scholarly journals Dal Catalogo generale dei beni culturali al knowledge graph del patrimonio culturale italiano: il progetto ArCo

DigItalia ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 43-56
Author(s):  
Chiara Veninata
Keyword(s):  
Semantic Web ◽  
Knowledge Graph ◽  
Sparql Endpoint

Le attività dell’ICCD sono da sempre indirizzate ad una maggiore condivisione e valorizzazione sia dei modelli di strutturazione della conoscenza sul patrimonio culturale sia dei dati prodotti nelle campagne di catalogazione. Negli ultimi anni l’ICCD ha concentrato le proprie attività sull’analisi e sull’applicazione delle potenzialità offerte dal semantic web e dai suoi strumenti. Uno dei risultati è il progetto ArCo, il grafo della conoscenza del patrimonio culturale italiano, costituito da una rete di ontologie e da oltre 169 milioni di triple riferite a oltre a 800 mila schede catalografiche. ArCo si basa sui dati del Catalogo generale dei beni culturali dell’Istituto centrale per il catalogo e la documentazione del MiBACT e sui dati dei suoi archivi fotografici. ArCo è distribuito congiuntamente con uno SPARQL endpoint, un software per convertire i record di catalogo in RDF e una ricca suite di materiale di documentazione (test, valutazione, istruzioni, esempi ecc.).

scholarly journals WINFRA: A Web-Based Platform for Semantic Data Retrieval and Data Analytics

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2090
Author(s):  
Addi Ait-Mlouk ◽  
Xuan-Son Vu ◽  
Lili Jiang
Keyword(s):  
Semantic Web ◽  
Association Rules ◽  
Language Processing ◽  
Data Analytics ◽  
Data Retrieval ◽  
Heterogeneous Data ◽  
Graph Representation ◽  
Knowledge Graph ◽  
Multiple Sources ◽  
Semantic Data

Given the huge amount of heterogeneous data stored in different locations, it needs to be federated and semantically interconnected for further use. This paper introduces WINFRA, a comprehensive open-access platform for semantic web data and advanced analytics based on natural language processing (NLP) and data mining techniques (e.g., association rules, clustering, classification based on associations). The system is designed to facilitate federated data analysis, knowledge discovery, information retrieval, and new techniques to deal with semantic web and knowledge graph representation. The processing step integrates data from multiple sources virtually by creating virtual databases. Afterwards, the developed RDF Generator is built to generate RDF files for different data sources, together with SPARQL queries, to support semantic data search and knowledge graph representation. Furthermore, some application cases are provided to demonstrate how it facilitates advanced data analytics over semantic data and showcase our proposed approach toward semantic association rules.

Evaluating search features of Google Knowledge Graph and Bing Satori

2015 ◽  
Vol 39 (2) ◽  
pp. 197-213 ◽  
Author(s):  
Ahmet Uyar ◽  
Farouk Musa Aliyu
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Search Engines ◽  
Web Search ◽  
Semantic Search ◽  
Knowledge Graph ◽  
Content Type ◽  
Query Interfaces ◽  
Natural Language Query ◽  
Web Search Engines

Purpose – The purpose of this paper is to better understand three main aspects of semantic web search engines of Google Knowledge Graph and Bing Satori. The authors investigated: coverage of entity types, the extent of their support for list search services and the capabilities of their natural language query interfaces. Design/methodology/approach – The authors manually submitted selected queries to these two semantic web search engines and evaluated the returned results. To test the coverage of entity types, the authors selected the entity types from Freebase database. To test the capabilities of natural language query interfaces, the authors used a manually developed query data set about US geography. Findings – The results indicate that both semantic search engines cover only the very common entity types. In addition, the list search service is provided for a small percentage of entity types. Moreover, both search engines support queries with very limited complexity and with limited set of recognised terms. Research limitations/implications – Both companies are continually working to improve their semantic web search engines. Therefore, the findings show their capabilities at the time of conducting this research. Practical implications – The results show that in the near future the authors can expect both semantic search engines to expand their entity databases and improve their natural language interfaces. Originality/value – As far as the authors know, this is the first study evaluating any aspect of newly developing semantic web search engines. It shows the current capabilities and limitations of these semantic web search engines. It provides directions to researchers by pointing out the main problems for semantic web search engines.

scholarly journals Selected articles from the Fourth International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019)

