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.

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.

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 Interactive medical word sense disambiguation through informed learning

2018 ◽  
Vol 25 (7) ◽  
pp. 800-808 ◽  
Author(s):  
Yue Wang ◽  
Kai Zheng ◽  
Hua Xu ◽  
Qiaozhu Mei
Keyword(s):  
Active Learning ◽  
Learning Curve ◽  
Domain Knowledge ◽  
Learning Algorithm ◽  
Interactive Learning ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
High Quality ◽  
Clinical Notes ◽  
Sense Disambiguation

Abstract Objective Medical word sense disambiguation (WSD) is challenging and often requires significant training with data labeled by domain experts. This work aims to develop an interactive learning algorithm that makes efficient use of expert’s domain knowledge in building high-quality medical WSD models with minimal human effort. Methods We developed an interactive learning algorithm with expert labeling instances and features. An expert can provide supervision in 3 ways: labeling instances, specifying indicative words of a sense, and highlighting supporting evidence in a labeled instance. The algorithm learns from these labels and iteratively selects the most informative instances to ask for future labels. Our evaluation used 3 WSD corpora: 198 ambiguous terms from Medical Subject Headings (MSH) as MEDLINE indexing terms, 74 ambiguous abbreviations in clinical notes from the University of Minnesota (UMN), and 24 ambiguous abbreviations in clinical notes from Vanderbilt University Hospital (VUH). For each ambiguous term and each learning algorithm, a learning curve that plots the accuracy on the test set against the number of labeled instances was generated. The area under the learning curve was used as the primary evaluation metric. Results Our interactive learning algorithm significantly outperformed active learning, the previous fastest learning algorithm for medical WSD. Compared to active learning, it achieved 90% accuracy for the MSH corpus with 42% less labeling effort, 35% less labeling effort for the UMN corpus, and 16% less labeling effort for the VUH corpus. Conclusions High-quality WSD models can be efficiently trained with minimal supervision by inviting experts to label informative instances and provide domain knowledge through labeling/highlighting contextual features.

scholarly journals Numerical Simulation of Ambiguity Resolution in Multiple Information Streams Based on Network Machine Translation

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Lei Wang ◽  
Qun Ai
Keyword(s):  
Mutual Information ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
Word Sense Disambiguation ◽  
Word Segmentation ◽  
Word Sense ◽  
Test Corpus ◽  
Sense Disambiguation ◽  
Professional Fields

In natural language, the phenomenon of polysemy is widespread, which makes it very difficult for machines to process natural language. Word sense disambiguation is a key issue in the field of natural language processing. This paper introduces the more common statistical learning methods used in the field of word sense disambiguation. Using the naive Bayesian machine learning method and the feature vector set extracted and constructed by the Dice coefficient method, a semantic word disambiguation model based on semantics is realized. The results of comparative experiments show that the proposed method is better compared with known systems. This paper proposes a method for disambiguation of word segmentation in professional fields based on unsupervised learning. This method does not rely on professional domain knowledge and training corpus and only uses the frequency, mutual information, and boundary entropy information of the string in the test corpus to solve the problem of word segmentation ambiguity. The experimental results show that these three evaluation standards can solve the problem of word segmentation ambiguity in professional fields and improve the effect of word segmentation. Among them, the segmentation result using mutual information is the best, and the performance is stable.

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.

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