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National Repository of Grey Literature 2 records found  Search took 0.00 seconds. 
Visual browser of graph data
 Stenchlák, Štěpán ; Nečaský, Martin (advisor) ; Skopal, Tomáš (referee)
One way to publish data in a machine-readable form on the Internet is in the form of a graph. Such data are easy to interconnect between different data sources and thus create a large network of linked data. In order to visualize this data, we need to use graph tools, which can often be impractical because they usually show us all the information about the graph. The aim of this work is to create a web application that is able to visualize those graph data, provide information about them, and browse them using so-called configurations. A configuration describes what data from a large set of data on the Internet we want to display, how we visualize them, and how we can look at them. It allows us to filter out hundreds of uninteresting information, thus shielding users from a complex network of linked data so that users can focus on the data that interests them. 1
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Hybrid Deep Question Answering
 Aghaebrahimian, Ahmad ; Holub, Martin (advisor) ; Kordik, Pavel (referee) ; Pecina, Pavel (referee)
Title: Hybrid Deep Question Answering Author: Ahmad Aghaebrahimian Institute: Institute of Formal and Applied Linguistics Supervisor: RNDr. Martin Holub, Ph.D., Institute of Formal and Applied Lin- guistics Abstract: As one of the oldest tasks of Natural Language Processing, Question Answering is one of the most exciting and challenging research areas with lots of scientific and commercial applications. Question Answering as a discipline in the conjunction of computer science, statistics, linguistics, and cognitive science is concerned with building systems that automatically retrieve answers to ques- tions posed by humans in a natural language. This doctoral dissertation presents the author's research carried out in this discipline. It highlights his studies and research toward a hybrid Question Answering system consisting of two engines for Question Answering over structured and unstructured data. The structured engine comprises a state-of-the-art Question Answering system based on knowl- edge graphs. The unstructured engine consists of a state-of-the-art sentence-level Question Answering system and a word-level Question Answering system with results near to human performance. This work introduces a new Question An- swering dataset for answering word- and sentence-level questions as well. Start- ing from a...
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