2020 ◽  
Vol 20 (S4) ◽  
Author(s):  
Zhe He ◽  
Cui Tao ◽  
Jiang Bian ◽  
Rui Zhang
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
New Zealand ◽  
Semantic Web ◽  
International Workshop ◽  
Supplement Issue ◽  
Research Articles ◽  
Knowledge Graph

AbstractIn this introduction, we first summarize the Fourth International Workshop on Semantics-Powered Data Mining and Analytics (SEPDA 2019) held on October 26, 2019 in conjunction with the 18th International Semantic Web Conference (ISWC 2019) in Auckland, New Zealand, and then briefly introduce seven research articles included in this supplement issue, covering the topics on Knowledge Graph, Ontology-Powered Analytics, and Deep Learning.

scholarly journals Parser Extraction of Triples in Unstructured Text

2017 ◽  
Vol 5 (4) ◽  
pp. 143 ◽  
Author(s):  
Shaun D'Souza
Keyword(s):  
Semantic Web ◽  
Question Answering ◽  
Web Applications ◽  
Knowledge Graph ◽  
Depth First Search ◽  
Unstructured Text ◽  
The Web

The web contains vast repositories of unstructured text. We investigate the opportunity for building a knowledge graph from these text sources. We generate a set of triples which can be used in knowledge gathering and integration. We define the architecture of a language compiler for processing subject-predicate-object triples using the OpenNLP parser. We implement a depth-first search traversal on the POS tagged syntactic tree appending predicate and object information. A parser enables higher precision and higher recall extractions of syntactic relationships across conjunction boundaries. We are able to extract 2-2.5 times the correct extractions of ReVerb. The extractions are used in a variety of semantic web applications and question answering. We verify extraction of 50,000 triples on the ClueWeb dataset.

scholarly journals R2D2: A Dbpedia Chatbot Using Triple-Pattern Like Queries

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 217
Author(s):  
Haridimos Kondylakis ◽  
Dimitrios Tsirigotakis ◽  
Giorgos Fragkiadakis ◽  
Emmanouela Panteri ◽  
Alexandros Papadakis ◽  
...  
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Semantic Web Technologies ◽  
User Input ◽  
Web Technologies ◽  
Structured Input ◽  
Controlled Natural Language ◽  
Sparql Endpoint ◽  
Natural Language Interface ◽  
Available Information

Chatbots, also known as conversation agents, are programs that are able to simulate and reproduce an intelligent conversation with humans. Although this type of program is not new, the explosion of the available information and the rapid increase of the users seeking this information have renewed the interest in their development. In this paper, we present R2D2, an intelligent chatbot relying on semantic web technologies and offering an intelligent controlled natural language interface for accessing the information available in DBpedia. The chatbot accepts structured input, allowing users to enter triple-pattern like queries, which are answered by the underlying engine. While typing, an auto-complete service guides users on creating the triple patterns, suggesting resources available in the DBpedia. Based on user input (in the form of triple-pattern like queries), the corresponding SPARQL queries are automatically formulated. The queries are submitted to the corresponding DBpedia SPARQL endpoint, and then the result is received by R2D2 and augmented with maps and visuals and eventually presented to the user. The usability evaluation performed shows the advantages of our solution and its usefulness.

scholarly journals COVID-19 knowledge graph from semantic integration of biomedical literature and databases

2021 ◽  
Author(s):  
Chuming Chen ◽  
Karen E Ross ◽  
Sachin Gavali ◽  
Julie E Cowart ◽  
Cathy H Wu
Keyword(s):  
Semantic Web ◽  
Relevant Information ◽  
Semantic Integration ◽  
Biomedical Literature ◽  
Knowledge Graph ◽  
Biological Databases ◽  
Global Response ◽  
Semantic Web Technology ◽  
Federated Queries ◽  
Insight Into

Abstract Summary The global response to the COVID-19 pandemic has led to a rapid increase of scientific literature on this deadly disease. Extracting knowledge from biomedical literature and integrating it with relevant information from curated biological databases is essential to gain insight into COVID-19 etiology, diagnosis, and treatment. We used Semantic Web technology RDF to integrate COVID-19 knowledge mined from literature by iTextMine, PubTator, and SemRep with relevant biological databases and formalized the knowledge in a standardized and computable COVID-19 Knowledge Graph (KG). We published the COVID-19 KG via a SPARQL endpoint to support federated queries on the Semantic Web and developed a knowledge portal with browsing and searching interfaces. We also developed a RESTful API to support programmatic access and provided RDF dumps for download. Availability and implementation The COVID-19 Knowledge Graph is publicly available under CC-BY 4.0 license at https://research.bioinformatics.udel.edu/covid19kg/.

