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.

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

2017 ◽  
Vol 2 (1) ◽  
pp. 45-58 ◽  
Author(s):  
Said Fathalla ◽  
Yaman M. Khalid Kannot
Keyword(s):  
Domain Knowledge ◽  
Human Diseases ◽  
Spreading Activation ◽  
Semantic Matching ◽  
Medical Systems ◽  
Biomedical Knowledge ◽  
Disease Ontology ◽  
Bidirectional Search ◽  
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.

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.

Exploring Diseases Relationships

2019 ◽  
pp. 133-159
Author(s):  
Said Fathalla ◽  
Heba Mohamed ◽  
Yaman Kannot
Keyword(s):  
Domain Knowledge ◽  
Word Sense Disambiguation ◽  
Spreading Activation ◽  
Use Case ◽  
Semantic Matching ◽  
Word Sense ◽  
Matching Process ◽  
Sense Disambiguation ◽  
Two Phases ◽  
The Relationship

Developing an efficient algorithm for traversing large ontologies is a key challenge for many semantic-based applications. This chapter introduces an approach, spreading activation over ontology (SAOO), to explore the relationship between two human diseases using an ontology-based spreading activation approach. SAOO comprises two phases: semantic matching and diseases relatedness detection. In the semantic matching phase, user-submitted diseases are semantically identified in the ontology graph using the proposed matching algorithm. Semantic matching conducts more analysis in the matching process, which comprises term normalization; phrase analysis, and word sense disambiguation. In the diseases relatedness detection phase, the URIs of these diseases are passed to the relatedness detector to detect the relationship connecting them. SAOO improves healthcare systems by considering semantic domain knowledge and a set of SWRL rules to infer diseases relatedness. We present a use case that outlines how SAOO can be used to explore relationships between vaccines in the vaccine ontology.

scholarly journals Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data

2014 ◽  
Vol 43 (D1) ◽  
pp. D1071-D1078 ◽  
Author(s):  
Warren A. Kibbe ◽  
Cesar Arze ◽  
Victor Felix ◽  
Elvira Mitraka ◽  
Evan Bolton ◽  
...  
Keyword(s):  
Human Diseases ◽  
Biomedical Knowledge ◽  
Disease Ontology

scholarly journals Turning Informal Thesauri into Formal Ontologies: A Feasibility Study on Biomedical Knowledge Re-Use

2003 ◽  
Vol 4 (1) ◽  
pp. 94-97 ◽  
Author(s):  
Udo Hahn
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
Description Logics ◽  
Biomedical Domain ◽  
Biomedical Knowledge ◽  
Knowledge Conversion ◽  
Formal Knowledge ◽  
Knowledge Repositories ◽  
Formal Ontologies ◽  
Rigorous Description

This paper reports a large-scale knowledge conversion and curation experiment. Biomedical domain knowledge from a semantically weak and shallow terminological resource, the UMLS, is transformed into a rigorous description logics format. This way, the broad coverage of the UMLS is combined with inference mechanisms for consistency and cycle checking. They are the key to proper cleansing of the knowledge directly imported from the UMLS, as well as subsequent updating, maintenance and refinement of large knowledge repositories. The emerging biomedical knowledge base currently comprises more than 240 000 conceptual entities and hence constitutes one of the largest formal knowledge repositories ever built.

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.

Telemedicine and Biotelemetry for E-Health Systems

2011 ◽  
pp. 676-692
Author(s):  
Elif Derya Übeyli
Keyword(s):  
Knowledge Management ◽  
Health Systems ◽  
Information Technologies ◽  
Clinical Care ◽  
Clinical Information ◽  
Medical Systems ◽  
Biomedical Knowledge ◽  
Physiological Variables ◽  
Medical Sector ◽  
Transmission Of Information

This chapter develops an integrated view of telemedicine and biotelemetry applications. The objective of the chapter is coherent with the objective of the book, which includes techniques in the biomedical knowledge management. Telemedicine is the use of modern telecommunications and information technologies for the provision of clinical care to individuals at a distance and the transmission of information to provide that care. The medical systems infrastructure underpinning this form of medicine, consisting of the equipment and processes used to acquire and present clinical information and to store and retrieve data are explained in detail. An investigation of telemedicine applications in various fields is presented and the likely enormous impact of telemedicine systems on the future of medicine is discussed. For example, bioelectric and physiological variables could be measured by biotelemetry systems. Developing a biotelemetry system and the principal operation of such a system are presented, and its components and the telemetry types are explained. The author suggests that the content of the chapter will assist the medical sector and the general reader in gaining a better understanding of the techniques in the telemedicine and biotelemetry applications.

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.

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