Improving the Efficiency of Semantic Based Search

An Effective Approach to Improve the Efficiency of Semantic Based Search

• Merlin Ann Roy

ABSTRACT: The incredible progress in the size of data and with the great growth of amount of web pages, outdated search engines are not suitable and not proper any longer. Search engine is the best significant device to determine any information in World Wide Web. Semantic Search Engine is innate of outdated search engine to solve the above problem. The Semantic Web is a postponement of the existing web where data is given in fixed meaning. Semantic web tools have an vital role in improving web search, because it is functioning to produce machine readable data and semantic web technologies will not exchange traditional search engine

1. Introduction

The keyword search engine does not provide the relevant result because they do not know the meaning of the words and expressions used in the web pages. The incredible progress in the size of data and with the excessive development of amount of web pages, traditional search engines are not suitable and not proper anymore. Search engine is an important tool to determine any information in World Wide Web. The Semantic Web is an postponement of the existing web where data is given in fixed meaning. Semantic web machineries have a vital role in improving web search, because it is functioning to produce machine readable data and semantic web technologies will not exchange traditional search engine. The keyword search engine like Google and yahoo and the semantic search engine like Hakia, DuckDuckGo and Bing are selected to search. While comparing both of the search engines, semantic engine result was shown better than keyword search engine.

Some pages contain hundreds of words just to attract the users. It shows only the advertisement of the page rather than giving the relevant result to the users, If a user gives a keyword in the search engine that itself will suggest for so many pages according to the previous user search. But if the keyword is wrong it does not going to show up anything. This research work proposes a framework to resolve this problem called enhanced skyline sweep algorithm. Algorithm says that even if the particular keyword given by the user is wrong, the search engine is going to give the relevant result to the user

2. Semantic Web Search Engine

The semantic search greatly advances search exactness of the query related data and the search engine provides the exact content, the user intent to know. There’s no rejecting the control and reputation of the Google search engine. By using semantic search engine we will ensure that it results in more relevant and smart results. The search engines are able to compare or extract the data and gives very relevant results for the queries.

A. Approaches to Semantic Web

There are four methods for semantic search. And the method differs that is based on the semantic search engine .First method uses contextual analysis to help to disambiguate queries. Second is reasoning and third is natural language reasoning and the fourth is ontology search

3. Literature Survey

In [1] researchers comparing the performance of different keyword search techniques and there results was not up to the expectation level .The run time performance was poor and the execution times for various search techniques vary for different evaluations

In [2], it explains and proposes an effective move towards keyword query in relational database. Keyword search technique in the web cannot be applied directly to the databases as data which present in the internet are of different forms. That is in databases the information is seen as data tuples and relationships. Researchers proposes a model called semantic graph model consist of database metadata, database values, user terms and their semantic connections

In [3], Systems produce answers quickly for many queries but the other side many others they take a long time or sometimes fail to produce answer after exhausting memory. It conclude that this approach is successful in returning a combination of answers in predictable amount of time

In [4],researchers investigates about the problem that occurs when the user searches for a data base query on a SQL database SQL database suggests so many tuples that satisfies the given query. The problem is when too many tuples are there in the answer. It leads to many-answers problem .They propose a ranking approach for the answers for database queries

In [5], researchers found that the problem for the graphical structured textual data is extracting best answer trees from a data graph. XML and HTML data can be characterized as graphs by using entities as nodes and relationships as edges. To achieve this elasticity, they create a novel search frontier prioritization technique and this technique is centered on spreading activation.

In [6], it proposed a new approach semantic search engine which will answer the intelligent queries and also more efficiently and accurately. They used XML Meta tags to search the information. The XML page contains built in and user defined tags. The proposed approach proves that it takes less time to answer the queries. Using W3C compliant tools helps the system to work on any platform

In [7] it evaluates search performance of various search engine by allowing each query to run in keyword based search engine as well as semantic based search engine. For both keyword-based search engines and the semantic based search engine semantic search engine performance was low

In [8] it presented a generic approach for mapping queries in a user language into an expressive logical language and also presented a particular instantiation of our generic approach which translates keyword queries into DL conjunctive queries using knowledge available in the KB

In [9], Semantic knowledge has repeatedly been engaged to apply relational database reliability. It also proposals the chance to convert a query into a semantically equivalent query which is more efficient .This paper explains a meaning based transformation technique that uses constraints and semantic integrity to reduce the cost of query processing

