Improve spreading activation algorithm using link assessment between actors from a mobile phone company network based on SMS traffic

2013 ◽  
Author(s):  
Aldo Perinetto ◽  
Wilfrido Inchaustti ◽  
Luca Cernuzzi ◽  
Mario Bort
Keyword(s):  
Mobile Phone ◽  
Spreading Activation ◽  
Spreading Activation Algorithm ◽  
Link Assessment

Bidirectional Spreading Activation Method for Finding Human Diseases Relatedness Using Well-Formed Disease Ontology

2020 ◽  
pp. 1814-1825
Author(s):  
Said Fathalla ◽  
Yaman M. Khalid Kannot
Keyword(s):  
Domain Knowledge ◽  
Human Diseases ◽  
Spreading Activation ◽  
Semantic Matching ◽  
Medical Systems ◽  
Biomedical Knowledge ◽  
Disease Ontology ◽  
Extensive Information ◽  
Spreading Activation Algorithm ◽  
Two Phases

The successful application of semantic web in medical informatics and the fast expanding of biomedical knowledge have prompted to the requirement for a standardized representation of knowledge and an efficient algorithm for querying this extensive information. Spreading activation algorithm is suitable to work on incomplete and large datasets. This article presents a method called SAOO (Spreading Activation over Ontology) which identifies the relatedness between two human diseases by applying spreading activation algorithm based on bidirectional search technique over large disease ontology. The proposed methodology is divided into two phases: Semantic matching and Disease relatedness detection. In Semantic Matching, semantically identify diseases in user's query in the ontology. In the Disease Relatedness Detection, URIs of the diseases are passed to the relatedness detector which returns the set of diseases that may connect them. The proposed method improves the non-semantic medical systems by considering semantic domain knowledge to infer diseases relatedness.

Detecting Human Diseases Relatedness

2018 ◽  
Vol 14 (3) ◽  
pp. 120-133 ◽  
Author(s):  
Said Fathalla
Keyword(s):  
Domain Knowledge ◽  
Web Applications ◽  
Human Diseases ◽  
Spreading Activation ◽  
Semantic Matching ◽  
Search Technique ◽  
Bidirectional Search ◽  
Spreading Activation Algorithm ◽  
Two Phases

Due to the ubiquitous availability of the information on the web, there is a great need for a standardized representation of this information. Therefore, developing an efficient algorithm for retrieving information from knowledge graphs is a key challenge for many semantic web applications. This article presents spreading activation over ontology (SAOO) approach in order to detect the relatedness between two human diseases by applying spreading activation algorithm based on bidirectional search technique. The proposed approach detects two diseases relatedness by considering semantic domain knowledge. The methodology of the proposed work is divided into two phases: Semantic Matching and Diseases Relatedness Detection. In semantic matching, diseases within the user-submitted query are semantically identified in the ontology graph. In diseases relatedness detection, the relatedness between the two diseases is detected by using bidirectional-based spreading activation on the ontology graph. The classification of these diseases is provided as well.

scholarly journals Vectorised Spreading Activation algorithm for centrality measurement

2011 ◽  
Vol 59 (7) ◽  
pp. 469-476 ◽  
Author(s):  
Alexander Troussov ◽  
František Dařena ◽  
Jan Žižka ◽  
Denis Parra ◽  
Peter Brusilovsky
Keyword(s):  
Network Flows ◽  
State Of The Art ◽  
Network Models ◽  
The Other ◽  
Spreading Activation ◽  
Computationally Efficient ◽  
Current State ◽  
Initial Seed ◽  
Spreading Activation Algorithm ◽  
Computationally Efficient Algorithms

