A huge multilingual knowledge graph
Knowledge graphs are the 21st century counterpart of dictionaries in previous centuries. They organize knowledge into a coherent network of meanings and they enable Artificial Intelligence applications which exploit this knowledge to perform text understanding.
WordAtlas is the next-generation multilingual knowledge graph. It greatly enhances BabelNet®, the award-winning multilingual semantic network, thanks to the know-how of years of research in computational linguistics in Prof. Roberto Navigli’s lab at the Sapienza University of Rome.
What makes WordAtlas special is its linkage between concepts and words in hundreds of languages: WordAtlas provides millions of lexicalizations for each language, from common nouns, adjectives, verbs and adverbs, to hundreds of thousands of technical terms and millions of named entities, such as people, locations, organizations and products.
Knowledge graph APIs
WordAtlas comes with high-performance API for Python and Java (therefore supporting all JVM-based languages, such as Kotlin, Scala and Groovy). The API enables access to the multilingual knowledge graph and includes a wide range of methods for:
searching by word and concept id;
retrieving information about concepts and entities, such as multilingual lexicalizations (synonyms), definitions, examples, semantic relations (e.g. hypernymy, meronymy, relatedness, born-in, etc.), links to images and much more;
support of functional programming, enabling the use of lambda functions for querying and retrieval purposes.
The Python API mimics the organization of the Java API, therefore making it easy to switch from one to the other. The APIs support both online and offline querying of WordAtlas and come with professional support.
If you are a Ph.D. student or a researcher affiliated with a research institution and you need to use the BabelNet indices for your non-commercial research project, please make a request for downloading the indices through the Sapienza NLP resource repository.
IMPORTANT: The email used in your BabelNet account registration must belong to your research institution.
NOTE: Each request is subject to approval and fulfillment is based on the above conditions.
https://babelnet.org/downloads
https://babelscape.com/wordatlas#api
https://babelnet.org/how-to-use
Extraído de -> https://www.lncc.br/~ziviani/papers/Texto-MC1-SBBD2019.pdf Aprendizado de máquina relacional (AMR) destina-se à criação de modelos estatísticos para dados relacionais (seria o mesmo que dados conectados) , isto é, dados cuja a informação relacional é tão ou mais impor tante que a informação individual (atributos) de cada elemento. Essa classe de aprendizado tem sido utilizada em diversas aplicações, por exemplo, na extração de informação de dados não estruturados [Zhang et al. 2016] e na modelagem de linguagem natural [Vu et al. 2018]. A adoção de técnicas de aprendizado de máquina relacional em tarefas de comple mentação de grafo de conhecimento se baseia na premissa de existência de regularidades semânticas presentes no mesmo . Modelos grafos probabilísticos Baseada em regras / heurísticas que não podem garantir 100% de precisão no resultado da inferência mas os resultados podem ser explicados. Modelos de características de ...
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