Study on Noun Phrase of "N1+V+N2" Structure in Search Engine Query Logs

2011 ◽  
Author(s):  
Xia Wang ◽  
Li Zheng ◽  
Shuicai Shi ◽  
Xueqiang Lv
Keyword(s):  
Search Engine ◽  
Noun Phrase ◽  
Query Logs

Enhancing Web Search through Query Log Mining

2011 ◽  
pp. 438-442
Author(s):  
Ji-Rong Wen
Keyword(s):  
Information Retrieval ◽  
Search Engine ◽  
Web Mining ◽  
Web Search ◽  
Information Source ◽  
Query Log ◽  
Additional Information ◽  
Query Logs ◽  
Query Log Mining ◽  
The Web

Web query log is a type of file keeping track of the activities of the users who are utilizing a search engine. Compared to traditional information retrieval setting in which documents are the only information source available, query logs are an additional information source in the Web search setting. Based on query logs, a set of Web mining techniques, such as log-based query clustering, log-based query expansion, collaborative filtering and personalized search, could be employed to improve the performance of Web search.

Semantic Question Answering Using Wikipedia Categories Clustering

2014 ◽  
Vol 23 (04) ◽  
pp. 1460014 ◽  
Author(s):  
Georgios Stratogiannis ◽  
Georgios Siolas ◽  
Andreas Stafylopatis
Keyword(s):  
Information Retrieval ◽  
Linear Combination ◽  
Search Engine ◽  
Noun Phrase ◽  
Question Answering ◽  
Semantic Information ◽  
Web Pages ◽  
Clustering Method ◽  
Information Retrieval Methods ◽  
The Ideal

We describe a system that performs semantic Question Answering based on the combination of classic Information Retrieval methods with semantic ones. First, we use a search engine to gather web pages and then apply a noun phrase extractor to extract all the candidate answer entities from them. Candidate entities are ranked using a linear combination of two IR measures to pick the most relevant ones. For each one of the top ranked candidate entities we find the corresponding Wikipedia page. We then propose a novel way to exploit Semantic Information contained in the structure of Wikipedia. A vector is built for every entity from Wikipedia category names by splitting and lemmatizing the words that form them. These vectors maintain Semantic Information in the sense that we are given the ability to measure semantic closeness between the entities. Based on this, we apply an intelligent clustering method to the candidate entities and show that candidate entities in the biggest cluster are the most semantically related to the ideal answers to the query. Results on the topics of the TREC 2009 Related Entity Finding task dataset show promising performance.

scholarly journals Recommending High Utility Queries via Query-Reformulation Graph

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
JianGuo Wang ◽  
Joshua Zhexue Huang ◽  
Dingming Wu
Keyword(s):  
Random Walk ◽  
Search Engine ◽  
State Of The Art ◽  
Transition Probability ◽  
Experimental Results ◽  
Query Reformulation ◽  
Search Results ◽  
Query Recommendation ◽  
Query Logs ◽  
High Utility

Query recommendation is an essential part of modern search engine which aims at helping users find useful information. Existing query recommendation methods all focus on recommending similar queries to the users. However, the main problem of these similarity-based approaches is that even some very similar queries may return few or even no useful search results, while other less similar queries may return more useful search results, especially when the initial query does not reflect user’s search intent correctly. Therefore, we propose recommending high utility queries, that is, useful queries with more relevant documents, rather than similar ones. In this paper, we first construct a query-reformulation graph that consists of query nodes, satisfactory document nodes, and interruption node. Then, we apply an absorbing random walk on the query-reformulation graph and model the document utility with the transition probability from initial query to the satisfactory document. At last, we propagate the document utilities back to queries and rank candidate queries with their utilities for recommendation. Extensive experiments were conducted on real query logs, and the experimental results have shown that our method significantly outperformed the state-of-the-art methods in recommending high utility queries.

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