Similarity Graphs

Knappe, Rasmus; Bulskov, Henrik; Andreasen, Troels

doi:10.1007/978-3-540-39592-8_97

Cited by 15 publications

(10 citation statements)

References 4 publications

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“…In croft research work, the author demonstrates the semantic relationship by means of semantic network (Agosti and Crestani, 1995;Rau, 1987). According to Simone Ponzetto and Michael Strube they created a graph by representing inheritance between the words in Word Net (Knappe et al, 2003;Lopez et al, 2010). But these works require more time and possibility of mismatching, because there is no adequate information in analyzing the meaning of the word used as query.…”

Section: Related Workmentioning

confidence: 99%

An Advanced ontology based automatic approach in improving the similarity by means of combining the sub‐graphs between the information

Sumathy

Chidambaram

2016

Int. j. adv. appl. sci

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In this research an advanced and automatic approach in finding the new products which are similar to the previous one that results in a rapid experience in production of design of manufacturing procedure of the new product has been proposed. The proposed work is based on advanced ontology based semantic model which computes similarity between the subgraphs in an effective manner. It builds a hierarchical structure by means of new similarity index that forms by overlapping sub-graph of existing two product concepts. By means of stored data the similarity measurement is calculated by matching the similar characteristics with the new one that helps in discovering knowledge. The examined result with the real-time data shows minimum computation cost along with high processing speed in similarity according to the global environment. Thus proves the proposed scheme is far better than other existing similarity approaches.

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Section: Related Workmentioning

confidence: 99%

An Advanced ontology based automatic approach in improving the similarity by means of combining the sub‐graphs between the information

Sumathy

Chidambaram

2016

Int. j. adv. appl. sci

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“…The idea of creating a graph that stores the degree of semantic similarity between words or phrases is not new. For example, [13], [25] show how to create a graph that only represents inheritance of words, while [10] approximates the similarity of words based on information about the structure of the graph in which they appear. These papers, however, differ from our approach because we suggest representing available evidence from all type of sources, including natural language descriptions and Wikipedia.…”

Section: Re L At E D Re S E a R C Hmentioning

confidence: 99%

Creating a Phrase Similarity Graph from Wikipedia

Stanchev

2014

2014 IEEE International Conference on Semantic Computing

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The paper addresses the problem of modeling the relationship between phrases in English using a similarity graph. The mathematical model stores data about the strength of the re lationship between phrases expressed as a decimal number. Both structured data from Wikipedia, such as that the Wikipedia page with title "Dog" belongs to the Wikipedia category "Domesticated animals", and textual descriptions, such as that the Wikipedia page with title "Dog" contains the word "wolf" thirty one times are used in creating the graph. The quality of the graph data is validated by comparing the similarity of pairs of phrases using our software that uses the graph with results of studies that were performed with human subjects. To the best of our knowledge, our software produces better correlation with the results of both the Miller and Charles study and the WordSimilarity-353 study than any other published research.

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“…The output of this function is a real number in interval [0, 1] that shows the degree of similarity between two concepts x and y. The zero output means lack of sharing and output one shows complete similarity between two concepts [21][22][23][24][25][26][27].…”

Section: Ontology and Semantic Similarity Functionmentioning

confidence: 99%

A New Method for Resource Discovery in a Grid Resource Service Chain

Barati¹,

Alizadeh²

2014

IJGDC

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A new decentralized method is proposed to resource discovery in Grid computing systems. This agent-based method supports semantic description and is flexible for resource identification and allocation. It allows each of the individual resource agents to interact only with its neighbor agents semantically and form a resource chain for a specific task dynamically. We discuss about the success of probability of our method under different task loading rates and compare it with some other known Algorithms. The experiments show the algorithm could flexibly and dynamically discover resources.

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Similarity Graphs

Cited by 15 publications

References 4 publications

An Advanced ontology based automatic approach in improving the similarity by means of combining the sub‐graphs between the information

An Advanced ontology based automatic approach in improving the similarity by means of combining the sub‐graphs between the information

Creating a Phrase Similarity Graph from Wikipedia

A New Method for Resource Discovery in a Grid Resource Service Chain

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