Localization of EHCPRs System in the Multilingual Domain: An Implementation Sarika Jain1, Deepa Chaudhary2, and N.K. Jain3 1
Department of Computer Application, CCS University, Meerut, Uttar Pradesh
[email protected] 2 Department of Computer Science and Engineering, Indraprastha Engineering College, Ghaziabad 3 Department of Electronics, Zakir Husain College, University of Delhi, New Delhi
[email protected]
Abstract. The increase of cross-cultural communication triggered by the Internet and the diverse language distribution of Internet users intensifies the needs for the globalization of the online intelligent system The Extended Hierarchical Censored Production Rules (EHCPRs) system might act as a generalized intelligent agent which takes care of context sensitivity in its reasoning. Efforts are to make the EHCPRs system online which can be localized as per the requirement of any specific multilingual domain of users. Keywords: Localization, Internationalization, Context, EHCPRs System.
1 System Architecture
Procedural
An Extended Hierarchical Censored Production Rule (EHCPR) is a unit of knowledge for representation in the EHCPRs based intelligent system. Any concept or object can be represented by employing the same uniform structure of an EHCPR. In an EHCPR, there are various operators to define different relations or dependencies of the objects with other objects. The operaDeclarative Knowledge tors are filled with fillers, which can be either atomic values or link to other EHCPRs. The Knowledge Base Data Base EHCPRs System has two major (Set of EHCPRs) (Set of Data Items) components (figure 1): Declarative knowledge and procedural Maintenance Learni Reasoning knowledge. The knowledge General base consists of all the EHCPRs Recognition User Control representing rules, definitions, Inheritance Interface Scheme or structures. The database stores all the instances of the concepts or objects that the Fig. 1. Components of EHCPRs System system has come across till date in the form of EHCPRs. C. Singh et al. (Eds.): ICISIL 2011, CCIS 139, pp. 314–316, 2011. © Springer-Verlag Berlin Heidelberg 2011
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Every EHCPR in the knowledge base is an instance of the class EHCPR defined in programming language Java as follows: class EHCPR {String concept; LinkedList preConditions; EHCPR generality; LinkedList specificity; LinkedList censors; LinkedList hasPart; LinkedList hasProperty; LinkedList hasInstance; }
2 System Working The EHCPRs System has been developed in India as a Ph.D. work without any financial help and is very widely published [ref. 1 - 3]. A prototype of the EHCPRs system is prepared to be deployed on the Internet for the online use by the multilingual global community of its users. The EHCPRs system exhibit changes in precision of the decision with changes in constraint of amount of resources, i.e., more time, more precise answer. Also it acts with the different level of precision with change in context of time, place, or emotions (i.e., state) of the subject, system or both. The EHCPRs system is helpful for answering queries of the type: “What is X doing” [ref. 2], and is given in Table1. 1. 2.
Display: The EHCPRs System exhibits the characteristics of logic, Production Rule, Semantic Network, Frame, Neural Network etc., in representation, and refer the Figure 2, as a snapshot of the system generated Semantic Network. Reasoning: Question Answering: If user asks the system “What is Dolly Doing”. The system provides a context sensitive answer, Table 1. Recognition: If user looks at an flying object and asks the system about it, then the system may reply that either it is a Crow, or Kite. Table 1. Variable Precise Reply with Change in Context
Context (Fuzzy Priority) Very Low Priority User Low Priority User Medium Priority User High Priority User
Very High Priority User
Output with different Specificity and Certainty Specificity Level is 0 "Dolly is in city Delhi" with certainty 0.476 Specificity Level is 0"Dolly is in city Delhi" with certainty 0.7917 "Dolly is at home" with certainty 0.624 Specificity Level is 0 "Dolly is in city Delhi" with certainty 0.7546 Specificity Level is 1 "Dolly is outdoor" with certainty 0.6324000000000001Specificity Level is 1. "Dolly is working outdoor" with certainty 0.5565120000000001 Specificity Level is 0 "Dolly is in city Delhi" with certainty 0.8623999999999999 Specificity Level is 1 "Dolly is outdoor" with certainty 0.7812 Specificity Level is 2 "Dolly is working outdoor" with certainty 0.7343280000000001
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S. Jain, D. Chaudhary, and N.K. Jain
Search Engine to support the question answering with inheritance and recognition Learning: The system has a Knowledge base (KB) and a database (DB). KB contains knowledge in the form of EHCPRs. Lot many EHCPRs have been hard coded. More will be learned through the interaction of the EHCPRs System with the users [ref. 3]. User Interface employing the Natural language processing, Vision and so on.
Fig. 2. A Snapshot of the semantic network extracted from the EHCPRs System
3 Conclusion Efforts are to develop the EHCPRs system as a machine tool which can be localized as per the requirement of any specific domain of users. The first step is internationalization, i.e., separating the locale dependencies from the source code, and then comes localization, i.e., adapting to the needs of a given locale.
References 1. Jain, N.K., Bharadwaj, K.K., Marranghello, N.: Extended Hierarchical Censored Production Rules (EHCPRs) System: An Approach toward Generalized Knowledge Representation. Journal of Intelligent Systems 9, 259–295 (1999) 2. Jain, S., Jain, N.K.: Generalized Knowledge Representation System for Context Sensitive Reasoning: Generalized HCPRs System. In: Artificial Intelligence Review, vol. 30, pp. 39– 42. Springer, Heidelberg (2009) 3. Jain, S., Jain, N.K.: Acquiring Knowledge in Extended Hierarchical Censored Production Rules (EHCPRs) System. International Journal of Artificial Life Research 1(4), 10–28 (2010)
