818 research outputs found
Deep Learning Relevance: Creating Relevant Information (as Opposed to Retrieving it)
What if Information Retrieval (IR) systems did not just retrieve relevant
information that is stored in their indices, but could also "understand" it and
synthesise it into a single document? We present a preliminary study that makes
a first step towards answering this question. Given a query, we train a
Recurrent Neural Network (RNN) on existing relevant information to that query.
We then use the RNN to "deep learn" a single, synthetic, and we assume,
relevant document for that query. We design a crowdsourcing experiment to
assess how relevant the "deep learned" document is, compared to existing
relevant documents. Users are shown a query and four wordclouds (of three
existing relevant documents and our deep learned synthetic document). The
synthetic document is ranked on average most relevant of all.Comment: Neu-IR '16 SIGIR Workshop on Neural Information Retrieval, July 21,
2016, Pisa, Ital
Effective and Efficient Similarity Search in Scientific Workflow Repositories
International audienceScientific workflows have become a valuable tool for large-scale data processing and analysis. This has led to the creation of specialized online repositories to facilitate worflkow sharing and reuse. Over time, these repositories have grown to sizes that call for advanced methods to support workflow discovery, in particular for similarity search. Effective similarity search requires both high quality algorithms for the comparison of scientific workflows and efficient strategies for indexing, searching, and ranking of search results. Yet, the graph structure of scientific workflows poses severe challenges to each of these steps. Here, we present a complete system for effective and efficient similarity search in scientific workflow repositories, based on the Layer Decompositon approach to scientific workflow comparison. Layer Decompositon specifically accounts for the directed dataflow underlying scientific workflows and, compared to other state-of-the-art methods, delivers best results for similarity search at comparably low runtimes. Stacking Layer Decomposition with even faster, structure-agnostic approaches allows us to use proven, off-the-shelf tools for workflow indexing to further reduce runtimes and scale similarity search to sizes of current repositories
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