Improving large-scale search engines with semantic annotations

an article by Damaris Fuentes-Lorenzo, Norberto Fernández, Jesús A. Fisteus and Luis Sánchez (Carlos III University, Madrid, Spain) published in Expert Systems with Applications Volume 40 Issue 6 (May 2013)

Abstract

Traditional search engines have become the most useful tools to search the World Wide Web. Even though they are good for certain search tasks, they may be less effective for others, such as satisfying ambiguous or synonym queries.

In this paper, we propose an algorithm that, with the help of Wikipedia and collaborative semantic annotations, improves the quality of web search engines in the ranking of returned results.

Our work is supported by

1. the logs generated after query searching,
2. semantic annotations of queries and
3. semantic annotations of web pages.

The algorithm makes use of this information to elaborate an appropriate ranking.

To validate our approach we have implemented a system that can apply the algorithm to a particular search engine.

Evaluation results show that the number of relevant web resources obtained after executing a query with the algorithm is higher than the one obtained without it.

Highlights

► We present a ranking algorithm for large-scale web search engines.
► The algorithm is built upon disambiguation with Wikipedia and semantic annotations.
► We have not built a new search engine from scratch; ranking algorithm enhances existing search engines with a semantic layer.
► We focus on the suitability of the ranking for a large amount of data.

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