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

Mountantonakis, Michalis; Minadakis, Nikos; Marketakis, Yannis; Fafalios, Pavlos; Tzitzikas, Yannis

doi:10.4018/ijswis.2016070102

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…This indicates that the increase in distance leads bidders to lower the psychological price of the art; thus, distance diffusion causes bidders to lower the valuation of artwork. As funding is a dynamic process, the time factor would also impact the pledge [39].…”

Section: Distance Diffusion and Interpretationmentioning

confidence: 99%

Distance Diffusion of Home Bias for Crowdfunding Campaigns between Categories: Insights from Data Analytics

et al. 2018

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As the information communication and investment of crowdfunding campaigns are usually accomplished online, online crowdfunding theoretically breaks the limitation of space. However, investors' behaviors still show significant home bias; furthermore, from a dynamic view, the changes in home bias during the funding duration reflect the investment preference. This paper studies the dynamics of home bias in crowdfunding investment as well as the distance diffusion of home bias in the crowdfunding market through data mining and econometric models. The distance between investors and founders gradually increases from 3605 km to 4229 km as the funding progresses, and home bias shows different diffusion patterns between categories. For most of the campaigns, the distance between backers and founders of the successful campaigns is always greater than that of the failed ones; thus, the distance diffusion impacts the pledge results. However, the study also finds that for some categories, home bias does not play a positive role in investment or that it even shows negative impacts, among which food and technology are two extreme categories. The study provides a theoretical basis from the perspective of user behavior to analyze online investment and to improve the promotion of crowdfunding campaigns.

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Section: Distance Diffusion and Interpretationmentioning

confidence: 99%

Distance Diffusion of Home Bias for Crowdfunding Campaigns between Categories: Insights from Data Analytics

et al. 2018

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“…Finally, there are various methods that focus on monitoring the “health” of various RDF-based systems, e.g. [ 11 ] focuses on the connectivity monitoring in the context of a semantic warehouse over time, [ 7 ] focuses on monitoring Linked Data over a specific period of time, [ 2 ] focuses on measuring the dynamics of a specific RDF dataset, and [ 15 ] proposes a framework that identifies, analyses and understands such dynamics. SPARQLES [ 20 ] and SpEnD [ 22 ] focus on the monitoring of public SPARQL endpoints, DyKOSMap framework [ 3 ] adapts the mappings of Knowledge Organization Systems, as the data are modified over time.…”

Section: Related Work and Noveltymentioning

confidence: 99%

On the Evolution of Semantic Warehouses: The Case of Global Record of Stocks and Fisheries

Marketakis

Tzitzikas

Gentile

et al. 2021

Communications in Computer and Information Science

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Semantic Warehouses integrate data from various sources for offering a unified view of the data and enabling the answering of queries which cannot be answered by the individual sources. However, such semantic warehouses have to be refreshed periodically as the underlying datasets change. This is a challenging requirement, not only because the mappings and transformations that were used for constructing the semantic warehouse can be invalidated, but also because additional information (not existing in the initial datasets) may have been added in the semantic warehouse, and such information needs to be preserved after every reconstruction. In this paper we focus on this particular problem in a real setting: the Global Record of Stocks and Fisheries, a semantic warehouse that integrates data about stocks and fisheries from various information systems. We propose and detail a process that can tackle these requirements and we report our experiences from implementing it.

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“…The most connected triad of datasets, concerning classes, contains the following set of datasets (DBpedia, Opencyc [51], ImageSnippets [52]) with 188 common classes, while for the properties, the most connected triad includes (VIVO Wustl [53], FAO [54], VIVO scripps [55]) with 68 common properties. All the measurements for pairs, triads, and quads of subsets of datasets (in total 11,689,103 million subsets) for each different measurement type are accessible through LODsyndesis and datahub.io (http://datahub.io/dataset/connectivity-oflod-datasets), in CSV and RDF format, by using VoID-WH ontology [56], which is an extension of VoID ontology [57] (in total we have created 99,221,766 million triples). The most popular datasets.…”

Section: Connectivity Measurements For Lod Cloud Datasetsmentioning

confidence: 99%

High Performance Methods for Linked Open Data Connectivity Analytics

Mountantonakis

Tzitzikas

2018

Information

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The main objective of Linked Data is linking and integration, and a major step for evaluating whether this target has been reached, is to find all the connections among the Linked Open Data (LOD) Cloud datasets. Connectivity among two or more datasets can be achieved through common Entities, Triples, Literals, and Schema Elements, while more connections can occur due to equivalence relationships between URIs, such as owl:sameAs, owl:equivalentProperty and owl:equivalentClass, since many publishers use such equivalence relationships, for declaring that their URIs are equivalent with URIs of other datasets. However, there are not available connectivity measurements (and indexes) involving more than two datasets, that cover the whole content (e.g., entities, schema, triples) or "slices" (e.g., triples for a specific entity) of datasets, although they can be of primary importance for several real world tasks, such as Information Enrichment, Dataset Discovery and others. Generally, it is not an easy task to find the connections among the datasets, since there exists a big number of LOD datasets and the transitive and symmetric closure of equivalence relationships should be computed for not missing connections. For this reason, we introduce scalable methods and algorithms, (a) for performing the computation of transitive and symmetric closure for equivalence relationships (since they can produce more connections between the datasets); (b) for constructing dedicated global semantics-aware indexes that cover the whole content of datasets; and (c) for measuring the connectivity among two or more datasets. Finally, we evaluate the speedup of the proposed approach, while we report comparative results for over two billion triples.

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

Cited by 8 publications

References 35 publications

Distance Diffusion of Home Bias for Crowdfunding Campaigns between Categories: Insights from Data Analytics

Distance Diffusion of Home Bias for Crowdfunding Campaigns between Categories: Insights from Data Analytics

On the Evolution of Semantic Warehouses: The Case of Global Record of Stocks and Fisheries

High Performance Methods for Linked Open Data Connectivity Analytics

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