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Wyszukujesz frazę "knowledge representation language" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Modeling & understanding environment using semantic agents
Autorzy:
Dourlens, S.
Ramdane-Cherif, A.
Powiązania:
https://bibliotekanauki.pl/articles/91661.pdf
Data publikacji:
2011
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
semantic agent
knowledge representation language
generalized meta-model
behavioral aspects
Opis:
Understanding environment is a very complex task. Modeling and development of components of the environment in a symbolic way permit to simplify and clarify functional parts of components and interactions. In order to have a complete system specification, a rigorous behavior description is needed. Different levels of behavior abstraction are taking into account. The objective of our semantic modeling is to enhance architectural design and reduce complexity. It permits to agents to understand the environment, manage events and adapt the architecture. All the concepts of the environment like the component models and their behavior models are stored under event frames written in knowledge representation language. We present in this document, a generalized meta-model of behavioral aspects, that indexes the various environment behaviors in three ontologies. We have fully linked abstraction level with modeling and execution of scenarios. We show how software semantic agents can be modeled to build any interactive architecture.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 4; 301-314
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Knowledge graphs effectiveness in Neural Machine Translation improvement
Autorzy:
Ahmadnia, Benyamin
Dorr, Bonnie J.
Kordjamshidi, Parisa
Powiązania:
https://bibliotekanauki.pl/articles/1839251.pdf
Data publikacji:
2020
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
natural language processing
neural machine translation
knowledge graph representation
Opis:
Maintaining semantic relations between words during the translation process yields more accurate target-language output from Neural Machine Translation (NMT). Although difficult to achieve from training data alone, it is possible to leverage Knowledge Graphs (KGs) to retain source-language semantic relations in the corresponding target-language translation. The core idea is to use KG entity relations as embedding constraints to improve the mapping from source to target. This paper describes two embedding constraints, both of which employ Entity Linking (EL)—assigning a unique identity to entities—to associate words in training sentences with those in the KG: (1) a monolingual embedding constraint that supports an enhanced semantic representation of the source words through access to relations between entities in a KG; and (2) a bilingual embedding constraint that forces entity relations in the source-language to be carried over to the corresponding entities in the target-language translation. The method is evaluated for English-Spanish translation exploiting Freebase as a source of knowledge. Our experimental results demonstrate that exploiting KG information not only decreases the number of unknown words in the translation but also improves translation quality
Źródło:
Computer Science; 2020, 21 (3); 299-318
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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