Synset Construction - Extending from Concepts Association of HNC to Lexical Semantic Web

2013 ◽  
Vol 756-759 ◽  
pp. 2064-2067
Author(s):  
Li Feng ◽  
Yi Qun Zhang
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Semantic Network ◽  
Theoretical Framework ◽  
Lexical Semantic

HNC designs a theoretical framework for machine to understand the meaning of natural language and offers different ways to represent concepts. We use Synset-Lexeme Anamorphosis Method to enrich the framework. It aims to reach an effective connection between HNC and lexical semantic network to make the current Semantic Web more complete and perfect.

scholarly journals Ten Years of BabelNet: A Survey

2021 ◽  
Author(s):  
Roberto Navigli ◽  
Michele Bevilacqua ◽  
Simone Conia ◽  
Dario Montagnini ◽  
Francesco Cecconi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Semantic Network ◽  
Semantic Knowledge ◽  
Desirable Property ◽  
Lexical Semantic ◽  
Wide Range ◽  
Lexical Semantic Knowledge ◽  
Multiple Languages

The intelligent manipulation of symbolic knowledge has been a long-sought goal of AI. However, when it comes to Natural Language Processing (NLP), symbols have to be mapped to words and phrases, which are not only ambiguous but also language-specific: multilinguality is indeed a desirable property for NLP systems, and one which enables the generalization of tasks where multiple languages need to be dealt with, without translating text. In this paper we survey BabelNet, a popular wide-coverage lexical-semantic knowledge resource obtained by merging heterogeneous sources into a unified semantic network that helps to scale tasks and applications to hundreds of languages. Over its ten years of existence, thanks to its promise to interconnect languages and resources in structured form, BabelNet has been employed in countless ways and directions. We first introduce the BabelNet model, its components and statistics, and then overview its successful use in a wide range of tasks in NLP as well as in other fields of AI.

scholarly journals From Natural Language to RDF Graphs with Pregroups

2017 ◽  
Author(s):  
Antonin Delpeuch ◽  
Anne Preller
Keyword(s):  
Side Effects ◽  
Semantic Web ◽  
Natural Language ◽  
Relative Clauses ◽  
Semantic Category ◽  
Target Language ◽  
Pregroup Grammars ◽  
Unbounded Dependencies ◽  
Efficient Type ◽  
Rdf Graphs

We define an algorithm translating natural language sentences to the formal syntax of RDF, an existential conjunctive logic widely used on the Semantic Web. Our translationis based on pregroup grammars, an efficient type-logical grammatical framework with atransparent syntax-semantics interface. We introduce a restricted notion of side effects inthe semantic category of finitely generated free semimodules over {0,1} to that end.The translation gives an intensional counterpart to previous extensional models.We establish a one-to-one correspondence between extensional models and RDF models such that satisfaction is preserved. Our translation encompasses the expressivity of the target language and supports complex linguistic constructions like relative clauses and unbounded dependencies.

Geocoding Tweets Based on Semantic Web and Ontologies

2018 ◽  
pp. 372-392
Author(s):  
Imelda Escamilla ◽  
Miguel Torres Ruíz ◽  
Marco Moreno Ibarra ◽  
Vladimir Luna Soto ◽  
Rolando Quintero ◽  
...  
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Mexico City ◽  
Spatial Relationships ◽  
Spatial Analyses ◽  
Human Ability ◽  
Share Information

Human ability to understand approximate references to locations, disambiguated by means of context and reasoning about spatial relationships, is the key to describe spatial environments and to share information about them. In this paper, we propose an approach for geocoding that takes advantage of the spatial relationships contained in the text of tweets, using semantic web, ontologies and spatial analyses. Microblog text has special characteristics (e.g. slang, abbreviations, acronyms, etc.) and thus represents a special variation of natural language. The main objective of this work is to associate spatial relationships found in text with a spatial footprint, to determine the location of the event described in the tweet. The feasibility of the proposal is demonstrated using a corpus of 200,000 tweets posted in Spanish related with traffic events in Mexico City.

Web knowledge

2006 ◽  
Author(s):  
Christopher Walton
Keyword(s):  
Semantic Web ◽  
Knowledge Representation ◽  
Knowledge Base ◽  
Web Applications ◽  
Semantic Network ◽  
Knowledge Bases ◽  
Web Standards ◽  
Description Framework ◽  
The Web

In the introductory chapter of this book, we discussed the means by which knowledge can be made available on the Web. That is, the representation of the knowledge in a form by which it can be automatically processed by a computer. To recap, we identified two essential steps that were deemed necessary to achieve this task: 1. We discussed the need to agree on a suitable structure for the knowledge that we wish to represent. This is achieved through the construction of a semantic network, which defines the main concepts of the knowledge, and the relationships between these concepts. We presented an example network that contained the main concepts to differentiate between kinds of cameras. Our network is a conceptualization, or an abstract view of a small part of the world. A conceptualization is defined formally in an ontology, which is in essence a vocabulary for knowledge representation. 2. We discussed the construction of a knowledge base, which is a store of knowledge about a domain in machine-processable form; essentially a database of knowledge. A knowledge base is constructed through the classification of a body of information according to an ontology. The result will be a store of facts and rules that describe the domain. Our example described the classification of different camera features to form a knowledge base. The knowledge base is expressed formally in the language of the ontology over which it is defined. In this chapter we elaborate on these two steps to show how we can define ontologies and knowledge bases specifically for the Web. This will enable us to construct Semantic Web applications that make use of this knowledge. The chapter is devoted to a detailed explanation of the syntax and pragmatics of the RDF, RDFS, and OWL Semantic Web standards. The resource description framework (RDF) is an established standard for knowledge representation on the Web. Taken together with the associated RDF Schema (RDFS) standard, we have a language for representing simple ontologies and knowledge bases on the Web.

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