Adam Jatowt

Can social bookmarking enhance search in the web

Social bookmarking is an emerging type of a Web service that helps users share, classify, and discover interesting resources. In this paper, we explore the concept of an enhanced search, in which data from social bookmarking systems is exploited for enhancing search in the Web. We propose combining the widely used link-based ranking metric with the one derived using social bookmarking data. First, this increases the precision of a standard link-based search by incorporating popularity estimates from aggregated data of bookmarking users. Second, it provides an opportunity for extending the search capabilities of existing search engines. Individual contributions of bookmarking users as well as the general statistics of their activities are used here for a new kind of a complex search where contextual, temporal or sentiment-related information is used. We investigate the usefulness of social bookmarking systems for the purpose of enhancing Web search through a series of experiments done on datasets obtained from social bookmarking systems. Next, we show the prototype system that implements the proposed approach and we present some preliminary results.

Survey of Temporal Information Retrieval and Related Applications

Temporal information retrieval has been a topic of great interest in recent years. Its purpose is to improve the effectiveness of information retrieval methods by exploiting temporal information in documents and queries. In this article, we present a survey of the existing literature on temporal information retrieval. In addition to giving an overview of the field, we categorize the relevant research, describe the main contributions, and compare different approaches. We organize existing research to provide a coherent view, discuss several open issues, and point out some possible future research directions in this area. Despite significant advances, the area lacks a systematic arrangement of prior efforts and an overview of state-of-the-art approaches. Moreover, an effective end-to-end temporal retrieval system that exploits temporal information to improve the quality of the presented results remains undeveloped.

Studying how the past is remembered: towards computational history through large scale text mining

History helps us understand the present and even to predict the future to certain extent. Given the huge amount of data about the past, we believe computer science will play an increasingly important role in historical studies, with computational history becoming an emerging interdisciplinary field of research. We attempt to study how the past is remembered through large scale text mining. We achieve this by first collecting a large dataset of news articles about different countries and analyzing the data using computational and statistical tools. We show that analysis of references to the past in news articles allows us to gain a lot of insight into the collective memories and societal views of different countries. Our work demonstrates how various computational tools can assist us in studying history by revealing interesting topics and hidden correlations. Our ultimate objective is to enhance history writing and evaluation with the help of algorithmic support.

Trustworthiness analysis of web search results

Increased usage of Web search engines in our daily lives means that the trustworthiness of searched results has become crucial. User studies on the usage of search engines and analysis of the factors used to determine trust that users have in search results are described in this paper. Based on the analysis, we developed a system to help users determine the trustworthiness of Web search results by computing and showing each returned pages topic majority, topic coverage, locality of supporting pages (i.e., pages linked to each search result) and other information. The measures proposed in the paper can be applied to the search of Web-based libraries or can be useful in the usage of digital library search systems.

A framework for analyzing semantic change of words across time

Recently, large amounts of historical texts have been digitized and made accessible to the public. Thanks to this, for the first time, it became possible to analyze evolution of language through the use of automatic approaches. In this paper, we show the results of an exploratory analysis aiming to investigate methods for studying and visualizing changes in word meaning over time. In particular, we propose a framework for exploring semantic change at the lexical level, at the contrastive-pair level, and at the sentiment orientation level. We demonstrate several kinds of NLP approaches that altogether give users deeper understanding of word evolution. We use two diachronic corpora that are currently the largest available historical language corpora. Our results indicate that the task is feasible and satisfactory outcomes can be already achieved by using simple approaches.

Extracting collective expectations about the future from large text collections

News articles often contain information about the future. Given the huge volume of information available nowadays, an automatic way for extracting and summarizing future-related information is desirable. Such information will allow people to obtain a collective image of the future, to recognize possible future scenarios and be prepared for the future events. We propose a model-based clustering algorithm for detecting future events based on information extracted from a text corpus. The algorithm takes into account both textual and temporal similarity of sentences. We demonstrate that our algorithm can be used to discover future events and estimate their probabilities over time.

Honto? search: estimating trustworthiness of web information by search results aggregation and temporal analysis

If the user wants to know trustworthiness of a proposition, such as whether gthe Japanese Prime Minister is Junichiro Koizumih is true or false, conventional search engines are not appropriate. We therefore propose a system that helps the user to determine trustworthiness of a statement that he or she is unconfident about. In our research, we estimate trustworthiness of a proposition by aggregating knowledge from the Web and analyzing creation time of web pages. We propose a method to estimate popularity from temporal viewpoint by analyzing how many pages discussed the proposition in a certain period of time and how continuously it appeared on the Web.

Estimating document focus time

Temporality is an important characteristic of text documents. While some documents are clearly atemporal, many have temporal character and can be mapped to certain time periods. In this paper, we introduce the problem of estimating focus time of documents. Document focus time is defined as the time to which the content of a document refers to and is considered as a complementary dimension to its creation time or timestamp. We propose several estimators of focus time by utilizing external knowledge bases such as news article collections which contain explicit temporal references. We then evaluate the effectiveness of our methods on diverse datasets of documents about historical events in five countries.

Detecting age of page content

Web pages often contain objects created at different times. The information about the age of such objects may provide useful context for understanding page content and may serve many potential uses. In this paper, we describe a novel concept for detecting approximate creation dates of content elements in Web pages. Our approach is based on dynamically reconstructing page histories using data extracted from external sources - Web archives and efficiently searching inside them to detect insertion dates of content elements. We discuss various issues involving the proposed approach and demonstrate the example of an application that enhances browsing the Web by inserting annotations with temporal metadata into page content on user request.

Web page summarization using dynamic content

Summarizing web pages have recently gained much attention from researchers. Until now two main types of approaches have been proposed for this task: content- and context-based methods. Both of them assume fixed content and characteristics of web documents without considering their dynamic nature. However the volatility of information published on the Internet argue for the implementation of more time-aware techniques. This paper proposes a new approach towards automatic web page description, which extends the concept of a web page by the temporal dimension. Our method provides a broader view on web document summarization and can complement the existing techniques.