Implementation of Intelligence Robotic Engines Using Ontologies and Process Natural Language

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1 Implementation of Intelligence Robotic Engines Using Ontologies and Process Natural Language Rafael Luiz de Macedo, Elvis Fusco, and Fábio Dacencio Pereira UNIVEM, University Center Eurípides of Marília, Marília, SP, Brazil Abstract - The AIML (Artificial Intelligence Markup Language) is a set of tags XML (extensible Markup Language) able to represent and relate expressions in natural language allowing the creation of robotic engines capable of maintaining a simple dialog, however, robotic engines are limited in the amount of questions they may respond, thus, failing to keep a simple dialogue for a long time. This article is intended to extend the AIML in incorporation of HTML5 elements and custom patterns using a Java interpreter. Through these proposed elements and with the aid of a source code Analyzer software developed semantic searches can be performed on the web using the sources of pages and, from them, the elements are parsed and extracted to a semantic treatment in extracted information, generating new information handled in the form of questions and answers for the robotic engines developed in AIML markup language. Keywords: AIML; Artificial Intelligence; Engines Robotic; Semantic Web. 1 Introduction Current technologies have allowed more and more, the perception that it is possible to make a machine be intelligent enough to answer questions made through robotic engines built with artificial intelligence. The language is intended to be used for communication on the world. When you learn the language, the more the world can test theories about the world to know that they give support to attempt to understand the language. And, if we can create a computational model of language, can be a powerful tool for communication on the world [8]. One of the languages that enables the creation and development of these robotic smart Motors is the markup language AIML (Artificial Intelligence Markup Languages). But, the AIML language has a limitation on the development of these engines, making it limited the number of questions that can be answered by these robotic engines. With the emergence of the Internet, you can create new AIML tags interpreted with HTML5 standards and customized tags for semantic searches are carried out on the web. These searches will be made by means of the questions that the robotic engines are not possible answer them. And, with a source code Analyzer software that makes extracting information from treatment Web sites found on the Internet and turning these new information on new questions and answers to enhance and extend the knowledge of these robotic intelligent engines. The purpose of this article is to extend markup language AIML AIML, with new elements from the HTML5 tags and embedding of parser software development able to make the treatment of the information extracted from the source code of websites, thus generating new AIML language information. To search and interpretation of information on the WEB, it is necessary to use concepts of search and natural language interpretations. In particular, ontology and Natural language processing impose these rules. 2 AIML The AIML language enables the creation of robotic conversational engines called Chatterbots (Chat = Chat and bot = robot), a robot with Artificial Intelligence that interacts with users, through conversations, causing a human thinks is "talking" with another human. One of the best-known software in natural language processing is the A.L.I.C.E. (Artificial Linguistic Internet Computer Entity). With the development of the A.L.I.C.E. language, resulting in the AIML markup language. AIML is license under the GNU GPL and there are AIML interpreters available in Java, Ruby, Python, C++, c #, Pascal, and other languages. The language is a set of AIML tags XML (extensible Markup Language) capable of representing and linking natural language expressions, allowing the creation of engines able to keep a simple dialog. Each AIML tag set has one or more tags category calls, and the categories are developed upon a context; a category is formed by all the tags and template pattern, which are the tags responsible for interpreting the message entered by the user and send a reply message back to the user. The AIML is divided into two modules, language and motor. The language is where all knowledge of natural

