Time-aware approaches to information retrieval

Abstract

In this thesis, we address major challenges in searching temporal document collections. In such collections, documents are created and/or edited over time. Examples of temporal document collections are web archives, news archives, blogs, personal emails and enterprise documents. Unfortunately, traditional IR approaches based on term-matching only can give unsatisfactory results when searching temporal document collections. The reason for this is twofold: the contents of documents are strongly time-dependent, i.e., documents are about events happened at particular time periods, and a query representing an information need can be time-dependent as well, i.e., a temporal query.Our contributions in this thesis are different time-aware approaches within three topics in IR: content analysis, query analysis, and retrieval and ranking models. In particular, we aim at improving the retrieval effectiveness by 1) analyzing the contents of temporal document collections, 2) performing an analysis of temporal queries, and 3) explicitly modeling the time dimension into retrieval and ranking.Leveraging the time dimension in ranking can improve the retrieval effectiveness if information about the creation or publication time of documents is available. In this thesis, we analyze the contents of documents in order to determine the time of non-timestamped documents using temporal language models. We subsequently employ the temporal language models for determining the time of implicit temporal queries, and the determined time is used for re-ranking search results in order to improve the retrieval effectiveness.We study the effect of terminology changes over time and propose an approach to handling terminology changes using time-based synonyms. In addition, we propose different methods for predicting the effectiveness of temporal queries, so that a particular query enhancement technique can be performed to improve the overall performance. When the time dimension is incorporated into ranking, documents will be ranked according to both textual and temporal similarity. In this case, time uncertainty should also be taken into account. Thus, we propose a ranking model that considers the time uncertainty, and improve ranking by combining multiple features using learning-to-rank techniques.Through extensive evaluation, we show that our proposed time-aware approaches outperform traditional retrieval methods and improve the retrieval effectiveness in searching temporal document collections.

TX-Kw: An Effective Temporal XML Keyword Search

“…Here, we design a ranking model which is based on a mixture model used for IR [25] that linearly combines keywords similarity and temporal similarity and then we map it into XML objects. Given a single temporal object Ot and a temporal query Qt, we compute the similarity degree ρ s of Ot to Qt using the following formula:…”

Improving the Efficiency of Term Weighting in Set of Dynamic Documents

“…For example, the Wikipedia pages have different versions that these versions are created by different people to improve the content of the pages. Previous researches [1][2][3][4][5][6][7][8][9][10][11] have shown that investigating on these changes can improve the efficiency of information retrieval systems.…”

Promoting electronic health record search through a time-aware approach

“…Research on temporal information aiming at free-text EHR includes temporal information extraction [10], temporal pattern mining for classification [11] and temporal order extraction for drug-disease pair [12]. On the other hand, temporal information is also explored in IR on other datasets [13] including microblog [14] and academic literature [15]. However, how to integrate temporal information into IR for EHR still remains a challenge.…”