scholarly journals An Overview of Web Intelligence

2012 ◽  
Vol 1 (1) ◽  
pp. 3-27
Author(s):  
أ.د. محمد أديب غنيمي أ.د. محمد أديب غنيمي
Keyword(s):  
Artificial Intelligence ◽  
Information Technology ◽  
Problem Solving ◽  
Semantic Web ◽  
Natural Language ◽  
Deep Web ◽  
Natural Languages ◽  
Web Intelligence ◽  
And Linguistics ◽  
Mathematics And Physics

. This paper gives an overview of Web intelligence which will enable the current Web to reach the Wisdom level by containing Distributed, Integrated, and Active knowledge. In this case it will be capable of performing tasks like problem solving and questionanswering. In addition, it will be capable of processing and understanding natural languages. Web intelligence draws results from a number of disciplines like: Artificial intelligence, Information technology. Mathematics and Physics, Psychology and Linguistics. The paper covers the following topics: Web evolution and architecture, Topics related to Web intelligence, The Deep Web, Semantic computing and the Semantic Web, The Wisdom Web, Precisiated Natural Language.

Linguistic Data Model for Natural Languages and Artificial Intelligence. Part 5. Introduction to Logic

Discourse ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 109-117
Author(s):  
O. M. Polyakov
Keyword(s):  
Artificial Intelligence ◽  
Mathematical Logic ◽  
Natural Language ◽  
Semantic Interpretation ◽  
General View ◽  
World Model ◽  
Semantic Approach ◽  
Natural Languages ◽  
The World ◽  
Linguistic Aspect

Introduction. The article continues the series of publications on the linguistics of relations (hereinafter R–linguistics) and is devoted to an introduction to the logic of natural language in relation to the approach considered in the series. The problem of natural language logic still remains relevant, since this logic differs significantly from traditional mathematical logic. Moreover, with the appearance of artificial intelligence systems, the importance of this problem only increases. The article analyzes logical problems that prevent the application of classical logic methods to natural languages. This is possible because R-linguistics forms the semantics of a language in the form of world model structures in which language sentences are interpreted.Methodology and sources. The results obtained in the previous parts of the series are used as research tools. To develop the necessary mathematical representations in the field of logic and semantics, the formulated concept of the interpretation operator is used.Results and discussion. The problems that arise when studying the logic of natural language in the framework of R–linguistics are analyzed. These issues are discussed in three aspects: the logical aspect itself; the linguistic aspect; the aspect of correlation with reality. A very General approach to language semantics is considered and semantic axioms of the language are formulated. The problems of the language and its logic related to the most General view of semantics are shown.Conclusion. It is shown that the application of mathematical logic, regardless of its type, to the study of natural language logic faces significant problems. This is a consequence of the inconsistency of existing approaches with the world model. But it is the coherence with the world model that allows us to build a new logical approach. Matching with the model means a semantic approach to logic. Even the most General view of semantics allows to formulate important results about the properties of languages that lack meaning. The simplest examples of semantic interpretation of traditional logic demonstrate its semantic problems (primarily related to negation).

SEMCL

2010 ◽  
Vol 1 (3) ◽  
pp. 1-19 ◽  
Author(s):  
Weisen Guo ◽  
Steven B. Kraines
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Knowledge Sharing ◽  
Language Processing ◽  
Global Knowledge ◽  
Semantic Web Technologies ◽  
Natural Languages ◽  
Web Technologies ◽  
Language Knowledge ◽  
Cross Language

To promote global knowledge sharing, one should solve the problem that knowledge representation in diverse natural languages restricts knowledge sharing effectively. Traditional knowledge sharing models are based on natural language processing (NLP) technologies. The ambiguity of natural language is a problem for NLP; however, semantic web technologies can circumvent the problem by enabling human authors to specify meaning in a computer-interpretable form. In this paper, the authors propose a cross-language semantic model (SEMCL) for knowledge sharing, which uses semantic web technologies to provide a potential solution to the problem of ambiguity. Also, this model can match knowledge descriptions in diverse languages. First, the methods used to support searches at the semantic predicate level are given, and the authors present a cross-language approach. Finally, an implementation of the model for the general engineering domain is discussed, and a scenario describing how the model implementation handles semantic cross-language knowledge sharing is given.

scholarly journals Perancangan Chatbot Menggunakan Dialogflow Natural Language Processing (Studi Kasus: Sistem Pemesanan pada Coffee Shop)

2020 ◽  
Vol 4 (1) ◽  
pp. 208
Author(s):  
Albert Yakobus Chandra ◽  
Didik Kurniawan ◽  
Rahmat Musa
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Information Technology ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Information Services ◽  
Human Language ◽  
Coffee Shop ◽  
The Impact

Some cases that are often experienced at a particular institution such as Micro Enterprise are often a staff / employee in providing information services and transactions that are carried out manually to customers related to these business activities. This cycle always repeats from one customer to another. The impact if there are conditions where the queue of customer that is quite crowded than the workload of staff/employees will be higher and the risk of error in transactions will be high too. The development of information technology in artificial intelligence on 4.0 industry era is moving forward. One of them is Machine Learning - Natural Language Processing (NLP) which is one of the sciences that focuses on how computers can understand the human language and response to it. Therefor in this research a chatbot system will be builtin providing information and conducting transaction with the customers. This chatbot will be develop using the Dialogflow tools provided by Google. This Chatbot that was build expected to be an alternative that can be implemented in various bussines to provide better service for customers

