Evolving a rapid prototyping environment for visually and analytically exploring large-scale Linked Open Data

2011 ◽  
Author(s):  
Marc Downie ◽  
Paul Kaiser ◽  
Dylan Enloe ◽  
Peter Fox ◽  
James Hendler ◽  
...  
Keyword(s):  
Rapid Prototyping ◽  
Large Scale ◽  
Open Data ◽  
Linked Open Data

scholarly journals Robustifying Scholia: paving the way for knowledge discovery and research assessment through Wikidata

2019 ◽  
Vol 5 ◽  
Author(s):  
Lane Rasberry ◽  
Egon Willighagen ◽  
Finn Nielsen ◽  
Daniel Mietchen
Keyword(s):  
Knowledge Discovery ◽  
Large Scale ◽  
Knowledge Workers ◽  
Open Data ◽  
Linked Open Data ◽  
Data Curation ◽  
Research Assessment ◽  
Knowledge Graph ◽  
Incomplete Datasets ◽  
Active Contributor

Knowledge workers like researchers, students, journalists, research evaluators or funders need tools to explore what is known, how it was discovered, who made which contributions, and where the scholarly record has gaps. Existing tools and services of this kind are not available as Linked Open Data, but Wikidata is. It has the technology, active contributor base, and content to build a large-scale knowledge graph for scholarship, also known as WikiCite. Scholia visualizes this graph in an exploratory interface with profiles and links to the literature. However, it is just a working prototype. This project aims to "robustify Scholia" with back-end development and testing based on pilot corpora. The main objective at this stage is to attain stability in challenging cases such as server throttling and handling of large or incomplete datasets. Further goals include integrating Scholia with data curation and manuscript writing workflows, serving more languages, generating usage stats, and documentation.

A Compact Hybrid Evolutionary Algorithm for Large Scale Instance Matching in Linked Open Data Cloud

2017 ◽  
Vol 26 (04) ◽  
pp. 1750013 ◽  
Author(s):  
Xingsi Xue ◽  
Jianhua Liu
Keyword(s):  
Evolutionary Algorithm ◽  
Large Scale ◽  
Open Data ◽  
Linked Open Data ◽  
Matching Task ◽  
Matching Problem ◽  
Matching Problems ◽  
Hybrid Evolutionary Algorithm ◽  
Instance Matching ◽  
Real World Datasets

Establishing correct links among the coreference ontology instances is critical to the success of Linked Open Data (LOD) cloud. However, because of the high level heterogeneity and large scale instance set, matching the coreference instances in LOD cloud is an error prone and time consuming task. To this end, in this work, we present an asymmetrical profile-based similarity measure for instance matching task, construct new optimal models for schema-level and instance-level matching problems, and propose a compact hybrid evolutionary algorithm based ontology matching approach to solve the large scale instance matching problem in LOD cloud. Finally, the experimental results of comprising our approach with the states of the art systems on the instance matching track of OAEI 2015 and real-world datasets show the effectiveness of our approach.

scholarly journals Early Steps Towards Web Scale Information Extraction with LODIE

AI Magazine ◽  
2015 ◽  
Vol 36 (1) ◽  
pp. 55-64 ◽  
Author(s):  
Anna Lisa Gentile ◽  
Ziqi Zhang ◽  
Fabio Ciravegna
Keyword(s):  
Information Extraction ◽  
Information Needs ◽  
Large Scale ◽  
Open Data ◽  
Linked Open Data ◽  
Extraction Techniques ◽  
Wrapper Induction ◽  
Textual Data ◽  
Core Idea ◽  
Structured Representation

Information extraction (IE) is the technique for transforming unstructured textual data into structured representation that can be understood by machines. The exponential growth of the Web generates an exceptional quantity of data for which automatic knowledge capture is essential. This work describes the methodology for web scale information extraction in the LODIE project (linked open data information extraction) and highlights results from the early experiments carried out in the initial phase of the project. LODIE aims to develop information extraction techniques able to scale at web level and adapt to user information needs. The core idea behind LODIE is the usage of linked open data, a very large-scale information resource, as a ground-breaking solution for IE, which provides invaluable annotated data on a growing number of domains. This article has two objectives. First, describing the LODIE project as a whole and depicting its general challenges and directions. Second, describing some initial steps taken towards the general solution, focusing on a specific IE subtask, wrapper induction.

Building up Japanese Vocabulary for Large Scale Linked Open Data

2014 ◽  
Vol 29 (4) ◽  
pp. 386-395
Author(s):  
Susumu Tamagawa ◽  
Kosuke Kagawa ◽  
Takeshi Morita ◽  
Takahira Yamaguchi
Keyword(s):  
Large Scale ◽  
Open Data ◽  
Linked Open Data

Europeana no Linked Open Data: conceitos de Web Semântica na dimensão aplicada das humanidades digitais

2017 ◽  
Vol 12 (2) ◽  
Author(s):  
Caio Saraiva Coneglian ◽  
José Eduardo Santarem Segundo
Keyword(s):  
Linked Data ◽  
Open Data ◽  
Linked Open Data

O surgimento de novas tecnologias, tem introduzido meios para a divulgação e a disponibilização das informações mais eficientemente. Uma iniciativa, chamada de Europeana, vem promovendo esta adaptação dos objetos informacionais dentro da Web, e mais especificamente no Linked Data. Desta forma, o presente estudo tem como objetivo apresentar uma discussão acerca da relação entre as Humanidades Digitais e o Linked Open Data, na figura da Europeana. Para tal, utilizamos uma metodologia exploratória e que busca explorar as questões relacionadas ao modelo de dados da Europeana, EDM, por meio do SPARQL. Como resultados, compreendemos as características do EDM, pela utilização do SPARQL. Identificamos, ainda, a importância que o conceito de Humanidades Digitais possui dentro do contexto da Europeana.Palavras-chave: Web semântica. Linked open data. Humanidades digitais. Europeana. EDM.Link: https://periodicos.ufsc.br/index.php/eb/article/view/1518-2924.2017v22n48p88/33031

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