Expert-Novice Differences in Searching the Web

Three web-user experts and three novices were observed in a problem-solving context. Their task was to search and evaluate information on the World Wide Web in order to write a research paper about an unfamiliar topic. A cognitive task analysis and verbal protocols were used. Results indicate differences in the ways experts approach and solve problem. These differences were in terms of (a) knowledge base; (b) problem space; (c) strategies; and (d) affect. Novice-expert differences have instructional implications for teachers, Information Technologists and Web site designers

Sharing and Reusing Semantic Queries for Searching the Web

This Bachelor's Thesis was preformed during a study stay at the Université de Nice, France. It is about developing a graphical user interface for drawing or generating intentional maps using goals and strategies.This Bachelor's Thesis was preformed during a study stay at the Université de Nice, France. It is about developing a graphical user interface for drawing or generating intentional maps using goals and strategies.

Searching the web builds fuller picture of arachnid trade

Trade in arachnids includes millions of individuals and over 1264 species, with over 70% of individuals coming from the wild. Wildlife trade is a major driver of biodiversity loss, yet whilst the impacts of trade in some species are relatively well-known, some taxa, such as many invertebrates are often overlooked. Here we explore global patterns of trade in the arachnids, and detected 1,264 species from 66 families and 371 genera in trade. Trade in these groups exceeds millions of individuals, with 67% coming directly from the wild, and up to 99% of individuals in some genera. For popular taxa, such as tarantulas up to 50% are in trade, including 25% of species described since 2000. CITES only covers 30 (2%) of the species potentially traded. We mapped the percentage and number of species native to each country in trade. To enable sustainable trade, better data on species distributions and better conservation status assessments are needed. The disparity between trade data sources highlights the need to expand monitoring if impacts on wild populations are to be accurately gauged and the impacts of trade minimised.Peer reviewe

Predicting Causes of Reformulation in Intelligent Assistants

Intelligent assistants (IAs) such as Siri and Cortana conversationally interact with users and execute a wide range of actions (e.g., searching the Web, setting alarms, and chatting). IAs can support these actions through the combination of various components such as automatic speech recognition, natural language understanding, and language generation. However, the complexity of these components hinders developers from determining which component causes an error. To remove this hindrance, we focus on reformulation, which is a useful signal of user dissatisfaction, and propose a method to predict the reformulation causes. We evaluate the method using the user logs of a commercial IA. The experimental results have demonstrated that features designed to detect the error of a specific component improve the performance of reformulation cause detection.Comment: 11 pages, 2 figures, accepted as a long paper for SIGDIAL 201

Searching the web: The impact of user knowledge on search satisfaction

Searching on the web is a tedious process as it requires knowledge and skills on what and how to search.What to search is basically, the core of the searching activity as it represents the need of the searcher.How to search is related to the knowledge on how the facilities available on the web can be utilized in order to achieve the needs. Search satisfaction is the level of measurement that describes the achievement of the searcher towards his/her information needs.In this paper, two categories of knowledge: topic understanding and web search system understanding have been identified to contribute to the search satisfaction.The Linear Regression analysis confirms that both topic and search system understanding are equally important since they are significantly associated with search satisfaction

Query Chains: Learning to Rank from Implicit Feedback

This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform a sequence, or chain, of queries with a similar information need. Using query chains, we generate new types of preference judgments from search engine logs, thus taking advantage of user intelligence in reformulating queries. To validate our method we perform a controlled user study comparing generated preference judgments to explicit relevance judgments. We also implemented a real-world search engine to test our approach, using a modified ranking SVM to learn an improved ranking function from preference data. Our results demonstrate significant improvements in the ranking given by the search engine. The learned rankings outperform both a static ranking function, as well as one trained without considering query chains.Comment: 10 page

Specialised tools are needed when searching the web for rare disease diagnoses

In our recent paper, we study web search as an aid in the process of diagnosing rare diseases. To answer the question of how well Google Search and PubMed perform, we created an evaluation framework with 56 diagnostic cases and made our own specialized search engine, FindZebra (findzebra.com). FindZebra uses a set of publicly available curated sources on rare diseases and an open-source information retrieval system, Indri. Our evaluation and the feedback received after the publication of our paper both show that FindZebra outperforms Google Search and PubMed. In this paper, we summarize the original findings and the response to FindZebra, discuss why Google Search is not designed for specialized tasks and outline some of the current trends in using web resources and social media for medical diagnosis