SEMCL

2013 ◽  
pp. 168-187
Author(s):  
Weisen Guo ◽  
Steven B. Kraines
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
Knowledge Sharing ◽  
Language Processing ◽  
Global Knowledge ◽  
Semantic Web Technologies ◽  
Natural Languages ◽  
Web Technologies ◽  
Language Knowledge ◽  
Cross Language

To promote global knowledge sharing, one should solve the problem that knowledge representation in diverse natural languages restricts knowledge sharing effectively. Traditional knowledge sharing models are based on natural language processing (NLP) technologies. The ambiguity of natural language is a problem for NLP; however, semantic web technologies can circumvent the problem by enabling human authors to specify meaning in a computer-interpretable form. In this paper, the authors propose a cross-language semantic model (SEMCL) for knowledge sharing, which uses semantic web technologies to provide a potential solution to the problem of ambiguity. Also, this model can match knowledge descriptions in diverse languages. First, the methods used to support searches at the semantic predicate level are given, and the authors present a cross-language approach. Finally, an implementation of the model for the general engineering domain is discussed, and a scenario describing how the model implementation handles semantic cross-language knowledge sharing is given.

scholarly journals Building ontologies for different natural languages

2014 ◽  
Vol 11 (2) ◽  
pp. 623-644 ◽  
Author(s):  
Emhimed Alatrish ◽  
Dusan Tosic ◽  
Nikola Milenkovic
Keyword(s):  
Semantic Web ◽  
Natural Language ◽  
General Procedure ◽  
Software Tools ◽  
Domain Ontology ◽  
Ontology Development ◽  
The Internet ◽  
Wide Spread ◽  
Natural Languages ◽  
Development Tool

Ontology construction of a certain domain is an important step in applying the Semantic web. A number of software tools adapted for building domain ontologies of most wide-spread natural languages are available, but accomplishing that for any given natural language presents a challenge. Here we propose a semi-automatic procedure to create ontologies for different natural languages. Our approach utilizes various software tools available on the Internet most notably DODDLE-OWL - a domain ontology development tool implemented for English and Japanese languages. By using this tool, WordNet, Prot?g? and XSLT transformations, we propose a general procedure to construct domain ontology for any natural language.

scholarly journals Linguistic Data Model for Natural Languages and Artificial Intelligence. Part 4. Language

Discourse ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 107-114
Author(s):  
O. M. Polyakov
Keyword(s):  
Artificial Intelligence ◽  
Natural Language ◽  
Natural Languages ◽  
New Approach ◽  
Linguistic Model ◽  
The World ◽  
Human World ◽  
The Common ◽  
The Difference ◽  
The Given

Introduction. The paper continues a series of publications on linguistics of relations (hereinafter R–linguistics) and is devoted to questions of the formation of a language from a linguistic model of the world. Moreover, the language is considered in its most general form, without taking into account the grammatical component. This allows you to focus on the general problems of language formation. Namely, this allows us to show why language adequately reflects the model of the world and what are the features of the transition from model to language. This new approach to language is relevant in connection with the formation of an understanding of the common core in all natural languages, as well as in connection with the needs for the formation of artificial intelligence subsystems of interaction with humans.Methodology and sources. Research methods consist in the formulation and proof of theorems about language spaces and their properties. The materials of the paper and the given proofs are based on the previously stated ideas about linguistic spaces and their decompositions into signs.Results and discussion. The paper shows how, in the most general form, the formation of language structures takes place. Namely, why does language adequately reflect the linguistic model, and what is the difference between linguistic and language spaces? The concepts of an open and closed form of the language are formulated, as well as the law of form. Examples of open and closed forms of the language are shown. It is shown that the formation of the language allows you to compensate for the lack of real signs in the surrounding world while maintaining the prognostic properties of the model.Conclusion. Any natural language is a reflection of the human world model. Moreover, all natural languages are similar in terms of the principles of forming the core of the language (language space). Language spaces standardize the models of the world by equalizing real and fictional signs of categories. In addition, the transition to language simplifies some of the problems of pattern recognition and opens the way to the logic of natural language.

Artificial Intelligence in Chemistry

1992 ◽  
Vol 57 (12) ◽  
pp. 2413-2451 ◽  
Author(s):  
Vladimír Jakuš
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Problem Solving ◽  
Expert Systems ◽  
Intelligent Systems ◽  
Structure Elucidation ◽  
Chromatographic Retention ◽  
Natural Languages ◽  
Synthesis Planning ◽  
Intelligent Instrumentation

The definition of artificial intelligence and the associated tasks of this branch of science are discussed. The tasks include pattern recognition, adaptation and learning, problem solving by means of expert systems or neural networks, and understanding the natural language and communication with a machine in it. The principles of problem solving are analyzed. It is demonstrated how artificial intelligence-based computer programs in which chemical expertise is encoded assist in structure elucidation, in the investigation of relations between structure and biological activity or chromatographic retention, etc.; problems emerging in the synthesis planning with a retrosynthetic analysis, or in the planning of experiments and intelligent consultations are dealt with. Several models used for structure elucidation and synthesis planning are evaluated. An overview is presented of additional expert systems which, along with artificial intelligence-based robotics, are used in intelligent instrumentation. Also discussed is the role of neural networks, which begin to be successfully employed in structure elucidation, synthesis planning, in intelligent instrumentation and in the treatment of natural languages. They are expected to be an important tool in the implementation of intelligent systems for the classification of chemical databases and prediction of properties of molecules.

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