Classic IR (information retrieval) is inherently predicated on users searching for information, the so-called "information need". But the need behind a web search is often not informational -- it might be navigational (give me the url of the site I want to reach) or transactional (show me sites where I can perform a certain transaction, e.g. shop, download a file, or find a map). We explore this taxonomy of web searches and discuss how global search engines evolved to deal with web-specific needs.

/pdf/a-taxonomy-of-web-search-1c1wfauugs.pdf

A taxonomy of web search

In this article, we performed a comparative study to investigate the performance of methods for detecting emerging research fronts. Three types of citation network, co-citation, bibliographic coupling, and direct citation, were tested in three research domains, gallium nitride (GaN), complex network (CNW), and carbon nanotube (CNT). Three types of citation network were constructed for each research domain, and the papers in those domains were divided into clusters to detect the research front. We evaluated the performance of each type of citation network in detecting a research front by using the following measures of papers in the cluster: visibility, measured by normalized cluster size, speed, measured by average publication year, and topological relevance, measured by density. Direct citation, which could detect large and young emerging clusters earlier, shows the best performance in detecting a research front, and co-citation shows the worst. Additionally, in direct citation networks, the clustering coefficient was the largest, which suggests that the content similarity of papers connected by direct citations is the greatest and that direct citation networks have the least risk of missing emerging research domains because core papers are included in the largest component. © 2009 Wiley Periodicals, Inc.

Comparative study on methods of detecting research fronts using different types of citation

The latest disruptive trends and developments in digital age comprise social networking, mobility, analytics and cloud, popularly known as SMAC. The year 2016 saw Big Data Technologies being leveraged to power business intelligence applications. What holds in store for 2020 and beyond?

Big Data Analytics

Welcome to CIKM 2005 -- the Fourteenth ACM Conference on Information and Knowledge Management. The Organizing Committee, and also the numerous sponsors wanted it to be a premier place for the participants to exchange the latest results in the strongly interrelated fields of information retrieval, data bases, and knowledge management.This year, the conference is held the first time outside the United States of America: it takes place in Bremen, Germany, not only one of the famous old European Hanse Cities and one of the contemporary sixteen German States, but also City of Science in Germany for 2005. This fits very well the goal of this conference and all the co-located workshops: staying abreast of the scientific progress and exchanging ideas. We look forward to host the top scientist working in the three conference fields and the workshop subjects.

Proceedings of the 14th ACM international conference on Information and knowledge management

Government innovation through social media

eGovernment refers to the use of information and communications technologies (ICTs) to improve the quality of services and information offered to citizens, to make government more accountable to citizens and ad- vance public sector transparency. As already pointed out by other researchers, one of the most important issues for making eGovernment effective is to enable citizens to participate in the decision-making process. Nowadays, topics related to governmental decisions are among the most widely discussed ones within digital societies. This is not only because web 2.0 has empowered people with the ability to communicate remotely but also because governments all around the globe publish a great volume of their decisions and regulations online. In this paper, we propose the exploration of text and data mining techniques towards capturing the public"s opinion communi- cated online and concerning governmental decisions. The objective of our study is twofold and focuses on under- standing the citizen opinions about eGovernment issues and on the exploitation of these opinions in subsequent governmental actions. We examine several features in the user-generated content discussing governmental de- cisions in an attempt to automatically extract the citizen opinions from online posts dealing with public sector regulations and thereafter be able to organize the extracted opinions into polarized clusters. Our goal is to be able to automatically identify the public"s stance against governmental decisions and thus be able to infer how the citizens" viewpoints may affect subsequent government actions. To demonstrate the usability and added value of our proposed approach we have designed an interactive eGovernment infrastructure, the architecture of which we will present and discuss in our paper. Moreover, we will elaborate on the system details, its adaptation capac- ity and we will discuss its usage benefits for both citizens and public sector bodies. In this paper, we try to fill this void by proposing a novel eGovernment mechanism that captures the societal impact of public sector regulations in an attempt to decipher the public"s stance towards gov- ernmental decisions. In particular, we propose the exploitation of data mining techniques towards firstly capturing the public"s opinions (communicated online) about governmental decisions and sec- ondly analysing the polarity of the mined opinions so that they are considered in subsequent govern- mental decisions. Specifically, we introduce a method for decomposing citizens" opinions and com- ments that are posted in online fora and blogs, in order to evaluate how governmental decisions are perceived by the public and thereafter how the public"s implicit feedback should be interpreted by governmental bodies in their subsequent actions. What motivates our study is that up-to-date gov-

/pdf/public-opinion-mining-for-governmental-decisions-2sx9lmwmdp.pdf

Public Opinion Mining for Governmental Decisions

In this paper we introduce HealthPal, an intelligent dialogue-based mobile health monitoring system, which aims at supporting the elderly people who lack IT skills into monitoring their health condition on a daily basis, in their preferred environments and without relying on the assistance of their caretakers. HealthPal is a software application that we have designed in order to assist the ageing society take care of their health related needs in an independent and autonomous manner. The most innovative feature of the HealthPal system is the dialogue-based application that it incorporates and which enables the effective interaction between the system and the user even if the latter has no technology skills at all. This paper discusses the system architecture and usage scenarios, while commenting on the expected social impact of the system on the ageing society in general.

/pdf/healthpal-an-intelligent-personal-medical-assistant-for-3wxwsblorm.pdf

HealthPal: an intelligent personal medical assistant for supporting the self-monitoring of healthcare in the ageing society

An adaptive and personalized open source e-learning platform

Web Directories provide a way of locating relevant information on the Web. Typically, Web Directories rely on humans putting in significant time and effort into finding important pages on the Web and categorizing them in the Directory. In this paper we present a way for automating the creation of a Web Directory. At a high level, our method takes as input a subject hierarchy and a collection of pages. We first leverage a variety of lexical resources from the Natural Language Processing community to enrich our hierarchy. After that, we process the pages and identify sequences of important terms, which are referred to as lexical chains. Finally, we use the lexical chains in order to decide where in the enriched subject hierarchy we should assign every page. Our experimental results with real Web data show that our method is quite promising into assisting humans during page categorization.

Web directory construction using lexical chains

Web Directories are repositories of Web pages organized in a hierarchy of topics and sub-topics. In this paper, we present DirectoryRank, a ranking framework that orders the pages within a given topic according to how informative they are about the topic. Our method works in three steps: first, it processes Web pages within a topic in order to extract structures that are called lexical chains, which are then used for measuring how informative a page is for a particular topic. Then, it measures the relative semantic similarity of the pages within a topic. Finally, the two metrics are combined for ranking all the pages within a topic before presenting them to the users.

/pdf/directoryrank-ordering-pages-in-web-directories-1npokexru2.pdf

DirectoryRank: ordering pages in web directories

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