It has been shown that using phrases properly in the document retrieval leads to higher retrieval effectiveness. In this paper, we define four types of noun phrases and present an algorithm for recognizing these phrases in queries. The strengths of several existing tools are combined for phrase recognition. Our algorithm is tested using a set of 500 web queries from a query log, and a set of 238 TREC queries. Experimental results show that our algorithm yields high phrase recognition accuracy. We also use a baseline noun phrase recognition algorithm to recognize phrases from the TREC queries. A document retrieval experiment is conducted using the TREC queries (1) without any phrases, (2) with the phrases recognized from a baseline noun phrase recognition algorithm, and (3) with the phrases recognized from our algorithm respectively. The retrieval effectiveness of (3) is better than that of (2), which is better than that of (1). This demonstrates that utilizing phrases in queries does improve the retrieval effectiveness, and better noun phrase recognition yields higher retrieval performance.

With the amount of available text data in relational databases growing rapidly, the need for ordinary users to search such information is dramatically increasing. Even though the major RDBMSs have provided full-text search capabilities, they still require users to have knowledge of the database schemas and use a structured query language to search information. This search model is complicated for most ordinary users. Inspired by the big success of information retrieval (IR) style keyword search on the web, keyword search in relational databases has recently emerged as a new research topic. The differences between text databases and relational databases result in three new challenges: (1) Answers needed by users are not limited to individual tuples, but results assembled from joining tuples from multiple tables are used to form answers in the form of tuple trees. (2) A single score for each answer (i.e. a tuple tree) is needed to estimate its relevance to a given query. These scores are used to rank the most relevant answers as high as possible. (3) Relational databases have much richer structures than text databases. Existing IR strategies to rank relational outputs are not adequate. In this paper, we propose a novel IR ranking strategy for effective keyword search. We are the first that conducts comprehensive experiments on search effectiveness using a real world database and a set of keyword queries collected by a major search company. Experimental results show that our strategy is significantly better than existing strategies. Our approach can be used both at the application level and be incorporated into a RDBMS to support keyword-based search in relational databases.

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Noun phrases in queries are identified and classified into four types: proper names, dictionary phrases, simple phrases and complex phrases. A document has a phrase if all content words in the phrase are within a window of a certain size. The window sizes for different types of phrases are different and are determined using a decision tree. Phrases are more important than individual terms. Consequently, documents in response to a query are ranked with matching phrases given a higher priority. We utilize WordNet to disambiguate word senses of query terms. Whenever the sense of a query term is determined, its synonyms, hyponyms, words from its definition and its compound words are considered for possible additions to the query. Experimental results show that our approach yields between 23% and 31% improvements over the best-known results on the TREC 9, 10 and 12 collections for short (title only) queries, without using Web data.

Current Web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to learn user profiles from users' search histories. The user profiles are then used to improve retrieval effectiveness in Web search. A user profile and a general profile are learned from the user's search history and a category hierarchy, respectively. These two profiles are combined to map a user query into a set of categories which represent the user's search intention and serve as a context to disambiguate the words in the user's query. Web search is conducted based on both the user query and the set of categories. Several profile learning and category mapping algorithms and a fusion algorithm are provided and evaluated. Experimental results indicate that our technique to personalize Web search is both effective and efficient.

Current web search engines are built to serve all users, independent of the needs of any individual user. Personalization of web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to map a user query to a set of categories, which represent the user's search intention. This set of categories can serve as a context to disambiguate the words in the user's query. A user profile and a general profile are learned from the user's search history and a category hierarchy respectively. These two profiles are combined to map a user query into a set of categories. Several learning and combining algorithms are evaluated and found to be effective. Among the algorithms to learn a user profile, we choose the Rocchio-based method for its simplicity, efficiency and its ability to be adaptive. Experimental results indicate that our technique to personalize web search is both effective and efficient.