Abstract
We address the problem of finding query facets which are multiple groups of words or phrases that explain and summarize the content covered by a query. We assume that the important aspects of a query are usually presented and repeated in the query’s top retrieved documents in the style of lists, and query facets can be mined out by aggregating these significant lists. We propose a systematic solution, which we refer to as QDMiner, to automatically mine query facets by extracting and grouping frequent lists from
free text, HTML tags, and repeat regions within top search results. Experimental results show that a large number of lists do exist and useful query facets can be mined by QDMiner. We further analyze the problem of list duplication, and find better query facets can be mined by modeling fine-grained similarities between lists and penalizing the duplicated lists.
Existing System with Limitations
Existing systems that aim to summarize and explain the content covered by a query face several limitations:
- Manual Identification of Query Facets: Identifying multiple groups of words or phrases that explain and summarize a query often requires manual effort, which is time-consuming and inefficient.
- Inadequate Coverage of Important Aspects: Current methods may fail to capture all the important aspects of a query, especially those that are presented and repeated across multiple documents in list formats.
- Lack of Systematic Extraction: There is no systematic approach to automatically mine query facets from the top retrieved documents, leading to inconsistent and incomplete results.
- Duplication Issues: Existing systems struggle with the problem of list duplication, where redundant information is included, reducing the overall quality of the mined query facets.
- Limited Use of HTML and Repeat Regions: Many systems do not fully utilize the structure of HTML tags and repeat regions within web pages, which can be valuable for identifying relevant information.
Proposed System with Advantages
The proposed system, QDMiner, addresses these limitations by providing a systematic solution for automatically mining query facets:
- Automatic Mining of Query Facets: QDMiner automates the process of identifying and extracting query facets, eliminating the need for manual effort and ensuring consistent results.
- Extraction from Multiple Sources: The system extracts frequent lists from free text, HTML tags, and repeat regions within the top search results, ensuring comprehensive coverage of important query aspects.
- Aggregation of Significant Lists: QDMiner aggregates significant lists found in the top retrieved documents, ensuring that the most relevant and repeated information is captured and summarized.
- Handling List Duplication: The system addresses the problem of list duplication by modeling fine-grained similarities between lists and penalizing duplicated lists, resulting in more accurate and useful query facets.
- Experimental Validation: Experimental results demonstrate that QDMiner can mine a large number of useful query facets, validating its effectiveness and efficiency.
- Improved Query Facet Quality: By systematically extracting and refining lists, QDMiner provides better query facets that are more informative and representative of the query content.
- Enhanced Use of HTML Structure: The system leverages the structure of HTML tags and repeat regions within web pages, which enhances its ability to identify and group relevant information effectively.
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Web Technologies : HTML, CSS, JS. JSP
• Programming Language : Java and J2EE
• Database Connectivity : JDBC
• Backend Database : MySQL
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
- Processor : Core I3
- RAM Capacity : 2 GB
- Hard Disk : 250 GB
- Monitor : 15″ Color
- Mouse : Two or Three Button Mouse
- Key Board : Windows 08/10