2 language and information developed in AIML language, and the motor makes communication between natural language and markup language AIML, however there is a limitation in your modules. The development of AIML is related to a context, making it limited its application. So, in a natural language processing system using the AIML, the conversation does not remain for a long time in a dialogue because it is lost in a question out of context that was developed. Alan Turing ( ), in his famous essay "Computing Machinery and Intelligence" (Turing 1950), suggested that, instead of asking whether machines can think, we must ask if machines can pass a test of behavioral intelligence, which came to be called Turing test [8]. The markup language AIML is an open source language, thus enabling the use of language in the search for improvement and creation of intelligent robotic conversation engines. Alan Turing proposed that, if it is not possible to distinguish a machine answers those provided by a person, the machine could be considered smart and, to the present day, there are no machines capable of going for this Turing test. To create robotic engines, can be used a specific platform and, in a markup language, there are many platforms and languages available for the creation of these robotic engines. In this article, is being used an interpreter in the Java programming language and platform ProgramD, which is a Open Source platform. The ProgramD platform is the most widely used platform in the development of robotic intelligent engines in AIML markup language and the most complete in AIML language feature, in addition to being the best ever tested implementation of AIML. 3 HTML With the advancement of Internet and browsers, HTML5 (Hypertext Markup Language) is a valid attempt to standardize how browsers interpret, classify and present the information received. While the World Wide Web Consortium (W3C) focused their attention to the creation of the second version of XHTML, a group called the Web Hypertext Application Technology Working Group or WHATWG working on a version of HTML that would bring more flexibility to the production of websites and Web-based systems. HTML5 is a markup language that facilitates the development of sites, because, with a few lines, it is possible to build a simple website and in addition enable plugins that facilitate the development of interfaces. Taking advantage of this new standard of organization of information on the Web it is possible to increase the scope and ability of languages like the AIML. Currently, most Web sites is developed on top of HTML5 and the new elements created in the AIML language will make the interpretation of these HTML5 tags, performing semantic search on the Web to find sites in HTML5 developments. 4 Ontology The ontology about the knowledge of be, studies the understanding of be. it creates various types of different domains, which each domain, is renowned on a particular subject, such as in a semantic search on the Web. According to [4], ontology is a specification of a conceptualization, that is, an ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents. On the Web, ontology is used to organize a knowledge domain, to be able to model a knowledge. Example of use of the ontology is a semantic search on the Web, because it gets a more accurate search, knowing of knowledge related to the subject or the object being sought and thereby getting more accurate results. The integration of the Semantic Web with the AIML language has the main goal of making the Web usage to enhance and embrace the AIML language and robotic engines that are created on top of that language, so that increases the number of questions that can be made to these robotic engines. For that language AIML can accomplish these semantic searches, new elements will be developed AIML interpreted with HTML5 tags and custom patterns. To carry out these searches, however, it is necessary to obtain clear and precise information about the context of the questions that are made to these robotic intelligent engines. To this will be made to human questions and thus information about the context in which the question belongs. Intelligent robotic engines developed in AIML markup language, to fail to answer any questions by not contain in its natural language information Bank, will hold two important questions so that it can be carried out searches fast and accurate web semantics. The two questions are: First-what is the name of the subject? Second-in a nutshell describes, with which it relates? With the information obtained by these two questions in a nutshell semantic search can be performed on the web more quickly and accurately with the application of OpenCyc, which makes use of the concepts of the ontology.

3 5 OpenCyc The OpenCyc platform is your gateway to the total power of Cyc, the world's largest and most complete general knowledge base and commonsense reasoning engine. OpenCyc contains hundreds of thousands of terms of Cyc, arranged in a carefully designed ontology. Cycorp offers this ontology at no cost and encourages you to use and extend, this ontology, instead of starting your own from scratch [5]. Cycorp is a leading provider of semantic technologies that bring a new level of intelligence and common sense reasoning to a wide variety of software applications. The software combines an incomparable sense Cyc ontology and knowledge base with a powerful motor of reasoning and natural language interfaces to enable the development of new applications of knowledge [5]. The Cyc was founded by Dr. Douglas Lenat, in the year 1984, your goal was as a lead project at microelectronics and computer technology. The OpenCyc makes use of numerous fields of ontology and is an open source platform that helps and helps in mapping and information extraction. Now it's even easier to use the rich and diverse collection of real-world concepts in OpenCyc to bring meaning to their semantic web applications! The entire contents of OpenCyc is now available both as downloadable OWL ontologies and semantic web endpoints (that is, permanent URIs). These URIs return RDF representations of each Cyc concept as well as a human-readable version when accessed through a Web browser [6]. The use of the OpenCyc assists in better absorption in the information found on web sites and in generating new questions and answers to intelligent robotic engines developed in AIML markup language. 6 Processing Natural Language Natural language processing is the study of language, being spoken language or text written language. The study of language enables us to a better understanding of the language and, if the machines understand language, you can create intelligent robotic engines. The written language makes use of lexical, syntactic and semantic knowledge of the language and also all the necessary information about the real world [7]. Natural language processing is divided into several components, each component has its function in natural language recognition. The components, Morphological Analysis, syntactic analysis, Semantic Analysis, integration of discourse and Pragmatic Analysis, are the Group of components that make up the body of natural language processing. 6.1 Morphplogical Analysis Every word is analyzed separately and is made the recognition of same; the words are analyzed in terms of its components, and the signs, such as punctuation, are separated from the words [7]. 6.2 Syntatic Analysis Syntactic analysis is the recognition of a set of words to see if they are correctly linked. Some sequences of words may be rejected if you violate the rules of the language about how words can be combined. For example, a parser of Portuguese would reject the phrase: "the Boy goes to the store" [7]. 6.3 Semantic Analysis Is done a mapping in the words to make the recognition and see if the meaning of the set is correct. Each set of words has a semantic meaning, belongs to some specific context. Semantic analysis is the recognition that context to which the set of words belongs and does a semantic mapping to see if is correct its meaning. A set of words that cannot be done the semantic mapping is rejected by the semantic Analyzer. 6.4 Integration of Speech Is the meaning of a sentence depends on the previous phrases isolated for its meaning and influence in subsequent sentences. For example, the word "it", in the phrase "John wanted it" depends on the context of the previous speech; While the word "John" may influence the meaning of phrases, such as: "He always wanted to" [7]. 6.5 Pragmatic Analysis A pragmatic analysis makes the interpretation of the phrase, recognizing what it meant. The phrase may represent a request, an affirmation, a warning and other representations contained in natural language. The phrase "you know what time is it?" should be interpreted as a request to which the hours are informed [7]. 7 Robotic Motors Combined With Process Natural Language Robotic Motors make the recognition of a set of characters of a natural language, that is, recognize words and phrases typed by users; However these robotic engines cannot understand the meaning of words or phrases typed, more clear and precise, cannot understand the semantics.

