An Investigation of HTTP Header Information for Detecting Changes of Linked Open Data Sources

Dividino, Renata; Krämer, André; Gottron, Thomas

doi:10.1007/978-3-319-11955-7_18

Cited by 5 publications

(6 citation statements)

References 3 publications

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“…The Last-Modified HTTP header field denotes when a LOD source behind this URI has been changed last. However, in a previous investigation [9], we showed that only very few LOD sources (on average only 8%) provide correct update values. Consequently, applications relying on such information are susceptible to draw wrong conclusions.…”

Section: Introductionmentioning

confidence: 55%

Strategies for Efficiently Keeping Local Linked Open Data Caches Up-To-Date

Dividino

Gottron

Scherp

2015

The Semantic Web - ISWC 2015

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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.

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Section: Introductionmentioning

confidence: 55%

Strategies for Efficiently Keeping Local Linked Open Data Caches Up-To-Date

Dividino

Gottron

Scherp

2015

The Semantic Web - ISWC 2015

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“…Dividino et al [4] measured the degree of how often the lastmodified field in HTTP header of RDF documents is available and how often it is correctly used. The analysis revealed that on average only 7% of the documents provide accurate information for the last-modified field.…”

Section: Related Workmentioning

confidence: 99%

“…We motivate this feature from Umbrich et al [19] which showed that entities coming from the same PLD show similar temporal dynamics. The PLD of a URI is extracted using Guava 4 . If Guava identifies no PLD, the placeholder "other subject PLDs" is assigned.…”

Section: Features Computed On the Triplesmentioning

confidence: 99%

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Keeping linked open data caches up-to-date by predicting the life-time of RDF triples

Nishioka

Scherp

2017

Proceedings of the International Conference on Web Intelligence

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Many Linked Open Data applications require fresh copies of RDF data at their local repositories. Since RDF documents constantly change and those changes are not automatically propagated to the LOD applications, it is important to regularly visit the RDF documents to refresh the local copies and keep them up-to-date. For this purpose, crawling strategies determine which RDF documents should be preferentially fetched. Traditional crawling strategies rely only on how an RDF document has been modified in the past. In contrast, we predict on the triple level whether a change will occur in the future. We use the weekly snapshots of the DyLDO dataset as well as the monthly snapshots of the Wikidata dataset. First, we conduct an in-depth analysis of the life span of triples in RDF documents. Through the analysis, we identify which triples are stable and which are ephemeral. We introduce different features based on the triples and apply a simple but effective linear regression model. Second, we propose a novel crawling strategy based on the linear regression model. We conduct two experimental setups where we vary the amount of available bandwidth as well as iteratively observe the quality of the local copies over time. The results demonstrate that the novel crawling strategy outperforms the state of the art in both setups.

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“…Dynamics quantifies the evolution of a dataset over a specific period of time and takes into consideration the changes occurring in this period. Dividino et. al (2014) lists probably all works related to the dynamics of LOD datasets.…”

Section: Dynamics / Evolutionmentioning

confidence: 99%

Quantifying the Connectivity of a Semantic Warehouse and Understanding its Evolution over Time

Mountantonakis

Minadakis

Marketakis

et al. 2016

International Journal on Semantic Web and Information Systems

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In many applications one has to fetch and assemble pieces of information coming from more than one source for building a semantic warehouse offering more advanced query capabilities. In this paper the authors describe the corresponding requirements and challenges, and they focus on the aspects of quality and value of the warehouse. For this reason they introduce various metrics (or measures) for quantifying its connectivity, and consequently its ability to answer complex queries. The authors demonstrate the behaviour of these metrics in the context of a real and operational semantic warehouse, as well as on synthetically produced warehouses. The proposed metrics allow someone to get an overview of the contribution (to the warehouse) of each source and to quantify the value of the entire warehouse. Consequently, these metrics can be used for advancing data/endpoint profiling and for this reason the authors use an extension of VoID (for making them publishable). Such descriptions can be exploited for dataset/endpoint selection in the context of federated search. In addition, the authors show how the metrics can be used for monitoring a semantic warehouse after each reconstruction reducing thereby the cost of quality checking, as well as for understanding its evolution over time.

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An Investigation of HTTP Header Information for Detecting Changes of Linked Open Data Sources

Cited by 5 publications

References 3 publications

Strategies for Efficiently Keeping Local Linked Open Data Caches Up-To-Date

Strategies for Efficiently Keeping Local Linked Open Data Caches Up-To-Date

Keeping linked open data caches up-to-date by predicting the life-time of RDF triples

Quantifying the Connectivity of a Semantic Warehouse and Understanding its Evolution over Time

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