Semantic Search Engines – Scientific Background
Semantic search engines represent an evolution in search technology, aiming to improve the precision and relevance of search results by understanding the context and semantics of user queries. Unlike traditional search engines that rely on keyword matching, semantic search engines leverage structured data and ontologies to deliver more accurate and contextually relevant information.
Key Features of Semantic Search Engines
Enhanced Query Understanding
Semantic search engines are designed to understand the intent behind user queries and the contextual significance of words, which allows them to generate more relevant results compared to traditional keyword-based search engines (Roy et al., 2020; Sinha, Dubey and Dubey, 2020). This involves interpreting the semantics of the information rather than just its location or display (Li, Wang and Huang, 2007).
Use of Semantic Web Technologies
These search engines operate over RDF Web data, also known as Linked Data, which presents unique challenges in terms of system design and implementation. They utilize Semantic Web standards to enhance data retrieval and indexing processes, allowing for more complex and context-aware queries (Hogan et al., 2015; Jagtap et al., 2015).
Improved User Experience
Semantic search engines aim to simplify the search process for end users by hiding the complexity of semantic data and query languages. This makes them more accessible to users who may not be familiar with domain-specific data or ontologies, while still supporting complex queries and providing precise, self-explanatory answers (Pratiba and Shobha, 2013).
Advantages Over Traditional Search Engines
Precision and Relevance
By understanding the purpose of the search and the context of the data, semantic search engines can significantly improve the precision of search results, reducing redundancy and increasing accuracy (Vora et al., 2019; Kassim and Rahmany, 2009). This is particularly beneficial in electronic business applications where users need to find specific information about products and services efficiently (Calsavara and Schmidt, 2004).
Support for Complex Queries
Semantic search engines are capable of handling complex queries that traditional search engines struggle with. They can solve intricate queries by considering the context and semantics of the web resources, which is a significant advantage in fields requiring detailed and specific information retrieval (Calsavara and Schmidt, 2004; Pratiba and Shobha, 2013).
Challenges and Future Directions
System Design and Implementation
The adoption of Semantic Web technologies introduces challenges related to the scale, unreliability, inconsistency, and noise of web data. These challenges necessitate careful system design and implementation to ensure effective performance and user satisfaction (Hogan et al., 2015).
Mainstream Adoption
Bringing semantic search engines into mainstream use involves overcoming difficulties such as knowledge overhead and the need for user-friendly interfaces. Future research and development are focused on enhancing the usability and accessibility of these systems to broaden their adoption (Hogan et al., 2015; Pratiba and Shobha, 2013).
In conclusion, semantic search engines offer a promising advancement in search technology by providing more precise and contextually relevant results. They leverage Semantic Web technologies to enhance query understanding and user experience, although challenges remain in system design and mainstream adoption.
Research Papers on Semantic Search Engines
Calsavara, A., & Schmidt, G., 2004. Semantic Search Engines. **, pp. 145-157. https://doi.org/10.1007/978-3-540-25958-9_14
Li, Y., Wang, Y., & Huang, X., 2007. A Relation-Based Search Engine in Semantic Web. IEEE Transactions on Knowledge and Data Engineering, 19. https://doi.org/10.1109/TKDE.2007.18
Hogan, A., Harth, A., Umbrich, J., Kinsella, S., Polleres, A., & Decker, S., 2015. Semantic Web Search Engine. International journal of engineering research and technology, 4. https://doi.org/10.17577/IJERTV4IS040908
Vora, H., Shah, Z., Gada, S., Bhatia, Y., & Kuri, M., 2019. Search Engine Based on Semantic Web. **.
Roy, S., Modak, A., Barik, D., & Goon, S., 2020. An Overview of Semantic Search Engines. **.
Jagtap, D., Argade, N., Date, S., Hole, S., & Salunke, M., 2015. Implementation of Intelligent Semantic Web Search Engine. International journal of engineering research and technology, 4. https://doi.org/10.17577/IJERTV4IS040156
Kassim, J., & Rahmany, M., 2009. Introduction to Semantic Search Engine. 2009 International Conference on Electrical Engineering and Informatics, 02, pp. 380-386. https://doi.org/10.1109/ICEEI.2009.5254709
Sinha, U., Dubey, V., & Dubey, V., 2020. The Technique of Different Semantic Search Engines. International Journal of Recent Technology and Engineering. https://doi.org/10.35940/ijrte.a2249.059120
Pratiba, D., & Shobha, G., 2013. Semantic Web Search Engine. **. https://doi.org/10.17577/ijertv4is040908
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