Multi Objective Criteria for Selection of Manufacturing Method using NLP Parser

Installing a manufacturing method might be very expensive and time consuming project. Organization should examine and decide on how best to make this decision of selecting appropriate process meeting their requirements. In order to improve the manufacturing cycle more than 110 manufacturing processes have been proposed. The objectives aimed at and the functions focused on by these processes vary. The process should be flexible enough to accommodate reasonable changes in design. This poses a great challenge to a manager in selection of effective and economical manufacturing process. Different organizations have different objectives and based on their specific requirement they deploy suitable process conforming to their objective. Today’s business scenario is highly competitive, complex and dynamic in nature which demands strategic planning meeting the challenges of changing time. In this paper we have made an attempt to enable the end user a quick selection of appropriate manufacturing method based on multiple objectives. The information pertaining to the method selection is stored in a persistent Relational DataBase Management System (RDBMS) which can be manipulated by the end user as the organizational objectives and the market needs change. The end user instead of querying the database directly will use the natural language, termed as Manufacturing Query Language (MQL) designed by us, which is interfaced with RDBMS using prolog. To implement MQL, we have defined a finite set of symbols, words and language rules, MQL grammar. The parse tree is constructed based on the grammar specified. The NLP query is parsed using NLP parser designed by us and the queries which are successfully parsed are evaluated by mapping them to the corresponding prolog query using Java interface to Prolog (JPL). Prolog rules are stored in three different prolog knowledge bases, mqlgrammar.pl, rules.pl, and methodrules.pl. NLP offers most flexible way to implement grammar which can be readily extended with least efforts and as such offers an efficient way of implementing rules in dynamically changing scenarios. Our current work focuses on a multiple objectives. In real situations multi objective and multi function criteria is required for the proper selection of the manufacturing method. Our future work involves modification of the tool and parser to take account of multiple objectives and function

JELLY VIEWS : EXTENDING RELATIONAL DATABASE SYSTEMS TOWARD DEDUCTIVE DATABASE SYSTEMS

This paper regards the Jelly View technology, which provides a new, practical methodology for knowledge decomposition, storage, and retrieval within Relational Database Management Systems (RDBMS). Intensional Knowledge clauses (rules) are decomposed and stored in the RDBMS founding reusable components. The results of the rule-based processing are visible as regular views, accessible through SQL. From the end-user point of view the processing capability becomes unlimited (arbitrarily complex queries can be constructed using Intensional Knowledge), while the most external queries are expressed with standard SQL. The RDBMS functionality becomes extended toward that of the Deductive Database

dbProlog: a Prolog/relational database interface

dbProlog is a prototype system that provides a C-Prolog user access to data in an external relational database via both loose and tight coupling. To the application programmer, dbProlog is a group of six built-in Prolog predicates that effect communication between a C-Prolog process and a database management system process. Prolog application program statements may be written using the six predicates to make the interface transparent to an end-user. The system is based on a driver process that must be customized to the interfaced DBMS and whose primary function is the translation of requests and replies between C-Prolog and the DBMS. dbProlog supports Prolog\u27s depth-first search on database retrievals by producing the next record when the retrieval predicate is encountered upon backtracking. dbProlog also supports multiple active database retrievals, as may be required by a Prolog rule that references two or more database retrievals, or by a recursive rule

User Preference Web Search -- Experiments with a System Connecting Web and User

We present models, methods, implementations and experiments with a system enabling personalized web search for many users with different preferences. The system consists of a web information extraction part, a text search engine, a middleware supporting top-k answers and a user interface for querying and evaluation of search results. We integrate several tools (implementing our models and methods) into one framework connecting user with the web. The model represents user preferences with fuzzy sets and fuzzy logic, here understood as a scoring describing user satisfaction. This model can be acquired with explicit or implicit methods. Model-theoretic semantics is based on fuzzy description logic f-EL. User preference learning is based on our model of fuzzy inductive logic programming. Our system works both for English and Slovak resources. The primary application domain are job offers and job search, however we show extension to mutual investment funds search and a possibility of extension into other application domains. Our top-k search is optimized with own heuristics and repository with special indexes. Our model was experimentally implemented, the integration was tested and is web accessible. We focus on experiments with several users and measure their satisfaction according to correlation coefficients