Pesquisa de Doutorado da Veronica

MAKG is MAG provisioning as RDF knowledge graph, both in the form of RDF files ( N-Triples format ) and as a data source on the Web, through a SPARQL EndPoint , with HTTP-resolvable URIs. It was enriched by reusing common vocabularies, resources  are linked to  other  data  sources  on  the  Web,  such  as  DBpedia, Wikidata, OpenCitations, and the Global Research Identifier Database (GRID). It was classified as 5-star according to Tim Berners-Lee’s  deployment scheme for Open Data Data set is licensed under the Open Data Commons Attribution License ( ODC-By ). All relevant data to be modeled in RDF takes about 350 GB of disk space ( input ).  MAG dump of November 2018 8,272,187,245 RDF triples 1.2 TB of disk space for the uncompressed RDF files ( output ) Virtuoso : Indexing the data requires about 514 GB of disk space and takes about 10 hours ; 256 GB of RAM. On the schema level, the MAKG contains 47 properties and 13 entity types (with 8 entity types being in the name

AI technologies natural language understanding,including Entity Recognition and Desambiguation (ERD) and concept detection, in extracting factoids from individual articles at the web scale,  knowledge assisted inference and reasoning in assembling the factoids into a knowledge graph (MAG), and  a reinforcement learning approach to assessing scholarly importance for entities participating in scholarly communications, through a probabilistic measure called the saliency, that serves both as an analytic and a predictive metric in MAS. Challenge in the study of science of science is the explosive growth in the volume of scientific reports and the diversity of research topics. These have outstripped the cognitive capacity of human beings to properly digest and catch up.  Microsoft Academic Services (MAS) consists of three parts:  an open dataset known as Microsoft Academic Graph (MAG),  a freely available inference engine called Microsoft Academic Knowledge Exploration Service (MAKES

Zhihong Shen, Hao Ma, Kuansan Wang: A Web-scale system for scientific knowledge exploration.   Association for Computational Linguistics (ACL) 2018: 87-92 identify hundreds of thousands of scientific concepts, tag these identified concepts to hundreds of millions of scientific publications by leveraging both text and graph structure, and build a six-level concept hierarchy with a subsumption-based model. Contribution: A cross-domain scientific concept (concepts or fields-of-study) ontology published to date, with more than 200 thousand concepts and over one million relationships. Scalability : Traditionally, academic discipline and concept taxonomies have been curated manually on a scale of hundreds or thousands, which is insufficient in modeling the richness of academic concepts across all domains. Consequently, the low concept coverage also limits the exploration experience of hundreds of millions of scientific publications. Passo 1 We formulate concept discovery as a knowledge b