Search

Accelerated by the technological advances in the biomedical domain, the size of its literature has been growing very rapidly. As a consequence, it is not feasible for individual researchers to comprehend and synthesize all the information related to their interests. Therefore, it is conceivable to discover hidden knowledge, or hypotheses, by linking fragments of information independently described in the literature. In fact, such hypotheses have been reported in the literature mining community; some of which have even been corroborated by experiments. This paper mainly focuses on hypothesis ranking and investigates an approach to identifying reasonable ones based on semantic similarities between events which lead to respective hypotheses. Our assumption is that hypotheses generated from semantically similar events are more reasonable. We developed a prototype system called, Hypothesis Explorer, and conducted evaluative experiments through which the validity of our approach is demonstrated in comparison with those based on term frequencies, often adopted in the previous work.

Most commercial search engines have a query suggestion feature, which is designed to capture various possible search intents behind the user's original query. However, even though different search intents behind a given query may have been popular at different time periods in the past, existing query suggestion methods neither utilize nor present such information. In this study, we propose Time-aware Structured Query Suggestion (TaSQS) which clusters query suggestions along a timeline so that the user can narrow down his search from a temporal point of view. Moreover, when a suggested query is clicked, TaSQS presents web pages from query-URL bipartite graphs after ranking them according to the click counts within a particular time period. Our experiments using data from a commercial search engine log show that the time-aware clustering and the time-aware document ranking features of TaSQS are both effective.

Query expansion methods using pseudo-relevance feedback have been shown effective for microblog search because they can solve vocabulary mismatch problems often seen in searching short documents such as Twitter messages (tweets), which are limited to 140 characters. Pseudo-relevance feedback assumes that the top ranked documents in the initial search results are relevant and that they contain topic-related words appropriate for relevance feedback. However, those assumptions do not always hold in reality because the initial search results often contain many irrelevant documents. In such a case, only a few of the suggested expansion words may be useful with many others being useless or even harmful. To overcome the limitation of pseudo-relevance feedback for microblog search, we propose a novel query expansion method based on two-stage relevance feedback that models search interests by manual tweet selection and integration of lexical and temporal evidence into its relevance model. Our experiments using a corpus of microblog data (the Tweets2011 corpus) demonstrate that the proposed two-stage relevance feedback approaches considerably improve search result relevance over almost all topics.

During disastrous events, much information is posted on microblogging services. These postings sometimes contain important information concerning, for example, safety and support for afflicted people. However, due to the overwhelming volume of information on microblog, it is often difficult to find such useful information using the current microblogging search system. Given the background, this paper proposes two types of interfaces for effectively identifying useful information in the event of disasters. First, postings containing similar contents are grouped and displayed in the chronological order so that users could easily identify a group of messages directly relevant to their urgent needs. Second, a message chosen by the users is used to reformulate the initial search query to refine the search results. In order to show the effectiveness of the proposed interfaces, a user study is conducted on the Twitter Corpus collected during the event of the Great East Japan Earthquake in 2011.

The appearance of microblogging services has led to many short documents being issued by crowds of people. To retrieve useful information from among such a huge quantity of messages, query expansion (QE) is usually used to enrich a user query. Some QE methods for microblog search utilize temporal properties (e.g., recency and temporal variation) derived from the real-time characteristic that many messages are posted by users when an interesting event has recently occurred. Our approach leverages temporal properties for QE and combines them according to the temporal variation of a given topic. Experimental results show that this QE method using automatically combined temporal properties is effective at improving retrieval performance.