From changes to dynamics: Dynamics analysis of linked open data sources

The Linked Open Data (LOD) cloud changes frequently. Recent approaches focus mainly on quantifying the changes that occur in the LOD cloud by comparing two snapshots of a linked dataset captured at two different points in time. These change metrics are able to measure absolute changes between these two snapshots. However, they cannot determine the dynamics of a dataset over a period of time, i.e., the intensity of how the data evolved in this period. In this paper, we present a general framework to analyse the dynamics of linked datasets within a given time interval. We propose a function to measure the dynamics of a LOD dataset, which is defined as the aggregation of absolute, infinitesimal changes, provided by change metrics. Our method can be parametrised to incorporate and make use of existing change metrics. Furthermore, our framework enables the use of different decay functions within the dynamics computation for different weights on changes depending on when they occurred in the observed time interval. We apply our framework to conduct an investigation on the dynamics of selected LOD datasets. We apply our analysis on a large-scale LOD dataset that is obtained from the LOD cloud by weekly crawls over more than a year. Finally, we discuss the benefits and potential applications of our dynamics function in a real world scenario

Provenance for SPARQL queries

Determining trust of data available in the Semantic Web is fundamental for applications and users, in particular for linked open data obtained from SPARQL endpoints. There exist several proposals in the literature to annotate SPARQL query results with values from abstract models, adapting the seminal works on provenance for annotated relational databases. We provide an approach capable of providing provenance information for a large and significant fragment of SPARQL 1.1, including for the first time the major non-monotonic constructs under multiset semantics. The approach is based on the translation of SPARQL into relational queries over annotated relations with values of the most general m-semiring, and in this way also refuting a claim in the literature that the OPTIONAL construct of SPARQL cannot be captured appropriately with the known abstract models.Comment: 22 pages, extended version of the ISWC 2012 paper including proof

Blog ontology (BloOn) and Blog visualization system (BloViS)

Blogs have emerged as a powerful way to convey and spread any sort of ideas. Thousands of people write daily in these on-line diaries and hold a captive audience. Furthermore information spread in blogs provide an online laboratory to analyze how brands, trends, ideas and information spread through social communities in the Internet. To support this analysis, in this paper describes an ontology for Blogs (BloOn) which describes the blog data domain and enable services for analyzing and reasoning over blog data across applications; and proposes a blog ontology visualization system (BloViS) based on 3D visualization techniques, metaphors and the incorporation of virtual world features for the users to investigate the nature of meme dissemination. We expect that this proposed Information Visualization platform may advance the state of the art of Internet memetics phenomena, blog ontology and 3D visualization of digital memes

Strategies for efficiently keeping local linked open data caches up-to-date

Quite often, Linked Open Data (LOD) applications pre-fetch data from the Web and store local copies of it in a cache for faster access at runtime. Yet, recent investigations have shown that data published and interlinked on the LOD cloud is subject to frequent changes. As the data in the cloud changes, local copies of the data need to be updated. However, due to limitations of the available computational resources (e.g., network bandwidth for fetching data, computation time) LOD applications may not be able to permanently visit all of the LOD sources at brief intervals in order to check for changes. These limitations imply the need to prioritize which data sources should be considered first for retrieving their data and synchronizing the local copy with the original data. In order to make best use of the resources available, it is vital to choose a good scheduling strategy to know when to fetch data of which data source. In this paper, we investigate different strategies proposed in the literature and evaluate them on a large-scale LOD dataset that is obtained from the LOD cloud by weekly crawls over the course of three years. We investigate two different setups: (i) in the single step setup, we evaluate the quality of update strategies for a single and isolated update of a local data cache, while (ii) the iterative progression setup involves measuring the quality of the local data cache when considering iterative updates over a longer period of time. Our evaluation indicates the effectiveness of each strategy for updating local copies of LOD sources, i.e, we demonstrate for given limitations of bandwidth, the strategies’ performance in terms of data accuracy and freshness. The evaluation shows that the measures capturing change behavior of LOD sources over time are most suitable for conducting updates