Spreading Activation is a family of graph-based algorithms widely used in areas such as information retrieval, epidemic models, and recommender systems. In this paper we introduce a novel Spreading Activation (SA) method that we call Vectorised Spreading Activation (VSA). VSA algorithms, like “traditional” SA algorithms, iteratively propagate the activation from the initially activated set of nodes to the other nodes in a network through outward links. The level of the node’s activation could be used as a centrality measurement in accordance with dynamic model-based view of centrality that focuses on the outcomes for nodes in a network where something is flowing from node to node across the edges. Representing the activation by vectors allows the use of the information about various dimensionalities of the flow and the dynamic of the flow. In this capacity, VSA algorithms can model multitude of complex multidimensional network flows. We present the results of numerical simulations on small synthetic social networks and multi­dimensional network models of folksonomies which show that the results of VSA propagation are more sensitive to the positions of the initial seed and to the community structure of the network than the results produced by traditional SA algorithms. We tentatively conclude that the VSA methods could be instrumental to develop scalable and computationally efficient algorithms which could achieve synergy between computation of centrality indexes with detection of community structures in networks. Based on our preliminary results and on improvements made over previous studies, we foresee advances and applications in the current state of the art of this family of algorithms and their applications to centrality measurement.

Graph-Based Abstractive Summarization

2018 ◽  
pp. 236-261
Author(s):  
Balaji Jagan ◽  
Ranjani Parthasarathi ◽  
Geetha T. V.
Keyword(s):  
Semantic Representation ◽  
Spreading Activation ◽  
Semantic Features ◽  
Statistical Features ◽  
Web Documents ◽  
Semantic Graph ◽  
Spreading Activation Algorithm ◽  
Abstractive Summarization ◽  
Fully Connected ◽  
Selection Of

Customization of information from web documents is an immense job that involves mainly the shortening of original texts. Extractive methods use surface level and statistical features for the selection of important sentences. In contrast, abstractive methods need a formal semantic representation, where the selection of important components and the rephrasing of the selected components are carried out using the semantic features associated with the words as well as the context. In this paper, we propose a semi-supervised bootstrapping approach for the identification of important components for abstractive summarization. The input to the proposed approach is a fully connected semantic graph of a document, where the semantic graphs are constructed for sentences, which are then connected by synonym concepts and co-referring entities to form a complete semantic graph. The direction of the traversal of nodes is determined by a modified spreading activation algorithm, where the importance of the nodes and edges are decided, based on the node and its connected edges under consideration.

Detecting Human Diseases Relatedness

2020 ◽  
pp. 471-486
Author(s):  
Said Fathalla
Keyword(s):  
Domain Knowledge ◽  
Web Applications ◽  
Human Diseases ◽  
Spreading Activation ◽  
Semantic Matching ◽  
Bidirectional Search ◽  
Knowledge Graphs ◽  
Spreading Activation Algorithm ◽  
Two Phases

Due to the ubiquitous availability of the information on the web, there is a great need for a standardized representation of this information. Therefore, developing an efficient algorithm for retrieving information from knowledge graphs is a key challenge for many semantic web applications. This article presents spreading activation over ontology (SAOO) approach in order to detect the relatedness between two human diseases by applying spreading activation algorithm based on bidirectional search technique. The proposed approach detects two diseases relatedness by considering semantic domain knowledge. The methodology of the proposed work is divided into two phases: Semantic Matching and Diseases Relatedness Detection. In semantic matching, diseases within the user-submitted query are semantically identified in the ontology graph. In diseases relatedness detection, the relatedness between the two diseases is detected by using bidirectional-based spreading activation on the ontology graph. The classification of these diseases is provided as well.

scholarly journals Simulating activation propagation in social networks using the graph theory

2010 ◽  
Vol 58 (3) ◽  
pp. 21-28 ◽  
Author(s):  
František Dařena ◽  
Alexander Troussov ◽  
Jan Žižka
Keyword(s):  
Social Networks ◽  
Graph Theory ◽  
Social Network ◽  
Network Formation ◽  
Directed Graphs ◽  
Good Method ◽  
Spreading Activation ◽  
The Social ◽  
Spreading Activation Algorithm ◽  
Social Network Formation

The social-network formation and analysis is nowadays one of objects that are in a focus of intensive research. The objective of the paper is to suggest the perspective of representing social networks as graphs, with the application of the graph theory to problems connected with studying the network-like structures and to study spreading activation algorithm for reasons of analyzing these structures. The paper presents the process of modeling multidimensional networks by means of directed graphs with several characteristics. The paper also demonstrates using Spreading Activation algorithm as a good method for analyzing multidimensional network with the main focus on recommender systems. The experiments showed that the choice of parameters of the algorithm is crucial, that some kind of constraint should be included and that the algorithm is able to provide a stable environment for simulations with networks.

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