Rough Set Based Aggregation for Effective Evaluation of Web Search Systems

Handbook of Research on Industrial Informatics and Manufacturing Intelligence - Advances in Civil and Industrial Engineering ◽

10.4018/978-1-4666-0294-6.ch008 ◽

2012 ◽

pp. 193-210

Author(s):

Rashid Ali ◽

M. M. Sufyan Beg

Keyword(s):

Set Theory ◽

Rough Set ◽

Search Engines ◽

Web Search ◽

Rough Set Theory ◽

User Feedback ◽

Rank Aggregation ◽

Experimental Results ◽

Industrial Environment ◽

Aggregation Technique

Rank aggregation is the process of generating a single aggregated ranking for a given set of rankings. In industrial environment, there are many applications where rank aggregation must be applied. Rough set based rank aggregation is a user feedback based technique which mines ranking rules for rank aggregation using rough set theory. In this chapter, the authors discuss rough set based rank aggregation technique in light of Web search evaluation. Since there are many search engines available, which can be used by used by industrial houses to advertise their products, Web search evaluation is essential to decide which search engines to rely on. Here, the authors discuss the limitations of rough set based rank aggregation and present an improved version of the same, which is more suitable for aggregation of different techniques for Web search evaluation. In the improved version, the authors incorporate the confidence of the rules in predicting a class for a given set of data. They validate the mined ranking rules by comparing the predicted user feedback based ranking with the actual user feedback based ranking. They show their experimental results pertaining to the evaluation of seven public search engines using improved version of rough set based aggregation for a set of 37 queries.

Download Full-text

Semantic Search Exploiting Formal Concept Analysis, Rough Sets, and Wikipedia

Natural Language Processing ◽

10.4018/978-1-7998-0951-7.ch069 ◽

2020 ◽

pp. 1436-1458

Author(s):

Yuncheng Jiang ◽

Mingxuan Yang

Keyword(s):

Set Theory ◽

Rough Set ◽

Formal Concept Analysis ◽

Web Search ◽

Rough Set Theory ◽

Concept Analysis ◽

Semantic Search ◽

Formal Concept ◽

Importance Ranking ◽

Keyword Searches

This article describes how the traditional web search is essentially based on a combination of textual keyword searches with an importance ranking of the documents depending on the link structure of the web. However, one of the dimensions that has not been captured to its full extent is that of semantics. Currently, combining search and semantics gives birth to the idea of the semantic search. The purpose of this article is to present some new methods to semantic search to solve some shortcomings of existing approaches. Concretely, the authors propose two novel methods to semantic search by combining formal concept analysis, rough set theory, and similarity reasoning. In particular, the authors use Wikipedia to compute the similarity of concepts (i.e., keywords). The experimental results show that the authors' proposals perform better than some of the most representative similarity search methods and sustain the intuitions with respect to human judgements.

Download Full-text

A Generalized Attributes Significance Measure Method

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.1551 ◽

2014 ◽

Vol 989-994 ◽

pp. 1551-1554

Author(s):

Fa Chao Li ◽

Ming Li ◽

Shuo Liu

Keyword(s):

Set Theory ◽

Rough Set ◽

Rough Set Theory ◽

Experimental Results ◽

Core Content ◽

Measure Methods ◽

The Difference

Currently, rough set theory has been widely used in many fields. In rough set theory, how to measure the attributes significance of the data is a core content. In order to solve the problem that the existing attributes significance measure methods usually ignore the interaction among the attributes, the paper presents a measure method based on difference degree. When given a set, the proposed method first divides it into several subsets according to the value of condition attributes, and then computes the difference degree in the subsets. Secondly, the important attributes are selected based on the value of difference degree. Further the paper discussed some properties of the difference degree, and the experimental results shows the effectiveness of this method in the final.

Download Full-text

Enriching Arabic Tweets Representation based on Web Search Engine and the Rough Set Theory

Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015 - ASONAM '15 ◽

10.1145/2808797.2809339 ◽

2015 ◽

Cited By ~ 2

Author(s):

Mohammed Bekkali ◽

Issam Sahmoudi ◽

Abdelmonaime Lachkar

Keyword(s):

Search Engine ◽

Set Theory ◽

Rough Set ◽

Web Search ◽

Rough Set Theory ◽

Web Search Engine

Download Full-text

Myriad- a Novel User Feedback Based Metasearch Engine

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.7554 ◽

2012 ◽

Vol 433-440 ◽

pp. 7554-7562

Author(s):

Rashid Ali ◽

Anjali Saxena ◽

Richa Gupta ◽

M. M. Sufyan Beg

Keyword(s):

Rough Set ◽

Search Engines ◽

Rule Learning ◽

User Feedback ◽

Rank Aggregation ◽

Information Need ◽

Rule Set ◽

Metasearch Engine ◽

Two Phases ◽

Search Systems

Metasearch engine is a system that provides unified access to multiple existing search engines. After the results returned from all used component search engines are collected, the metasearch system merges the results into a single ranked list which is expected to be better than the results of the best of the participating search systems. The success of a metasearch engine depends mainly on their rank aggregation method. The system is a better one, if the aggregated list of results displayed before the user satisfies the user with his information need. In this paper, we discuss the development of a metasearch engine that performs user feedback based metasearching using modified rough set based aggregation. Metasearching using the modified rough set based aggregation is performed in two phases namely the ranking rule learning phase and the rank aggregation phase. For each query in the training set, we mine the ranking rules and select the best rules-set by performing cross-validation test. Once the system is trained, we use the best rule set to get the overall ranking for the results returned from different search systems in response to other queries. We also present few snapshots of our system.

Download Full-text