scholarly journals Charaterizing RDF graphs through graph-based measures – framework and assessment

Semantic Web ◽  
2020 ◽  
pp. 1-24
Author(s):  
Matthäus Zloch ◽  
Maribel Acosta ◽  
Daniel Hienert ◽  
Stefan Conrad ◽  
Stefan Dietze
Keyword(s):  
Topological Structure ◽  
Open Data ◽  
Descriptive Statistics ◽  
Minimal Set ◽  
Knowledge Domains ◽  
Cross Domain ◽  
Hierarchical Relations ◽  
Social Graphs ◽  
Essential Set ◽  
Rdf Graphs

The topological structure of RDF graphs inherently differs from other types of graphs, like social graphs, due to the pervasive existence of hierarchical relations (TBox), which complement transversal relations (ABox). Graph measures capture such particularities through descriptive statistics. Besides the classical set of measures established in the field of network analysis, such as size and volume of the graph or the type of degree distribution of its vertices, there has been some effort to define measures that capture some of the aforementioned particularities RDF graphs adhere to. However, some of them are redundant, computationally expensive, and not meaningful enough to describe RDF graphs. In particular, it is not clear which of them are efficient metrics to capture specific distinguishing characteristics of datasets in different knowledge domains (e.g., Cross Domain vs. Linguistics). In this work, we address the problem of identifying a minimal set of measures that is efficient, essential (non-redundant), and meaningful. Based on 54 measures and a sample of 280 graphs of nine knowledge domains from the Linked Open Data Cloud, we identify an essential set of 13 measures, having the capacity to describe graphs concisely. These measures have the capacity to present the topological structures and differences of datasets in established knowledge domains.

scholarly journals Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data

2021 ◽  
Vol 13 (5) ◽  
pp. 124
Author(s):  
Jiseong Son ◽  
Chul-Su Lim ◽  
Hyoung-Seop Shim ◽  
Ji-Sun Kang
Keyword(s):  
Artificial Intelligence ◽  
Domain Knowledge ◽  
Open Data ◽  
Heterogeneous Data ◽  
Big Data Analysis ◽  
Knowledge Graph ◽  
Cross Domain ◽  
Disaster Data ◽  
Knowledge Graphs ◽  
Open Datasets

Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.

Minimal Sets on Graphs and Dendrites

2003 ◽  
Vol 13 (07) ◽  
pp. 1721-1725 ◽  
Author(s):  
Francisco Balibrea ◽  
Roman Hric ◽  
L'ubomír Snoha
Keyword(s):  
Topological Structure ◽  
Partial Characterization ◽  
Minimal Set ◽  
Full Characterization ◽  
Minimal Sets ◽  
Continuous Maps

The topological structure of minimal sets of continuous maps on graphs, dendrites and dendroids is studied. A full characterization of minimal sets on graphs and a partial characterization of minimal sets on dendrites are given. An example of a minimal set containing an interval on a dendroid is given.

Natural Language Generation from Graphs

2014 ◽  
Vol 08 (03) ◽  
pp. 335-384 ◽  
Author(s):  
Ngan T. Dong ◽  
Lawrence B. Holder
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Web Search ◽  
Open Data ◽  
Natural Language Generation ◽  
Language Generation ◽  
Expert User ◽  
Computer Representation ◽  
Description Framework ◽  
Rdf Graphs

The Resource Description Framework (RDF) is the primary language to describe information on the Semantic Web. The deployment of semantic web search from Google and Microsoft, the Linked Open Data Community project along with the announcement of schema.org by Yahoo, Bing and Google have significantly fostered the generation of data available in RDF format. Yet the RDF is a computer representation of data and thus is hard for the non-expert user to understand. We propose a Natural Language Generation (NLG) engine to generate English text from a small RDF graph. The Natural Language Generation from Graphs (NLGG) system uses an ontology skeleton, which contains hierarchies of concepts, relationships and attributes, along with handcrafted template information as the knowledge base. We performed two experiments to evaluate NLGG. First, NLGG is tested with RDF graphs extracted from four ontologies in different domains. A Simple Verbalizer is used to compare the results. NLGG consistently outperforms the Simple Verbalizer in all the test cases. In the second experiment, we compare the effort spent to make NLGG and NaturalOWL work with the M-PIRO ontology. Results show that NLGG generates acceptable text with much smaller effort.

