Mapping Knowledge Flows in ExploratoryWeb Searches

This paper provides an understanding of the knowledge flow in exploratory Web searches. Based on the Design Science Research epistemology, we represented the information-gathering behaviour and uncovered the knowledge flow of Web searchers as a Knowledge-intensive Process (KiP). By mapping searchers' steps and paths during Web searches, representing their search patterns and decision-making process, it was possible to reveal the knowledge flow and infer the resources more likely to be selected to meet the searchers' information needs. With the help of six teachers, we applied the Think-Aloud method in a scenario where they searched for online (educational) resources on the Web. The application of the Think-Aloud method made knowledge-flow processes explicit throughout Web searches. The results can support new strategies for information retrieval systems and be applied to support expertise exchange and innovation

A Systematic Mapping Study on Search Engine Industry’s Patent Innovations Focused on Matching the Search Results to User Intent

This report presents a systematic mapping study to survey patent documents in Brazilian and international databases to characterize the technical state-of-the-art in search engines under the intent matching paradigm as well as to discover the size of academia initiatives as part of the technical state-of-the-art agenda to commercial innovation ventures in the search engine industry. The goal is achieved by the identification of the search engine industry most common innovation claims concerning the intent matching paradigm and by the discussion of important issues regarding its patent application patterns, such as the search engine industry’s proficiency to churn its innovative landscape.This report presents a systematic mapping study to survey patent documents in Brazilian and international databases to characterize the technical state-of-the-art in search engines under the intent matching paradigm as well as to discover the size of academia initiatives as part of the technical state-of-the-art agenda to commercial in- novation ventures in the search engine industry. The goal is achieved by the identifica- tion of the search engine industry most common innovation claims concerning the in- tent matching paradigm and by the discussion of important issues regarding its patent application patterns, such as the search engine industry’s proficiency to churn its inno- vative landscape

Using Query Reformulation to Compare Learning Behaviors in Web Search Engines

Web search engines have gained importance as tools capable of connecting informal and self-learning with formal learning by aiding individuals in retrieving relevant information through the formulation and modification of their queries. Understand the differences between query states and their transitions becomes increasingly important, as doing so makes the optimization of search engines' results according to educational uses and needs possible. This paper introduces the ESKiP Taxonomy of Query States, a classification framework validated in an experiment involving two different query log datasets. It enables the comparison between the behaviors of users in search for knowledge (learners) and users performing transactional or factual searches in Web search engines

Modeling Exploratory Search as a Knowledge-Intensive Process

Searching as Learning and Information Seeking require exploratory search to be modeled for supporting learning. The present paper introduces a model of exploratory search that was applied on web searching in language teacher education, which promoted its evolution and validation, and enabled a visualization of search pattern and learning process. This model was able to help clarify best practices associated to users' decision-making process regarding suitable and not suitable information and to capture the relevance of context variables, personal skills and expertise that users utilize as filters for the search