![]() The Web of Data shows the potential for being extended to a truly multilingual web as vocabularies and data can be published in a language-independent fashion, while associated language-dependent (linguistic) information supporting the access across languages can be stored separately. This leads to situations in which data expressed in a certain language is not easily accessible to speakers of other languages. Indeed, although the Web of Data can contain any kind of information in any language, it still lacks explicit mechanisms to automatically reconcile such information when it is expressed in different languages. Although this brings us a big step closer to the vision of a Semantic Web, it also raises new issues such as the need for dealing with information expressed in different natural languages. This growth has been stimulated to a large extent by the emergence of Linked Data. The Web has witnessed an enormous growth in the amount of semantic information published in recent years. Intrinsic and extrinsic evaluations show that this is a high quality resource and beneficial to various NLP tasks. The result is a network containing approximately 3.7 million concepts with lexicalizations in numerous languages and 49+ million relation instances. We then exploit this gained information to discover new relations that refine some of those found in the previous step. Working on top of this network allows for added information to influence the network and be propagated throughout it using inference mechanisms that connect different pieces of existing knowledge. We show how we can build upon WikipediaŹ¼s existing network of categories and articles to automatically discover new relations and their instances. This paper describes an approach to deriving such a large scale and multilingual resource by exploiting several facets of the on-line encyclopedia Wikipedia. Our proposal aims to overcome the language barrier, and connect not only texts across languages, but also images, videos, speech and sound, and logical formulas, across many fields of AI.A knowledge base for real-world language processing applications should consist of a large base of facts and reasoning mechanisms that combine them to induce novel and more complex information. Indeed, we tackle their key limits by fully abstracting text into meaning and introducing language-independent concepts and semantic relations, in order to obtain an interlingual representation. Through this paper, we aim to revamp the historical dream of AI, by putting forward a novel, all-embracing, fully semantic meaning representation, that goes beyond the many existing formalisms. ![]() Even today, at the core of Natural Language Understanding lies the task of Semantic Parsing, the objective of which is to convert natural sentences into machine-readable representations. Conceptual representations of meaning have long been the general focus of Artificial Intelligence (AI) towards the fundamental goal of machine understanding, with innumerable efforts made in Knowledge Representation, Speech and Natural Language Processing, Computer Vision, inter alia.
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