4 A robotic engine created in markup language AIML, contains a limitation on the recognition of these questions; You can only perform this engine have questions in your natural language information bank. With the rules of natural language processing techniques and a specific domain of ontology, it is possible to develop a software source code Analyzer that makes a better understanding in natural language, generating new questions and answers to the robotic engines. When a robotic motor can't answer a question, it will make use of the new elements of AIML markup language for semantic searches on the web, finding sites that contains information related to this question was made and with the integration of the software source code Analyzer applies a mapping and treatment information that is contained in the source code of the web site that was obtained in the search result to generate new questions and responses, in order to be able to answer the question that is not contained in your natural language information bank. 8 Related Work The implementation of information mapping requires a great aid of the domain ontology concepts, in order to get a better absorption of the context in which this information belong. But, in related jobs realizes that it is too big the difficulty in obtaining a total absorption and in many cases it is not clear what this information is. The concepts of the ontology are applied in several works of information mapping, one of those jobs that make use of these concepts of the ontology is the "Extracting Ontological Structure of the OpenCyc for Reuse and Portability of Cognitive Models" [4], which makes the mapping and information extraction using OpenCyc. General-purpose knowledge ontologies, such as OpenCyc, have been suggested as a means of increasing the portability and reuse of cognitive models through a mapping onto domain-independent language. Previous efforts have revealed that this mapping process is difficult to perform due to several factors including the difficulty of understanding the underlying structure of the ontology and mismatches in representation between the target cognitive modeling architecture and the source ontology [4]. The approach of "Extracting Ontological Structure of the OpenCyc for Reuse and Portability of Cognitive Models" was divided into three stages, determining a suitable mapping, an extracted hierarchy and show the results. The first step is the hardest step is the step which determines an appropriate mapping of specific terms, because a search is normally performed in a simple way, containing little information related to what is being sought and performing a search without much precision. To perform a more accurate search, one would use the simple lookup as a starting point and dig for more specific constants. It is important to mention here that the full meaning of an OpenCyc term is best understood as a combination of 1) the name, 2) the related (more general/specific) terms, and 3) the comment tag associated with the term. [4]. After the implementation of the first stage, is passed to the second stage, extracted or pruning hierarchy hierarchy. In the second stage is a pruning on information obtained on the web. Once the web of terms has been extracted from OpenCyc, some amount of pruning can be done; the level of pruning (or possibly expansion) depends highly on the intended use of the web. For instance, a web pruned from the root down to the most specific parent term (Lowest Common Genl or LCG) is a useful way to get an overall sense of the complexity and structure of OpenCyc [4]. With the exclusion of information that are not totally related to the subject that was sought, the amount of information that was obtained in the search, decrease and end up getting more accurate and thus can make a better mapping and information extraction. After the implementation of the second stage, will be held the third and last step, you will see the hierarchy. In the third and final step is presented the results obtained after being performed a search on the web and mapping information for the exclusion of information unrelated to the search context. Hierarchically, is presented as the search result before you run a mapping and deleted some information and after the mapping is presented in the same hierarchical way the result. The end result is obtained after the application of these three steps, but, with the implementation of the first two steps it is possible to obtain a more accurate result. In the first two steps, determining a suitable mapping and a hierarchy extracted, made use of the OpenCyc for application of the concepts of some specific fields of the ontology. In this article, the form in which it is made, treatment and extraction mapping in the information found on the web sites used which were used for the development of source code Analyzer software, makes the use of the concepts of ontology, and also, the rules of natural language processing for better software development and recognition of natural language that the site belongs. The form in which the work "Extracting Ontological Structure of the OpenCyc for Reuse and Portability of Cognitive Models" [4], applies the concepts of the ontology and the mapping in the information with the help of the OpenCyc is very rich in functionality, quality, get a quick and precise result.