Determining the Impact of Eric Clapton on Music Using RDF Graphs

2011 ◽  
Author(s):  
Ronald P. Reck ◽  
Kenneth B. Sall ◽  
Wendy A. Swanbeck
Keyword(s):  
Rock Music ◽  
Open Data ◽  
Large Datasets ◽  
Data Sources ◽  
Semantic Technology ◽  
Public Dataset ◽  
Primary Focus ◽  
Rdf Graphs ◽  
The Impact

As music is a topic of interest to many, it is no surprise that developers have applied web and semantic technology to provide various RDF datasets for describing relationships among musical artists, albums, songs, genres, and more. As avid fans of blues and rock music, we wondered if we could construct SPARQL queries to examine properties and relationships between performers in order to answer global questions such as "Who has had the greatest impact on rock music?" Our primary focus was Eric Clapton, a musical artist with a decades-spanning career who has enjoyed both a very successful solo career as well as having performed in several world-renowned bands. The application of semantic technology to a public dataset can provide useful insights into how similar approaches can be applied to realistic domain problems, such as finding relationships between persons of interest. Clearly understood semantics of available RDF properties in the dataset is of course crucial but is a substantial challenge especially when leveraging information from similar yet different data sources. This paper explores the use of DBpedia and MusicBrainz data sources using OpenLink Virtuoso Universal Server with a Drupal frontend. Much attention is given to the challenges we encountered, especially with respect to relatively large datasets of community-entered open data sources of varying quality and the strategies we employed or recommend to overcome the challenges.

scholarly journals Assessing Large-Scale, Cross-Domain Knowledge Bases for Semantic Search

2020 ◽  
Vol 39 (3) ◽  
pp. 595-602
Author(s):  
Aatif Ahmad Khan ◽  
Sanjay Kumar Malik
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
Open Data ◽  
Knowledge Bases ◽  
Semantic Search ◽  
Semantic Web Technologies ◽  
Data Format ◽  
Web Technologies ◽  
Cross Domain ◽  
Description Framework

Semantic Search refers to set of approaches dealing with usage of Semantic Web technologies for information retrieval in order to make the process machine understandable and fetch precise results. Knowledge Bases (KB) act as the backbone for semantic search approaches to provide machine interpretable information for query processing and retrieval of results. These KB include Resource Description Framework (RDF) datasets and populated ontologies. In this paper, an assessment of the largest cross-domain KB is presented that are exploited in large scale semantic search and are freely available on Linked Open Data Cloud. Analysis of these datasets is a prerequisite for modeling effective semantic search approaches because of their suitability for particular applications. Only the large scale, cross-domain datasets are considered, which are having sizes more than 10 million RDF triples. Survey of sizes of the datasets in triples count has been depicted along with triples data format(s) supported by them, which is quite significant to develop effective semantic search models.

scholarly journals Cyber–Physical Active Distribution Networks Robustness Evaluation against Cross-Domain Cascading Failures

2019 ◽  
Vol 9 (23) ◽  
pp. 5021 ◽  
Author(s):  
Sun ◽  
Dong ◽  
Wang ◽  
Lv ◽  
War
Keyword(s):  
Failure Rate ◽  
Topological Structure ◽  
Average Power ◽  
Distribution Networks ◽  
Node Degree ◽  
Cascading Failures ◽  
Initial Failure ◽  
Distributed Generators ◽  
Cross Domain ◽  
Active Distribution Networks

