This paper presents a creativity support tool, called FreePub, to collect and organize scienti?c material using mindmaps. Mindmaps are visual, graph-based represenations of concepts, ideas, notes, tasks, etc. They generally take a hierarchical or tree branch format, with ideas branching into their subsections. FreePub supports creativity cycles. A user starts such a cycle by setting up her domain of interest using mindmaps. Then, she can browse mindmaps and launch search tasks to gather relevant publications from several data sources. FreePub, besides publications, identi?es helpful supporting material (e.g., blog posts, presentations). All retrieved information from FreePub can be imported and organized in mindmaps. FreePub has been fully implemented on top of FreeMind, a popular open-source, mindmapping tool.
The use of mindmaps in information retrieval tasks has been acknowledged by several researchers. @cite_19 , the authors present how information retrieval on mind maps could be used to enhance expert search, document summarization, keyword based search engines, document recommender systems and determining word relatedness.
@cite_19
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Mind maps are used by millions of people. In this paper we present how information retrieval on mind maps could be used to enhance expert search, document summarization, keyword based search engines, document recommender systems and determining word relatedness. For instance, words in a mind map could be used for creating a skill profile of the mind maps' author and hence enhance expert search. This paper is a research-in-progress paper which means no research results are presented but only ideas. In the Navigational Retrieval Subtask 2 (Navi-2) at the NTCIR-5 WEB Task, a hypothetical user knows a specific item (e.g., a product, company, and person) and requires to find one or more representative Web pages related to the item. This paper describes our system participated in the Navi-2 subtask and reports the evaluation results of our system. Our system uses three types of information obtained from the NTCIR5 Web collection: page content, anchor text, and link structure. Specifically, we exploit anchor text in two perspectives. First, we compare the effectiveness of two different methods to model anchor text. Second, we use anchor text to extract synonyms for query expansion purposes. We show the effectiveness of our system experimentally. We propose a method to highlight query hits in hierarchically clustered collections of interrelated items such as digital libraries or knowledge bases. The method is based on the idea that organizing search results similarly to their arrangement on a fixed reference map facilitates orientation and assessment by preserving a user's mental map. Here, the reference map is built from an MDS layout of the items in a Voronoi treemap representing their hierarchical clustering, and we use techniques from dynamic graph layout to align query results with the map. The approach is illustrated on an archive of newspaper articles. Many recommendation and retrieval tasks can be represented as proximity queries on a labeled directed graph, with typed nodes representing documents, terms, and metadata, and labeled edges representing the relationships between them. Recent work has shown that the accuracy of the widely-used random-walk-based proximity measures can be improved by supervised learning - in particular, one especially effective learning technique is based on Path-Constrained Random Walks (PCRW), in which similarity is defined by a learned combination of constrained random walkers, each constrained to follow only a particular sequence of edge labels away from the query nodes. The PCRW based method significantly outperformed unsupervised random walk based queries, and models with learned edge weights. Unfortunately, PCRW query systems are expensive to evaluate. In this study we evaluate the use of approximations to the computation of the PCRW distributions, including fingerprinting, particle filtering, and truncation strategies. In experiments on several recommendation and retrieval problems using two large scientific publications corpora we show speedups of factors of 2 to 100 with little loss in accuracy.