An Ontology-Based Approach to Query Suggestion Diversification

Query suggestion is proposed to generate alternative queries and help users explore and express their information needs. Most existing query suggestion methods generate query suggestions based on document information or search logs without considering the semantic relationships between the original query and the suggestions. In addition, existing query suggestion diversifying methods generally use greedy algorithm, which has high complexity. To address these issues, we propose a novel query suggestion method to generate semantically relevant queries and diversify query suggestion results based on the WordNet ontology. First, we generate the query suggestion candidates based on Markov random walk. Second, we diversify the candidates according the different senses of original query in the WordNet. We evaluate our method on a large-scale search log dataset of a commercial search engine. The outstanding feature of our method is that our query suggestion results are semantically relevant belonging to different topics. The experimental results show that our method outperforms the two well-known query suggestion methods in terms of precision and diversity with lower time consumption.