In [10], In this paper, a survey is done on the web search engine that are developed by different authors and they confirmed that no search engine gives answer properly and seamlessly modern means up-to-date

In [11], This paper, a survey done about the semantic based search engine to extract the gifted features of various semantic search engine and also it says about the explanation of some of the better semantic search engine

In [12], In this paper, a survey is done on the approaches and features of some of the semantic search engines and they give a detail about the various advantages and techniques of some of the best semantic search engine .And the difference between the semantic search engines and traditional search

In [13],the paper says that retrieving relevant information by the search engine is tough. To solve the above problem the semantic search engine plays a vital role in computer system. A survey is done on the generations of search engines and advantages, features of the various search engines and also survey is done on the role in web

In [14], Traditional search engine does not provide the relevant information because it does not know the meaning but the semantic search engine are meaning based search engine and it can overcome the above problem. This paper gives a brief about the traditional search engine and keyword search engine

In [15], the paper says that however a number of techniques have been implemented and proposed those all had a lack of standardization for system evaluation. This paper gives an empirical evaluation of the performance for the relational keyword search systems. They concluded with the results like many existing search technique is not giving a good performance and also discover the relationship between execution time and factors that mottled in earlier calculations

4. Methodology

Fig 1: System Architecture

The above Fig 1 says that when the user gives a particular query in the semantic search engine it will extract the relevant result and gives to the user. If the particular query is wrong the result is not going to show to the user .So the skyline sweep algorithm helps to give the relevant result even if the particular query is wrong by key combination process. In this various enhancements on resource in keyword search is introduce the Skyline sweep is the process of extension has been an active area of research throughout the past decade. Despite a significant number of research papers being published in this area, no research prototypes have transitioned from proof-of-concept implementations into deployed systems. The lack of technology transfer coupled with discrepancies among existing evaluations indicates a need for a thorough, independent empirical evaluation of proposed search techniques. Two data sets IMDb and Wikipedia contain the full text of articles, which emphasizes sophisticated ranking schemes for results. Our data sets roughly span the range of data set sizes that have been used in other evaluations even though our IMDb and Wikipedia data sets are both subsets of original databases. Using a database subset likely overstates the efficiency and effectiveness of evaluated search techniques.

A. USER INTERFACE

To connect with server user must give their username and password then only they can able to connect the server. If the user already exits directly can login into the server else user must register their details such as username, password and Email id, into the server. Server will create the account for the entire user to maintain upload and download rate. Name will be set as user id. . Logging in is usually used to enter a specific page.

Example: Create node and set name, port for that node. Nodes are created and displayed.

B. ADMIN MODULE:

Admin maintain the user information. And he can upload the file to search the user. The file uploaded completed then only the user can able to search the file what we are want. And then admin can check the user information. Suppose here one file is searched that related all information is stored into admin. Searching information mean when the user searched the file and timing everything stored in admin. Finally admin check what file we are uploaded.

Example: Admin upload the files into database .And then check the uploaded files.

C. Query processing

Query processing means what we are searching that is passed by query. Admin uploaded all files are stored in database. User search in database where is available the requested keyword. Suppose the requested file is available in database that is passed to user. Suppose the user give one keyword depends upon the keyword all related lines are displayed. In that line from user get what are the data we need. This file searching and execution details is stored in data base. When ever need this we can able to view this details.

Example: User searches the Query (keyword) in database. User gets that query related output.

D. Recommended module

Recommended module meant suppose now we give any keyword wrongly that word automatically going to mapped correct keyword. And then displayed what are the keyword mapped related that word. Suppose we give any wrong keyword that related all correct word going to mapped and displayed. Here we used “Skyline sweep algorithm” for automatically checked that correct keyword.

Example: The user gives the wrong query. Key combination

Will give the correct output

E. Top ranking

File ranking can be viewed by the chart. Top rank meant most of the files viewed by user that is called top ranked. That files are come in first. After then only comes the user searching keyword. So now we can easily understand which files are mostly viewed by user. That ranking is displayed in chart.

Example: User searches the keyword. The keyword already viewed by user, that keyword displayed in first.

