taxonomical information: generalizations (i.e., more general concepts) and specializations (i.e.definitions (often multiple) in each language.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.Įach meaning comes with a wealth of information, including:

Roberto Navigli’s lab at the Sapienza University of Rome. It greatly enhances BabelNet®, the award-winning multilingual semantic network, thanks to the know-how of years of research in computational linguistics in Prof. WordAtlas is the next-generation multilingual knowledge graph. They organize knowledge into a coherent network of meanings and they enable Artificial Intelligence applications which exploit this knowledge to perform text understanding. These two stages are mutually beneficial, progressively enriching less-resourced languages and contributing towards leveling the playing field for all languages.Knowledge graphs are the 21st century counterpart of dictionaries in previous centuries. In stage 2, we will leverage this resource to develop innovative algorithms that perform semantic parsing in any language. Roberto Navigli, Rexhina Blloshmi, Abelardo Carlos Martnez. In stage 1 of the project we will acquire a huge network of language-independent, structured semantic representations of sentences. BabelNet Meaning Representation: A Fully Semantic Formalism to Overcome Language Barriers. The key intuitions of our proposal are treating multilinguality as a resource rather than an obstacle and embracing the knowledge-based paradigm which allows supervision in the machine learning sense to be replaced with efficacious use of lexical knowledge resources. MOUSSE proposes a research program to investigate radically new directions for enabling multilingual semantic parsing, without the heavy requirement of annotating training data for each new language. However, current semantic parsers require supervision, binding them to the language of interest and hindering their extension to multiple languages. This aims at creating structured representations of meaning for an input text. To tackle this long-lasting challenge in Natural Language Processing (NLP), the task of semantic parsing has recently gained popularity. But, in order to ”read” it, that is perform machine reading, they still have to face the hard problem of Natural Language Understanding, i.e., automatically making sense of human language. And even if we had time to read all the Web we could not understand it, as most of it is written in languages we do not speak.Ĭomputers, instead, have the power to process the entire Web. BabelNet was automatically created by linking Wikipedia to the most popular computational lexicon of the English language, WordNet. BabelNet is used to automatically recognize and disambiguate concepts in a piece of unstructured text. In this paper we leveraged two valuable knowledge bases in the task of information extraction. In order to convert BabelNet data into RDF format we need to: Read the original data: BabelNet’s data, originally stored within Lucene indexes, were accessed through BabelNet’s API and translated into RDF triples through the Jena API. Using linked data in real world applications is a hot topic in the field of Information Retrieval. However, the information expressed therein is not at easy reach: what we typically browse is only an infinitesimal part of the Web. BabelNet is a multilingual lexicalized semantic network and ontology developed at the NLP group of the Sapienza University of Rome. Linked Data generation process Technical details. The exponential growth of the Web is resulting in vast amounts of online content.

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