Workshops
1st International Workshop on AI Methods for Interdisciplinary Research in Language and Biology - BILC 2011
Co-chairs
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Gemma Bel-Enguix GRLMC-Research Group on Mathematical Linguistics, Universitat Rovira i Virgili Spain |
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Veronica Dahl Simon Fraser University / GRLMC-Research Group on Mathematical Linguistics, Universitat Rovira i Virgili Canada / Spain |
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Alfonso Ortega De La Puente Departamento de Ingeniería Informática, Universidad Autónoma de Madrid Spain |
Scope
During the 20th century, biology has become a pilot science, so that many disciplines have formulated their theories under models taken from biology. Computer science has become almost a bio-inspired field thanks to the great development of natural computing and DNA computing. From linguistics, several attempts to establish structural parallelisms between DNA sequences and verbal language have been made (Jakobson, 1973, Marcus, 1998, Ji, 2002). In general, it can be stated that formal languages and Natural Language Processing (NLP) can take great advantage of the structural and "semantic" similarities between those codes and other bio-inspired computing models. Therefore, NLP could become another "bio-inspired" science, by means of theoretical computer science, that provides the theoretical tools and formalizations which are necessary for approaching such exchange of methodology. In this way, we obtain a theoretical framework where biology, NLP and computer science exchange models and interact, thanks to the semiotic parallelisms that are being uncovered between the genetic code and natural language.
Artificial intelligence methods could be relevant for interdisciplinary research in language and biology. Important topics in this interplay where AI methods could be interesting are the following:
a) Modelling cognitive capabilities for producing language
b) Modelling tools for verbal language and nucleic acid language comprehension
c) Modelling human learning to achieve automatic learning
d) Modelling language evolution
During the 20th century, biology has become a pilot science, so that many disciplines have formulated their theories under models taken from biology. Computer science has become almost a bio-inspired field thanks to the great development of natural computing and DNA computing. From linguistics, several attempts to establish structural parallelisms between DNA sequences and verbal language have been made (Jakobson, 1973, Marcus, 1998, Ji, 2002). In general, it can be stated that formal languages and Natural Language Processing (NLP) can take great advantage of the structural and "semantic" similarities between those codes and other bio-inspired computing models. Therefore, NLP could become another "bio-inspired" science, by means of theoretical computer science, that provides the theoretical tools and formalizations which are necessary for approaching such exchange of methodology. In this way, we obtain a theoretical framework where biology, NLP and computer science exchange models and interact, thanks to the semiotic parallelisms that are being uncovered between the genetic code and natural language.
Artificial intelligence methods could be relevant for interdisciplinary research in language and biology. Important topics in this interplay where AI methods could be interesting are the following:
a) Modelling cognitive capabilities for producing language
b) Modelling tools for verbal language and nucleic acid language comprehension
c) Modelling human learning to achieve automatic learning
d) Modelling language evolution
International Workshop on Semantic Interoperability - IWSI 2011
Scope
The Semantic Web would be an evolving extension of current Web model (referred as Syntactic Web) that introduces a semantic layer in which semantics, or meaning of information are formally defined.
Semantics should integrate web-centric standard information infrastructures improving several aspects of interaction among heterogeneous systems: semantic interoperability would improve common interoperability models (basic and functional interoperability) introducing the interpretation of means of data. Semantic interoperability is a concretely applicable interaction model under the assumption of adopting rich data models (commonly called Ontology) composed of concepts within a domain and the relationships among those concepts.
Semantic technologies are partially inverting the common view at actor intelligence: intelligence is not implemented (only) by actors but it is implicitly resident in the knowledge model. In other words, schemas contain information and the "code" to interpretate it.
The aim of this Workshop is promoting an open international discussion among researchers from both academia and industry about Semantic Interoperability (and related issues) as well as the selection of a restricted number of high-quality selected papers about interest topics.
The Semantic Web would be an evolving extension of current Web model (referred as Syntactic Web) that introduces a semantic layer in which semantics, or meaning of information are formally defined.
Semantics should integrate web-centric standard information infrastructures improving several aspects of interaction among heterogeneous systems: semantic interoperability would improve common interoperability models (basic and functional interoperability) introducing the interpretation of means of data. Semantic interoperability is a concretely applicable interaction model under the assumption of adopting rich data models (commonly called Ontology) composed of concepts within a domain and the relationships among those concepts.
Semantic technologies are partially inverting the common view at actor intelligence: intelligence is not implemented (only) by actors but it is implicitly resident in the knowledge model. In other words, schemas contain information and the "code" to interpretate it.
The aim of this Workshop is promoting an open international discussion among researchers from both academia and industry about Semantic Interoperability (and related issues) as well as the selection of a restricted number of high-quality selected papers about interest topics.