science Youth foundation of Ministry of Education of China (grant no. 15YJC860001), National Statistical Science Research Project (grant no. 2017LZ38  ... 
doi:10.24205/03276716.2020.400 fatcat:dl34qn62ijarjpd73rrfait6vm

BiNet: Trust Sub-network Extraction Using Binary Ant Colony Algorithm in Contextual Social Networks

Xiaoming Zheng, Yan Wang, Mehmet A. Orgun
2015 2015 IEEE International Conference on Web Services  
However, predicting the trust from a source participant to a target one based on the whole social network is not really feasible.  ...  Online Social Networks (OSNs) have become an integral part of daily life in recent years.  ...  [15] propose a social contextaware trust network extraction model, which applies an optimized Monte Carlo method to extract an optimal trust network from the source to the target participants, under  ... 
doi:10.1109/icws.2015.51 dblp:conf/icws/ZhengWO15 fatcat:yzejuhgmt5ezpf5gxlbpktylsq

Email Social Network Extraction and Search

Michal Laclavik, Štefan Dlugolinsky, Marcel Kvassay, Ladislav Hluchy
2011 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology  
The article discusses our email search prototype, which exploits social networks hidden in email archives and the spread of activation algorithm.  ...  The prototype offers new way of searching email archives as knowledge repository. The prototype was partially evaluated on the Enron email corpus.  ...  Extracting social networks and contact information from emails and the Web and combining this information is discussed in [2] .  ... 
doi:10.1109/wi-iat.2011.30 dblp:conf/iat/LaclavikDKH11 fatcat:h4ctwbivorg35ftpeiaqlak7ui

Knowledge Graph Semantic Enhancement of Input Data for Improving AI

Shreyansh Bhatt, Amit Sheth, Valerie Shalin, Jinjin Zhao, Amit Sheth
2020 IEEE Internet Computing  
The term Knowledge Graph (KG) is in vogue as for many practical applications, it is convenient and useful to organize this background knowledge in the form of a graph.  ...  Background knowledge provides complementary, real world factual information that can augment the limited labeled data to train a machine learning algorithm.  ...  They used iterative optimization over an attributed social network graph and a hierarchical KG to detect and characterize communities.  ... 
doi:10.1109/mic.2020.2979620 fatcat:q4xrsmddnfbvzjhev4xqknfo64

Descriptive Modeling of Social Networks

Erick Stattner, Martine Collard
2015 Procedia Computer Science  
These last years, many analysis methods have been proposed to extract knowledge from social networks.  ...  In this paper, we review the main descriptive modelling methods of social networks and show for each of them the resulting useful knowledge on a running example.  ...  Thus, numerous social network mining methods have been proposed for extracting various kinds of knowledge from social networks.  ... 
doi:10.1016/j.procs.2015.05.505 fatcat:a5bpxvryofcdvp4gq45gkc6xom

Correlation between the Dissemination of Classic English Literary Works and Cultural Cognition in the New Media Era

Weiwei Guo, Qiangyi Li
2022 Advances in Multimedia  
Feature: in terms of named entity recognition, based on the existing iterative atrous convolutional network model, an iterative atrous convolutional network model is proposed.  ...  From the current point of view, "literary works," as the spiritual food of contemporary people, are promoting social spirit.  ...  from text, which is the most critical part of knowledge extraction. (2) Relation extraction: relation extraction means that, after obtaining entities, the relationship between entities needs to be extracted  ... 
doi:10.1155/2022/3616432 fatcat:iyhwwr2sgbhmlna7nwbdjx6up4

A survey on text mining in social networks

Rizwana Irfan, Christine K. King, Daniel Grages, Sam Ewen, Samee U. Khan, Sajjad A. Madani, Joanna Kolodziej, Lizhe Wang, Dan Chen, Ammar Rayes, Nikolaos Tziritas, Cheng-Zhong Xu (+3 others)
2015 Knowledge engineering review (Print)  
In this survey, we review different text mining techniques to discover various textual patterns from the social networking sites.  ...  Social network applications create opportunities to establish interaction among people leading to mutual learning and sharing of valuable knowledge, such as chat, comments, and discussion boards.  ...  Text mining is a knowledge discovery process used to extract interesting and non-trivial patterns from natural language (Sorensen, 2009) .  ... 
doi:10.1017/s0269888914000277 fatcat:uzcny5uh2jduzjhv6rmxg2niuu

On utilising social networks to discover representatives of human communities

Przemysław Kazienko, Katarzyna Musial
2007 International Journal of Intelligent Information and Database Systems  
They can be either directly maintained by computer systems like Frindster, LinkedIn or extracted from data about user activities in the communication network like e-mails, chats, blogs, homepages connected  ...  Social network analysis can be applied to virtual communities and can deliver interesting knowledge about particular network members as well as cohesive subgroups.  ...  The social network analysis supported by computer science gives opportunity to develop other branches of knowledge.  ... 
doi:10.1504/ijiids.2007.016682 fatcat:qfi7kcyb3zcbbmegwfcxtzbbre

Label-dependent node classification in the network

Przemyslaw Kazienko, Tomasz Kajdanowicz
2012 Neurocomputing  
Therefore, there is a need for accurate and efficient algorithms that are able to perform good classification based only on scanty knowledge of network nodes.  ...  by networks.  ...  Features extraction Feature extraction from networks is a general term for the methods of constructing variables from the structure of the graph.  ... 
doi:10.1016/j.neucom.2011.04.047 fatcat:2c6p7n6tzngwlac3jjq2c5tepu
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