SAVE-SD 2015 Publication: Supplementary Material

Overview

This page provides supplementary material for our publication [1] in the SAVE-SD 2015 [6] workshop on Semantics, Analytics, Visualisation: Enhancing Scholarly Data.

We have published our populated knowledge base from the experiments described in the paper. In order to reproduce the results in the "Application" section, we have provided a public endpoint using the Fuseki server and you can use the query box to execute the queries yourself.

Scenarios

In the "Application" section of our paper, there are three scenarios, where the user interacts with the populated knowledge base in order to retrieve documents related to her task at hand. In this page, you can find each query and execute it yourself by copying and pasting it to the query box below.

As a running example, let us imagine a use case: A user wants to write a literature review from a given set of documents about a specific topic. Ordinarily, the user has to read all of the retrieved documents in order to evaluate their relevance to her task — a cumbersome and time-consuming task. However, using our approach the user can directly query for the rhetorical type that she needs from the system. We demonstrate this with the example queries shown below.

Scenario 1: The user has downloaded a set of new articles from the web. Before reading each article thoroughly, she would like to obtain a summary of the contributions of all articles, so she can decide which articles are relevant to her task.

Scenario 2: From the set of downloaded articles, the user would like to find only those articles that have a contribution mentioning `linked data'.

Scenario 3: The user would like to find only those articles that have a contribution mentioning topics related to `linked data'.
(Please note that this query uses the public DBpedia endpoint and thus may take longer to execute, or fail if the endpoint is offline.)

SPARQLer Interface

References