Study On An Exploratory Search Query Recommendation Method Based On Multi-domain Ontology

In a process of information search, if a user is not familiar with the target domain, or a search task itself is relatively complex, or the index of information system is not sufficient, the search behaviors of users are called exploratory search. In exploratory search, due to query recommendations based on ontology can find queries or keywords related to the initial query to help users construct accurate queries, they became effective ways to support exploratory search. In exploratory search, the characters of exploratory search lead to broaden concerns of users, and users are usually involved in multiple domains. Therefore, these characters make ontologies of multiple domains should be considered in exploratory search query recommendation based on ontology to help users complete exploratory search task.In response to these problems, this thesis introduces an exploratory search query recommendation method based on multi-domain ontology. Firstly, this thesis solves the problem of multi-ontology resource ambiguity through the process of training and predicting parameters of Hidden Markov models. Then, with the help of search engine to get authority domain collections of keywords, this thesis analyzes the usage of concepts in domain ontology in authority domain collections, which can give weights for query recommendations. Finally, by leveraging a collection of documents related to recommendations and information of exploratory paths, exploratory models are constructed, which can be used as bases to sort and select recommendations.In details, aiming at the resource ambiguity problem in multi-domain ontology, by leveraging Hidden Markov model and large scale ontology semantic annotated corpus, the thesis extracts features and trains model parameters, and forecasts unlabeled sentences to achieve resource disambiguation. Subsequently, by obtaining authority domain collections of queries, similarity measures between keywords in authority domain collections and text-based information in different domain ontologies are calculated, which are the weights of ontologies during query recommendation. Finally, utilizing a collection of documents related to recommendations and information of exploratory paths, language models, exploratory models and query models are constructed. By examining the differences in distribution between exploratory models and query models, recommendation weights are decided. By synthesizing ontology weights and recommendation weights, recommendations are sorted and selected.By respectively considering both objective and subjective aspects, this thesis compares recommendation selection methods based on exploratory models with two other baseline methods. Experiment results show that the recommendation selection method based on exploratory models can help users to construct queries more effectively, thus complete the exploratory search task.