167 research outputs found

    Knowledge Representation and WordNets

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    Knowledge itself is a representation of “real facts”. Knowledge is a logical model that presents facts from “the real world” witch can be expressed in a formal language. Representation means the construction of a model of some part of reality. Knowledge representation is contingent to both cognitive science and artificial intelligence. In cognitive science it expresses the way people store and process the information. In the AI field the goal is to store knowledge in such way that permits intelligent programs to represent information as nearly as possible to human intelligence. Knowledge Representation is referred to the formal representation of knowledge intended to be processed and stored by computers and to draw conclusions from this knowledge. Examples of applications are expert systems, machine translation systems, computer-aided maintenance systems and information retrieval systems (including database front-ends).knowledge, representation, ai models, databases, cams

    The Role of E-Vocabularies in the Description and Retrieval of Digital Educational Resources

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    Vocabularies are linguistic resources that make it possible to access knowledge through words. They can constitute a mechanism to identify, describe, explore, and access all the digital resources with informational content pertaining to a specific knowledge domain. In this regard, they play a key role as systems for the representation and organization of knowledge in environments in which content is created and used in a collaborative and free manner, as is the case of social wikis and blogs on the Internet or educational content in e-learning environments. In e-learning environments, electronic vocabularies (e-vocabularies) constitute a mechanism for conceptual representation of digital educational resources. They enable human and software agents either to locate and interpret resource content in large digital repositories, including the web, or to use them (vocabularies) as an educational resource by itself to learn a discipline terminology. This review article describes what e-vocabularies are, what they are like, how they are used, how they work, and what they contribute to the retrieval of digital educational resources. The goal is to contribute to a clearer view of the concepts which we regard as crucial to understand e-vocabularies and their use in the field of e-learning to describe and retrieve digital educational resources

    Coding the semantic relations for basic nouns and verbs

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    Modelling, Detection And Exploitation Of Lexical Functions For Analysis.

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    Lexical functions (LF) model relations between terms in the lexicon. These relations can be knowledge about the world (Napoleon was an emperor) or knowledge about the language (‘destiny’ is synonym of ‘fate’)

    Extending the Galician Wordnet Using a Multilingual Bible Through Lexical Alignment and Semantic Annotation

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    In this paper we describe the methodology and evaluation of the expansion of Galnet - the Galician wordnet - using a multilingual Bible through lexical alignment and semantic annotation. For this experiment we used the Galician, Portuguese, Spanish, Catalan and English versions of the Bible. They were annotated with part-of-speech and WordNet sense using FreeLing. The resulting synsets were aligned, and new variants for the Galician language were extracted. After manual evaluation the approach presented a 96.8% accuracy

    Semi-automatic enrichment of crowdsourced synonymy networks: the WISIGOTH system applied to Wiktionary

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    International audienceSemantic lexical resources are a mainstay of various Natural Language Processing applications. However, comprehensive and reliable resources are rare and not often freely available. Handcrafted resources are too costly for being a general solution while automatically-built resources need to be validated by experts or at least thoroughly evaluated. We propose in this paper a picture of the current situation with regard to lexical resources, their building and their evaluation. We give an in-depth description of Wiktionary, a freely available and collaboratively built multilingual dictionary. Wiktionary is presented here as a promising raw resource for NLP. We propose a semi-automatic approach based on random walks for enriching Wiktionary synonymy network that uses both endogenous and exogenous data. We take advantage of the wiki infrastructure to propose a validation "by crowds". Finally, we present an implementation called WISIGOTH, which supports our approach

    Word Sense Disambiguation Based on Large Scale Polish CLARIN Heterogeneous Lexical Resources

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    Word Sense Disambiguation Based on Large Scale Polish CLARIN Heterogeneous Lexical Resources Lexical resources can be applied in many different Natural Language Engineering tasks, but the most fundamental task is the recognition of word senses used in text contexts. The problem is difficult, not yet fully solved and different lexical resources provided varied support for it. Polish CLARIN lexical semantic resources are based on the plWordNet — a very large wordnet for Polish — as a central structure which is a basis for linking together several resources of different types. In this paper, several Word Sense Disambiguation (henceforth WSD) methods developed for Polish that utilise plWordNet are discussed. Textual sense descriptions in the traditional lexicon can be compared with text contexts using Lesk’s algorithm in order to find best matching senses. In the case of a wordnet, lexico-semantic relations provide the main description of word senses. Thus, first, we adapted and applied to Polish a WSD method based on the Page Rank. According to it, text words are mapped on their senses in the plWordNet graph and Page Rank algorithm is run to find senses with the highest scores. The method presents results lower but comparable to those reported for English. The error analysis showed that the main problems are: fine grained sense distinctions in plWordNet and limited number of connections between words of different parts of speech. In the second approach plWordNet expanded with the mapping onto the SUMO ontology concepts was used. Two scenarios for WSD were investigated: two step disambiguation and disambiguation based on combined networks of plWordNet and SUMO. In the former scenario, words are first assigned SUMO concepts and next plWordNet senses are disambiguated. In latter, plWordNet and SUMO are combined in one large network used next for the disambiguation of senses. The additional knowledge sources used in WSD improved the performance. The obtained results and potential further lines of developments were discussed
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