Active distribution networks (ADNs) are a typical cyber–physical system (CPS), which consist of two kinds of interdependent sub-networks: power networks (PNs) and communication networks (CNs). The combination of typical characteristics of the ADN includes (1) a large number of distributed generators contained in the PN, (2) load redistribution in both the PN and CN, and (3) strong interdependence between the PN and CN, which makes ADNs vulnerable to cross-domain cascading failures (CCFs). In this paper, we focus on the robustness analysis of the ADN against the CCF. Rather than via the rate of the clusters with size greater than a predefined threshold, we evaluate the robustness of the ADN using the rate of the clusters containing generators after the CCF. Firstly, a synchronous probabilistic model is derived to calculate the proportions of remaining normal operational nodes after the CCF. With this model, the propagation of the CCF in the ADN can be described as recursive equations. Secondly, we analyze the relationship between the proportions of remaining normal operational nodes after the CCF and the distribution of distributed generators, unintentional random initial failure rate, the interdependence between the sub-networks, network topology, and tolerance parameters. Some results are revealed which include (1) the more distributed generators the PN contains, the higher ADN robustness is, (2) the robustness of the ADN is negatively correlated with the unintentional random initial failure rate, (3) the robustness of the ADN can be improved by increasing the average control fan in of each node in the PN and the average power fan in of each node in the CN, (4) the robustness of the ADN with Erdos–Renyi (ER) network topological structure is greater than that with Barabasi–Albert (BA) network topological structure under the same average node degree, and (5) the robustness of the ADN is greater, when the tolerance parameters increase. Lastly, some simulation experiments are conducted and experimental results also demonstrate that the conclusions above are effective to improve the robustness of the ADN against the CCF.

scholarly journals Performance Based Abstraction of Biomimicry Design Principles using Prototyping

Designs ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 38 ◽  
Author(s):  
Erin Rovalo ◽  
John McCardle
Keyword(s):  
Performance Evaluation ◽  
Design Principles ◽  
Design Practice ◽  
Form Finding ◽  
Knowledge Domains ◽  
Cross Domain ◽  
Collaborative Performance ◽  
Semantic Techniques ◽  
Depth Interviews ◽  
Unique Context

A key challenge faced by biomimicry practitioners is making the conceptual leap between biology and design, particularly regarding collaborating across these knowledge domains and developing and evaluating design principles abstracted from biology. While many tools and resources to support biomimicry design exist, most largely rely on semantic techniques supporting analogical translation of information between biology and design. However, the challenges of evaluation and collaboration are common in design practice and frequently addressed through prototyping. This study explores the utility of prototyping in the unique context of biomimicry by investigating its impact on the abstraction and transfer of design principles derived from biology as well as on cross-domain collaboration between biologists and designers. Following a survey exploring current practices of practitioners, in depth interviews provided detailed accounts of project experiences that leveraged prototyping. Four primary themes were observed: (1) Approximation; (2) The Prototyping Principle; (3) Synthesis and Testing; and (4) Validation. These themes introduce a unique abstraction and transfer process based on form-finding and collaborative performance evaluation in contrast to the widely accepted semantic language-based approaches. Our findings illustrate how designers and engineers can leverage a prototyping skillset in order to develop boundary objects between the fields of biology and design to navigate challenges uniquely associated with the biomimicry approach.

Combining Linked Open Data Similarity and Relatedness for Cross OSN Recommendation

2020 ◽  
Vol 16 (2) ◽  
pp. 59-90
Author(s):  
Mohamed Boubenia ◽  
Abdelkader Belkhir ◽  
Fayçal M'hamed Bouyakoub
Keyword(s):  
Social Networks ◽  
Recommender Systems ◽  
Similarity Measure ◽  
Online Social Networks ◽  
Experimental Evaluation ◽  
Open Data ◽  
Linked Open Data ◽  
Cross Domain ◽  
Data Similarity ◽  
New Generation

The emergence of online social networks (OSNs) and linked open data (LOD) bring up opportunities to experiment on a new generation of cross-domain recommender systems in which the true benefit of LOD can be exploited, particularly to address the new user problems. In this article, the authors explore the feasibility of combining the two axes of comparison, similarity and relatedness, in LOD space, and introduce a new LOD-based similarity measure. The reason is to take benefit more from LOD to compare general resources, which can be useful in the context of cross-OSN recommendation. Experimental evaluation demonstrates the effectiveness of the proposed approach.

Forecasting Price Trend of Bulk Commodities Leveraging Cross-domain Open Data Fusion

2020 ◽  
Vol 11 (1) ◽  
pp. 1-26
Author(s):  
Binbin Zhou ◽  
Sha Zhao ◽  
Longbiao Chen ◽  
Shijian Li ◽  
Zhaohui Wu ◽  
...  
Keyword(s):  
Data Fusion ◽  
Open Data ◽  
Price Trend ◽  
Cross Domain
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