Effect of heuristics on serendipity in path-based storytelling with linked data

Path-based storytelling with Linked Data on the Web provides users the ability to discover concepts in an entertaining and educational way. Given a query context, many state-of-the-art pathfinding approaches aim at telling a story that coincides with the user's expectations by investigating paths over Linked Data on the Web. By taking into account serendipity in storytelling, we aim at improving and tailoring existing approaches towards better fitting user expectations so that users are able to discover interesting knowledge without feeling unsure or even lost in the story facts. To this end, we propose to optimize the link estimation between - and the selection of facts in a story by increasing the consistency and relevancy of links between facts through additional domain delineation and refinement steps. In order to address multiple aspects of serendipity, we propose and investigate combinations of weights and heuristics in paths forming the essential building blocks for each story. Our experimental findings with stories based on DBpedia indicate the improvements when applying the optimized algorithm

Distributed agents for online spatial searches

As the availability and utilisation of online data blossoms, automated online searches—whether to answer a simple question, seek specific sensor readings, or investigate research in a particular domain—have raised a number of issues. Simple search tools do not access the deep web of services and online forms, and cannot handle knowledge domain-specific search problems, but specialist search tools can have a narrow domain and applicability. Some online tools circumvent these problems by putting more filter controls into the hands of users, but this leads to more complex interfaces which can raise usability barriers. A distributed approach, where specialised search agents act autonomously to find contextualised information, can provide a useful compromise between a simple, general search interface and specialist searches. This paper outlines work in progress on design and use of specialist search agents, with a case study to find public transportation bus stops within a spatial region. The approach is demonstrated with a proof of concept web interface, developed to interpret a text query to find and show bus stop locations within a named boundary by coordinating multiple online search agents. Search agents were designed to follow a common model to allow for future development of agent types, including specialist agents used in the case study to search standard open web services and extract spatial features

mspecLINE: bridging knowledge of human disease with the proteome

<p>Abstract</p> <p>Background</p> <p>Public proteomics databases such as PeptideAtlas contain peptides and proteins identified in mass spectrometry experiments. However, these databases lack information about human disease for researchers studying disease-related proteins. We have developed mspecLINE, a tool that combines knowledge about human disease in MEDLINE with empirical data about the detectable human proteome in PeptideAtlas. mspecLINE associates diseases with proteins by calculating the semantic distance between annotated terms from a controlled biomedical vocabulary. We used an established semantic distance measure that is based on the co-occurrence of disease and protein terms in the MEDLINE bibliographic database.</p> <p>Results</p> <p>The mspecLINE web application allows researchers to explore relationships between human diseases and parts of the proteome that are detectable using a mass spectrometer. Given a disease, the tool will display proteins and peptides from PeptideAtlas that may be associated with the disease. It will also display relevant literature from MEDLINE. Furthermore, mspecLINE allows researchers to select proteotypic peptides for specific protein targets in a mass spectrometry assay.</p> <p>Conclusions</p> <p>Although mspecLINE applies an information retrieval technique to the MEDLINE database, it is distinct from previous MEDLINE query tools in that it combines the knowledge expressed in scientific literature with empirical proteomics data. The tool provides valuable information about candidate protein targets to researchers studying human disease and is freely available on a public web server.</p

Geotag Propagation with User Trust Modeling

The amount of information that people share on social networks is constantly increasing. People also comment, annotate, and tag their own content (videos, photos, notes, etc.), as well as the content of others. In many cases, the content is tagged manually. One way to make this time-consuming manual tagging process more efficient is to propagate tags from a small set of tagged images to the larger set of untagged images automatically. In such a scenario, however, a wrong or a spam tag can damage the integrity and reliability of the automated propagation system. Users may make mistakes in tagging, or irrelevant tags and content may be added maliciously for advertisement or self-promotion. Therefore, a certain mechanism insuring the trustworthiness of users or published content is needed. In this chapter, we discuss several image retrieval methods based on tags, various approaches to trust modeling and spam protection in social networks, and trust modeling in geotagging systems. We then consider a specific example of automated geotag propagation system that adopts a user trust model. The tag propagation in images relies on the similarity between image content (famous landmarks) and its context (associated geotags). For each tagged image, similar untagged images are found by the robust graph-based object duplicate detection and the known tags are propagated accordingly. The user trust value is estimated based on a social feedback from the users of the photo-sharing system and only tags from trusted users are propagated. This approach demonstrates that a practical tagging system significantly benefits from the intelligent combination of efficient propagation algorithm and a user-centered trust model

The structure-function relationship of oncogenic LMTK3

Elucidating signaling driven by lemur tyrosine kinase 3 (LMTK3) could help drug development. Here, we solve the crystal structure of LMTK3 kinase domain to 2.1Å resolution, determine its consensus motif and phosphoproteome, unveiling in vitro and in vivo LMTK3 substrates. Via high-throughput homogeneous time-resolved fluorescence screen coupled with biochemical, cellular, and biophysical assays, we identify a potent LMTK3 small-molecule inhibitor (C28). Functional and mechanistic studies reveal LMTK3 is a heat shock protein 90 (HSP90) client protein, requiring HSP90 for folding and stability, while C28 promotes proteasome-mediated degradation of LMTK3. Pharmacologic inhibition of LMTK3 decreases proliferation of cancer cell lines in the NCI-60 panel, with a concomitant increase in apoptosis in breast cancer cells, recapitulating effects of LMTK3 gene silencing. Furthermore, LMTK3 inhibition reduces growth of xenograft and transgenic breast cancer mouse models without displaying systemic toxicity at effective doses. Our data reinforce LMTK3 as a druggable target for cancer therap

Image Annotation Incorporating Low-Rankness, Tag and Visual Correlation and Inhomogeneous Errors

Tag-based image retrieval (TBIR) has drawn much attention in recent years due to the explosive amount of digital images and crowdsourcing tags. However, TBIR is still suffering from the incomplete and inaccurate tags provided by users, posing a great challenge for tag-based image management applications. In this work, we proposed a novel method for image annotation, incorporating several priors: Low-Rankness, Tag and Visual Correlation and Inhomogeneous Errors. Highly representative CNN feature vectors are adopt to model the tag-visual correlation and narrow the semantic gap. And we extract word vectors for tags to measure similarity between tags in the semantic level, which is more accurate than traditional frequency-based or graph-based methods. We utilize the accelerated proximal gradient (APG) method to solve our model efficiently. Extensive experiments conducted on multiple benchmark datasets demonstrate the effectiveness and robustness of the proposed method.Comment: This paper has been withdrawn by the author to update more experiments and some errors in the algorith