6. Advantages

1. Reduce Time consumption during retrieval.
2. Efficient to search an data in various search engines.
3. Easy to execute in realistic manner in proposed system

7. Conclusion and Future Work

It is concluded that searching the internet today is a encounter and it is projected that approximately partial of the complex questions go unanswered .Semantic search has the power to enhance the traditional web search. Whether a search engine can meet all these conditions still remain a question .We proposed a framework using enhanced skyline sweep algorithm to overcome this problem. In which the process can be done by the favor a realistic query workload instead of a larger workload with queries that are unlikely to be representative in various resource that can being with experimental results do not reflect well on existing relational keyword search techniques. Runtime performance is unacceptable for most search techniques. Memory consumption is also excessive for many search techniques in our experimental results, question to the scalability and improvements claimed by previous evaluations so we will prefer the consumption on runtime of searching an data in upcoming technologies.

8. References

[1] J. Coffman and A.C. Weaver, “An Empirical Performance Evaluation of Relational Keyword Search Systems,” Technical Report CS-2011-07, Univ. of Virginia, IEEE transaction on knowledge and data engineering, Vol. 26, No. 1,January 2014

[2] Jarunee Saelee, Veera Boonjing,” A metadata data search approach to keyword query in relational databases, International Journal of Computer Applications pp. 140-149, May 2013

[3] A. Baid, I. Rae, J. Li, A. Doan, and J. Naughton, “Toward Scalable Keyword Search over Relational Data,” University of Wisconsin, Madison fbaid, ian, jxli, anhai, naughtong@cs.wisc.edu

[4] F. Surajit Chaudhuri, Gautam Das, “Probabilistic Ranking of Database Query Results,” Microsoft Research One Microsoft Way Redmond, WA 98053 USA {surajitc, gautamd}@microsoft.com

[5] V. Kacholia, S. Pandit, S. Chakrabarti, S. Sudarshan, R. Desai, and H. Karambelkar, “Bidirectional Expansion for Keyword Search on Graph Databases,” Indian Institute of Technology, Bombay varunk@acm.org shashank+@cs.cmu.edu

{Soumen, Sudarsha, hrishi}@cse.iitb.ac.in rushi@desai.name

[6] Ritu Khatri, Kanwalvir Singh Dhindsa, Vishal Khatri “Investigation and Approach Of New Analysis Of Intelligent Semantic Web Search Engine” International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-1, Issue-1, April 2012

[7] Duygu Tümer, Mohammad Ahmed Shah, Yıltan Bitirim “An Empirical Evaluation on Semantic Search Performance of Keyword-Based and Semantic Search Engines: Google, Yahoo, Msn and Hakia” Fourth International Conference on Internet Monitoring and Protection, 2009

[8] Thanh Tran, Philipp Cimiano, Sebastian Rudolph and Rudi Studer “Ontology-based Interpretation of Keywords for Semantic Search” Institute AIFB, University ät Karlsruhe, Germany

[9] W. David Haseman, University of Wisconsin-Milwaukee, daveh@uwm.edu Tung-Ching Lin, Nationa Sun Yat-Sen University, Taiwan, tclin@mis.nsysu.edu.tw Derek L. Nazareth, University of Wisconsin-Milwaukee, derek@uwm.edu “An Intelligent Approach to Semantic Query Processing”

[10] S. Latha Shanmuga Vadivu1, M. Rajaram2, and S. N. Sivanandam3 “A Survey on Semantic Web Mining Based Web Search Engines”ARPN Journal of Engineering and Applied Sciences VOL. 6, NO. 10, OCTOBER 2011

[11] Anusree.ramachandran, R.Sujatha School of Information Technology and Engineering, VIT University” Semantic search engine: A survey” Int. J. Comp. Tech. Appl., Vol 2 (6), 1806-1811

[12] G .Sudeepthi1 , G. Anuradha ,Prof. M.Surendra Prasad Babu “A Survey on Semantic Web Search Engine” IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 2, No 1, March 2012

[13] G.Madhu1 and Dr.A.Govardhan2 Dr.T.V.Rajinikanth3 “Intelligent Semantic Web Search Engines: A Brief Survey” International journal of Web & Semantic Technology (IJWesT) Vol.2, No.1, January 2011

[14] Junaidah Mohamed Kassim and Mahathir Rahmany Introduction to Semantic Search Engine 2009 International Conference on Electrical Engineering and Informatics5-7 August 2009, Selangor, Malaysia

[15] Joel Coffman, Alfred C. Weaver “An Empirical Performance Evaluation of Relational Keyword Search Systems” Department of Computer Science, University of Virginia Charlottesville, VA, USA