5 Without the help of OpenCyc in software development source code Analyzer in this article, you can get an extraction of the information contained in the web sites. 9 Result The software source code analyzer developed in this article, performs some analysis and certain treatments on top of the scenarios that were developed for this article. The operation of the software source code Analyzer is divided by steps. In the first step, is passed around the site's source code found on a file already created by the parser, so that software, so it is possible to analyze and give the right treatment to the source code found. In Fig. 1, is shown as a file, after being passed the whole source code of the site found for the file. Figure 1. Uploaded file with the source code of the site. In the second step, the software analyzes all the file containing the source code of the Web site and apply the treatment; the parser makes the recognition of sets of words that do not belong to any programming language of Web sites and separating these sets of words into a new file in the order in which the words were found. All tags and commands that belong to any Web programming language sites are dropped by the software. In Fig. 2, is shown the result of the second stage, which is the file generated with the information found on the site. Figure 2. File with the information that has been extracted from the source code of the site. In the third and final step, the parser software loads the file that contains the information taken from the Web site analyzed, that, with this file, applies a treatment of the questions and answers. The software analyzes the entire file looking for questions that may be directly or indirectly writes this information. The questions and answers found by parser software are written to a new file along with AIML tags, so that the engine developed robotic AIML can make use of this file and, therefore, answer the questions that pertain to this file generated by the source code parser software. In Fig. 3 shows how an AIML file generated by the source code parser software.

6 questions and answers to the markup language AIML knowledge and increase the number of questions that can be carried out for these robotic intelligent engines. With the use of domain specific ontology concepts by the OpenCyc and technical rules for natural language processing, it is possible to obtain a better result in the generation of new questions and answers to these robotic intelligent engines. 11 References [1] ALICE AI Foundation. AIML: The Artificial Intelligence Markup Language May [2] ALVES, Rachel C. V. (2005) Web Semântica: Uma Análise focada no uso de metadados, Dissertação (Mestrado em Ciência da Informação) Faculdade de Filosofia e Ciências Universidade Estadual Paulista - UNESP, Marilia/SP. [3] BUSH, Noel. ProgramD November Figure 3. AIML file generated by the source code parser software. The AIML file that was generated by the software source code Analyzer will be used by the robotic engine built in AIML markup language, to cover his knowledge with new questions and answers that contains this new file. ProgramD platform is being used, which is a free platform to test the AIML files generated by the Analyzer software. 10 Conclusion AIML markup language has a limitation in creating robotic talk engines, making it limited the number of questions that may be made to these robotic engines. Due to this limitation, the robotic engines fail to keep a long conversation with a human, because questions made outside the context of knowledge in which these engines were developed cannot be answered. For such a problem, it is proposed to extend the AIML with the incorporation of elements interpreted with standard and customized, HTML5 tag for semantic searches on the Web, using concepts of a specific domain ontology for a better understanding on the information containing in question held these robotic engines. With the use of the concepts of a domain of ontology, it will be possible to make a more specific search and get more accurate results in the context of the question. The integration of these new AIML tags with the software source code Analyzer will make the use of natural language processing techniques, for the treatment of this information extracted from sites analyzed, transforming them into new [4] BEST, Bradley J., Gerhart, Nathan, & Lebiere, Christian Extracting the Ontological Structure of OpenCyc for Reuse and Portability of Cognitive Models, Proceedings of the 19th Conference on Behavior Representation in Modeling and Simulation, Charleston, SC, March [5] Cycorp Inc. (2013), OpenCyc [6] Cycorp Inc. (2013), OpenCyc for the Semantic Web, [7] Gruber, T.R. (1993) Toward Principles for the Design of Ontologies Used for Knowledge Sharing, International Workshop on Formal Ontology, Padova, Italy. [8] MENEZES, Paulo, B. (1998), Linguagens Formais e Autômatos Second Edition. Porto Alegre. [9] O'Brien. P, Zhan C. (2000) Domain Ontology Management Environment, Proceedings of 33rd Hawaii International Conference on Systems Sciences, January, Havaí. [10] RICH, Elaine e KNIGHT, Kevin. (1993) Inteligência Artificial Second Edition. São Paulo/SP. [11] RUSSELL, Stuart e NORVIG, Peter. (2004) Inteligência Artificial Translation of Second Edition. Rio de Janeiro/RJ. [12] School of Mathematics and Statistics University of St Andrews. Historia do Matemático Alan Turing, Escócia, Octuber. [13] SILVA, Maurício Samy. (2010) HTML5 - A linguagem de marcação que revolucionou a web - Publisher Novatec, São Paulo/SP.

7 [14] WALLACE, Dr. Richard S. (2003) The Element of AIML Style, ALICE A. I. Foundation, Inc., Octuber. [1] WALLACE, Dr. Richard; TAYLOR, Anthony. AIML Reference Manual, Octuber 2012.