Cumulative centrality index: Centrality measures based ranking technique for molecular chemical structural graphs

Nirmala, P.; Nadarajan, R.

doi:10.1016/j.molstruc.2021.131354

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…Literatürde, çizge teorisinde düğümlerin sıralaması konusunda pek çok çalışma bulunmaktadır [33,26,15,11]. Fakat bilgi çizgesinde sıralamaya yönelik bir araştırmaya bilebildiğimiz kadarıyla rastlanmamıştır.…”

Section: İlgi̇li̇ çAlişmalarunclassified

Novel Extensions to the Knowledge Graph Using the Hashing Mechanism

TAKAN

2023

International Journal of Advances in Engineering and Pure Sciences

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Bilginin doğasına ilişkin, onu şekillendiren çok çeşitli unsurlar bulunmaktadır. Örneğin güvenirlik, tutarlılık, değişmezlik ve bağlam gibi mekanizmalar bunların başında gelir. Ancak söz konusu mekanizmaların bilgi çizgesinde temsil edilmesi oldukça yaygın bir problemdir. Çalışmamızda bu problemin çözümüne katkıda bulunmak amacıyla, bilginin karmaşık doğasına ilişkin güven, tutarlılık, değişmezlik ve bağlam gibi temel mekanizmalar, hashing teknolojisi kullanılarak bilgi çizgesine entegre edilmiştir. Çalışmamızda bu eklentiler, bilgi çizgesinden ayrı tutularak, yapıların işlevselliklerinin bozulmaması sağlanmıştır. Geliştirdiğimiz eklentiler sayesinde bir bilgi değiştiğinde onu etkileyen tüm bilgilerin otomatik güncellenmesi, belirsizlik, bilgiler arasında sıralama yapılamaması, bazı bilgilerin değişmez olarak tutulamaması ve bilgiler arasında hızlı bir karşılaştırmanın yapılamaması gibi yaygın bilgi çizgesi problemleri, örnek senaryolar üzerinden test edilerek çözüme kavuşturulmuştur. Çalışmamızın, bilgi çizgesinin iyileştirilmesine yönelik literatüre ve bilgi çizgesini kullanan yapay zeka yazılımlarının geliştirilmesine katkı sunması beklenmektedir.

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Section: İlgi̇li̇ çAlişmalarunclassified

Novel Extensions to the Knowledge Graph Using the Hashing Mechanism

TAKAN

2023

International Journal of Advances in Engineering and Pure Sciences

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“…In the literature, there are many studies on the ordering of nodes in graph theory ( Sciriha & da Fonseca, 2012 ; Nirmala & Nadarajan, 2022 ; Huang et al, 2021 ; Christoforou et al, 2021 ). However, as far as we know, there needs to be research on ordering in the KG.…”

Section: Related Workmentioning

confidence: 99%

Knowledge graph augmentation: consistency, immutability, reliability, and context

Takan¹

2023

PeerJ Computer Science

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A knowledge graph is convenient for storing knowledge in artificial intelligence applications. On the other hand, it has some shortcomings that need to be improved. These shortcomings can be summarised as the inability to automatically update all the knowledge affecting a piece of knowledge when it changes, ambiguity, inability to sort the knowledge, inability to keep some knowledge immutable, and inability to make a quick comparison between knowledge. In our work, reliability, consistency, immutability, and context mechanisms are integrated into the knowledge graph to solve these deficiencies and improve the knowledge graph’s performance. Hash technology is used in the design of these mechanisms. In addition, the mechanisms we have developed are kept separate from the knowledge graph to ensure that the functionality of the knowledge graph is not impaired. The mechanisms we developed within the scope of the study were tested by comparing them with the traditional knowledge graph. It was shown graphically and with t-test methods that our proposed structures have higher performance in terms of update and comparison. It is expected that the mechanisms we have developed will contribute to improving the performance of artificial intelligence software using knowledge graphs.

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“…Therefore, let's examine global neighborhood overlap measurements [21] right now (continuing our discussion from Section 3). The statistic we'll discuss is known as the Katz index [12], and it counts the number of paths-of various lengths-that can be taken between a particular pair of nodes. We must now resolve two issues in this situation.…”

Section: Globalneighborhoodoverlap Featuresmentioning

confidence: 99%

Machine Learning Methods with Link-Level Features

-¹

2022

IJFMR

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This paper describes graph link level features. We describe three different kinds of them. We discuss distance-based characteristics that consumers might use, such as the shortest path between two nodes, which does not account for neighborhood overlaps. Then we discuss neighborhood overlap metrics like Common neighbors, Jaccard coefficient, and the Adamic-Adar index that measures how many neighbors a pair of nodes share in a fine-grained manner. The issue with this is that the metric will return a value of 0 for nodes that are more than two hops away or that do not have any neighbors. The global neighborhood overlap metrics, for instance, like Katz use the global graph structure to give us a score for a pair of nodes, and the Katz index counts the number of pets of all lands between a pair of nodes where these paths are discounted exponentially with their length.

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Cumulative centrality index: Centrality measures based ranking technique for molecular chemical structural graphs

Cited by 7 publications

References 16 publications

Novel Extensions to the Knowledge Graph Using the Hashing Mechanism

Novel Extensions to the Knowledge Graph Using the Hashing Mechanism

Knowledge graph augmentation: consistency, immutability, reliability, and context

Machine Learning Methods with Link-Level Features

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