User Retrieval of Trademarks System Using Conceptual Similarity Approach

Author Name(s): Sonali Khamkar, Sushma Nandgaonkar
Author Email: khamkarsonali111@gmail.com

Abstract

The trademark is a form of intellectual property. There is a need to protect them. Trademarks are images, texts, slogan, and any domain name. It uniquely distinguishes the services or goods of businesses. There are a number of issues of trademark infringement. The conceptual similarity is also one issue regarding trademark infringement litigation for different trademarks. This paper retrieves the trademark suggestion on the basis of conceptual similarity. Also, the system will be used for logo comparison by using histogram algorithm.

Introduction

Trademarks are important for businesses. The trademarks are in different forms. These are images, slogans, characters or any domain name. The trademarks uniquely distinguish goods or services. When anyone wants to register trademark, it requires to apply for it in a trademark register office. It takes more time to process trademarks while checking for availability. They also do not calculate the logo similarity. So the numbers of trademark infringement cases are due in court. Researchers studied that cases and it developed a system to retrieve similar trademarks [1].

The system proposed deciphers the hypothetical similarities among the trademarks for this purpose. The system is based on text retrieval. This approach uses the trademark retrieval algorithm. It removes stopword and stemming and analyze trademark query which user has entered. It measures the conceptual similarity of trademark. But the system is not given the trademark suggestion. So this system contributes the trademark suggestion to the given trademark with the help of retrieval algorithm. Also the system can be used for finding the logo similarity. The existing system only finds query image similarity partially. This approach solves problem by using histogram algorithm. Because of histogram algorithm calculated logo similarity effectively.

Conclusion

The work is motivated by data similarities increasing of fraud cases, where information retrieval system does not handle this particular issue and trademark similarity. The advantages and limitations of each data similarity of retrieval algorithm are described. The system worked conceptual similarities among trademarks like equal or relevant semantic implant. The natural language processing technology, lexical resources are used to calculate hypothetical similarity between different trademarks. The system gives some suggestion to the user if the entered trademark query is similar to in trademark database and also gives logo comparison ranking results.

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