- The paper presents SemanTelli, a hybrid search engine combining features of semantic and meta-search systems using intelligent agents.
- It employs key components like Query Combination Generator and Search Engine Priority Assigner to optimize search ranking via enhanced snippet analysis.
- Pilot results indicate improved ranking relevance with plans for future enhancements in response time and user interface adaptability.
The paper "Experience of Developing a Meta-Semantic Search Engine" presents a meta-semantic search engine called SemanTelli, which is a hybrid model combining features from both semantic and meta-search engines. The implementation leverages intelligent agents to enhance search results by integrating information from multiple existing semantic search engines, including DuckDuckGo, Hakia, and SenseBot. The focus is on improving the accuracy and relevance of search results through enhanced snippet analysis algorithms and supporting diverse search content such as images and news.
Components and Functionality
The SemanTelli architecture consists of several key components:
- Query Combination Generator (QCG): This module generates potential query permutations which are subsequently used to interact with different search engines.
- Search Engine Priority Assigner (SEPA): This component assigns priorities to different search engines based on query context, leveraging a Search Engine Information Database (SEID) that holds engine-specific data.
- Temporary Web Page Storage (TWPS): This acts as a buffer to store retrieved results which are later post-processed to enhance relevancy.
The system employs a refined ranking algorithm grounded in snippet analysis to arrange search results in order of relevance. Factors considered include keyword count, sequence alignment with the query, and initial weight derived from empirical assessments of source effectiveness (e.g., favoring DuckDuckGo over Hakia and SenseBot in some contexts).
Numerical Results and Implementation
The initial pilot of SemanTelli outlined in the paper demonstrates an improved ranking capability through the snippet analysis approach. However, the development phase is ongoing, with specific aspects, such as response time optimization and faster post-processing mechanisms, identified as areas for future refinement.
Practical and Theoretical Implications
Practically, the integration of multiple semantic engines with intelligent agent technology presents an innovative approach to augment search output relevance, potentially assisting information retrieval in various domains. Theoretically, the model highlights the nuanced benefits of semantic search through ontological structures, which theoretically allow for more accurate parsing and understanding of queries over traditional keyword-based engines.
The proposed model also raises interesting discussions about privacy, knowledge representation, and the broader implications of integrating semantic webs into daily search habits. With further refinements, SemanTelli could potentially lead to more sophisticated systems for web-based information retrieval, tailored to user preferences based on contextual factors and domain specificity.
Future Directions
Future development should consider:
- Enhancing the snippet analysis algorithm to accommodate larger and more complex datasets.
- Expanding support to include more semantic search engines, increasing the breadth of information that can be processed.
- Improving user interface and experience by incorporating adaptive learning frameworks to tailor search results more precisely to user behavior and preferences.
In conclusion, the SemanTelli project sets forth a promising framework that brings together multiple semantic search methodologies in a consolidated platform. The enhancements in snippet analysis mark a significant step forward in the field of search engine efficiency, potentially paving the way for future advancements in intelligent and autonomous search technologies.