scholarly journals Creating RESTful APIs over SPARQL endpoints using RAMOSE

Semantic Web ◽  
2021 ◽  
pp. 1-19
Author(s):  
Marilena Daquino ◽  
Ivan Heibi ◽  
Silvio Peroni ◽  
David Shotton
Keyword(s):  
Semantic Web ◽  
Service Providers ◽  
Query Language ◽  
Sparql Query ◽  
Direct Access ◽  
Technical Solution ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Sparql Endpoint ◽  
Rdf Data

Semantic Web technologies are widely used for storing RDF data and making them available on the Web through SPARQL endpoints, queryable using the SPARQL query language. While the use of SPARQL endpoints is strongly supported by Semantic Web experts, it hinders broader use of RDF data by common Web users, engineers and developers unfamiliar with Semantic Web technologies, who normally rely on Web RESTful APIs for querying Web-available data and creating applications over them. To solve this problem, we have developed RAMOSE, a generic tool developed in Python to create REST APIs over SPARQL endpoints. Through the creation of source-specific textual configuration files, RAMOSE enables the querying of SPARQL endpoints via simple Web RESTful API calls that return either JSON or CSV-formatted data, thus hiding all the intrinsic complexities of SPARQL and RDF from common Web users. We provide evidence that the use of RAMOSE to provide REST API access to RDF data within OpenCitations triplestores is beneficial in terms of the number of queries made by external users of such RDF data using the RAMOSE API, compared with the direct access via the SPARQL endpoint. Our findings show the importance for suppliers of RDF data of having an alternative API access service, which enables its use by those with no (or little) experience in Semantic Web technologies and the SPARQL query language. RAMOSE can be used both to query any SPARQL endpoint and to query any other Web API, and thus it represents an easy generic technical solution for service providers who wish to create an API service to access Linked Data stored as RDF in a triplestore.

scholarly journals Visualisasi Pemain Sepak Bola Indonesia pada DBPedia dengan menggunakan Node2Vec dan Closeness Centrality

2020 ◽  
Vol 11 (2) ◽  
pp. 103
Author(s):  
Ardha Perwiradewa ◽  
Ahmad Naufal Rofiif ◽  
Nur Aini Rakhmawati
Keyword(s):  
Semantic Web ◽  
Soccer Player ◽  
Sparql Query ◽  
Closeness Centrality ◽  
The Internet ◽  
Football Players ◽  
Web Data ◽  
Large Graph ◽  
Sparql Endpoint ◽  
Centrality Score

Abstract. Visualization of Indonesian Football Players on DBpedia through Node2Vec and Closeness Centrality Implementation. Through Semantic Web, data available on the internet are connected in a large graph. Those data are still raw so that they need to be processed to be an information that can help humans. This research aims to process and analyze the Indonesian soccer player graph by implementing node2vec and closeness centrality algorithm. The graph is modeled through a dataset obtained from the DBpedia by performing a SPARQL query on the SPARQL endpoint. The results of the Node2vec algorithm and closeness centrality are visualized for further analysis. Visualization of node2vec shows that the defenders are distributed over the players. Meanwhile, the result of closeness centrality shows that the strikers have the highest centrality score compared to other positions.Keywords: visualization, node2vec, closeness centralityAbstrak. Dengan adanya web semantik, data yang tersebar di internet dapat saling terhubung dan membentuk suatu graf. Data yang ada pada graf tersebut masih berupa data mentah sehingga perlu dilakukan pengolahan agar data mentah tersebut dapat menjadi informasi yang dapat membantu manusia. Penelitian ini bertujuan untuk melakukan pengolahan dan analisis terhadap graf pemain sepak bola Indonesia dengan mengimplementasikan algoritma node2vec dan closeness centrality. Graf dimodelkan melalui dataset yang didapat dari website DBpedia dengan cara melakukan query SPARQL pada SPARQL endpoint. Hasil dari algoritma node2vec dan closeness centrality divisualisasikan untuk dianalisis. Visualisasi dari node2vec menunjukkan pemain defender tersebar. Hasil closeness centrality menunjukkan bahwa pemain striker memiliki nilai tertinggi daripada posisi lainnya.Kata Kunci: visualisasi, node2vec, closeness centrality

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