Currently, ontologies are recognized as the most effective means of formalizing and systematizing knowledge and data in scientific subject area (SSA). Practice has shown that using ontology design patterns is effective in developing the ontology of scientific subject areas. This is due to the fact that scientific subject areas ontology, as a rule, contains a large number of typical fragments that are well described by patterns of ontology design. In the paper, we present an approach to ontology engineering of automatic text processing methods based on ontology design patterns. In order to get an ontology that would describe automatic text processing sufficiently fully, it is required to process a large number of scientific publications and information resources containing information from modeling area. It is possible to facilitate and speed up the process of updating ontology with information from such sources by using lexical and syntactic patterns of ontology design. Our ontology of automatic text processing will become the conceptual basis of an intelligent information resource on modern methods of automatic text processing, which will provide systematization of all information on these methods, its integration into a single information space, convenient navigation through it, as well as meaningful access to it.
Development of an intelligent information resource model based on modern na...IJECEIAES
Currently, there is an avalanche-like increase in the need for automatic text processing, respectively, new effective methods and tools for processing texts in natural language are emerging. Although these methods, tools and resources are mostly presented on the internet, many of them remain inaccessible to developers, since they are not systematized, distributed in various directories or on separate sites of both humanitarian and technical orientation. All this greatly complicates their search and practical use in conducting research in computational linguistics and developing applied systems for natural text processing. This paper is aimed at solving the need described above. The paper goal is to develop model of an intelligent information resource based on modern methods of natural language processing (IIR NLP). The main goal of IIR NLP is to render convenient valuable access for specialists in the field of computational linguistics. The originality of our proposed approach is that the developed ontology of the subject area “NLP” will be used to systematize all the above knowledge, data, information resources and organize meaningful access to them, and semantic web standards and technology tools will be used as a software basis.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
Towards Ontology Development Based on Relational Databaseijbuiiir1
Ontology is defined as the formal explicit specification of a shared conceptualization. It has been widely used in almost all fields especially artificial intelligence, data mining, and semantic web etc. It is constructed using various set of resources. Now it has become a very important task to improve the efficiency of ontology construction. In order to improve the efficiency, need an automated method of building ontology from database resource. Since manual construction is found to be erroneous and not up to the expectation, automatic construction of ontology from database is innovated. Then the construction rules for ontology building from relational data sources are put forward. Finally, ontology for �automated building of ontology from relational data sources� has been implemented
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING cscpconf
In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used toface the great challenge of representing the semantics of data, in order to bring the actual web to its full
power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. In deed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the
main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former
analysis the concept’s names and the later analysis their properties. Each one of these two sub-modules is
it self based on the combination of lexical and semantic similarity measures.
Ontology learning techniques and applications computer science thesis writing...Tutors India
At Tutors India, we offer Computer science and Information Technology Research Guidance services – We deliver exceptional work where your dissertation will deserve publication without significant reworking or alternation.
For #Enquiry
https://www.tutorsindia.com
info@tutorsindia.com
(Whatsapp): +91-8754446690
(UK): +44-1143520021
IDENTIFYING THE SEMANTIC RELATIONS ON UNSTRUCTURED DATAijistjournal
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic concepts that characterizes the domain as well as their definitions and interrelationships. This paper will describe some algorithms for identifying semantic relations and constructing an Information Technology Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences. We then extract these sentences based on English pattern in order to build training set. We use a random sample among 245 categories of ACM to evaluate our results. Results generated show that our system yields superior performance.
Development of an intelligent information resource model based on modern na...IJECEIAES
Currently, there is an avalanche-like increase in the need for automatic text processing, respectively, new effective methods and tools for processing texts in natural language are emerging. Although these methods, tools and resources are mostly presented on the internet, many of them remain inaccessible to developers, since they are not systematized, distributed in various directories or on separate sites of both humanitarian and technical orientation. All this greatly complicates their search and practical use in conducting research in computational linguistics and developing applied systems for natural text processing. This paper is aimed at solving the need described above. The paper goal is to develop model of an intelligent information resource based on modern methods of natural language processing (IIR NLP). The main goal of IIR NLP is to render convenient valuable access for specialists in the field of computational linguistics. The originality of our proposed approach is that the developed ontology of the subject area “NLP” will be used to systematize all the above knowledge, data, information resources and organize meaningful access to them, and semantic web standards and technology tools will be used as a software basis.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
Ontologies are being used to organize information in many domains like artificial intelligence,
information science, semantic web, library science. Ontologies of an entity having different information
can be merged to create more knowledge of that particular entity. Ontologies today are powering more
accurate search and retrieval in websites like Wikipedia etc. As we move towards the future to Web 3.0,
also termed as the semantic web, ontologies will play a more important role.
Ontologies are represented in various forms like RDF, RDFS, XML, OWL etc. Querying ontologies can
yield basic information about an entity. This paper proposes an automated method for ontology creation,
using concepts from NLP (Natural Language Processing), Information Retrieval and Machine Learning.
Concepts drawn from these domains help in designing more accurate ontologies represented using the
XML format. This paper uses document classification using classification algorithms for assigning labels
to documents, document similarity to cluster similar documents to the input document, together, and
summarization to shorten the text and keep important terms essential in making the ontology. The module
is constructed using the Python programming language and NLTK (Natural Language Toolkit). The
ontologies created in XML will convey to a lay person the definition of the important term's and their
lexical relationships.
Towards Ontology Development Based on Relational Databaseijbuiiir1
Ontology is defined as the formal explicit specification of a shared conceptualization. It has been widely used in almost all fields especially artificial intelligence, data mining, and semantic web etc. It is constructed using various set of resources. Now it has become a very important task to improve the efficiency of ontology construction. In order to improve the efficiency, need an automated method of building ontology from database resource. Since manual construction is found to be erroneous and not up to the expectation, automatic construction of ontology from database is innovated. Then the construction rules for ontology building from relational data sources are put forward. Finally, ontology for �automated building of ontology from relational data sources� has been implemented
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING cscpconf
In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used toface the great challenge of representing the semantics of data, in order to bring the actual web to its full
power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. In deed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the
main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former
analysis the concept’s names and the later analysis their properties. Each one of these two sub-modules is
it self based on the combination of lexical and semantic similarity measures.
Ontology learning techniques and applications computer science thesis writing...Tutors India
At Tutors India, we offer Computer science and Information Technology Research Guidance services – We deliver exceptional work where your dissertation will deserve publication without significant reworking or alternation.
For #Enquiry
https://www.tutorsindia.com
info@tutorsindia.com
(Whatsapp): +91-8754446690
(UK): +44-1143520021
IDENTIFYING THE SEMANTIC RELATIONS ON UNSTRUCTURED DATAijistjournal
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic concepts that characterizes the domain as well as their definitions and interrelationships. This paper will describe some algorithms for identifying semantic relations and constructing an Information Technology Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences. We then extract these sentences based on English pattern in order to build training set. We use a random sample among 245 categories of ACM to evaluate our results. Results generated show that our system yields superior performance.
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information
Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology
is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic
concepts that characterizes the domain as well as their definitions and interrelationships. This paper will
describe some algorithms for identifying semantic relations and constructing an Information Technology
Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed
based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our
algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language
Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences.
We then extract these sentences based on English pattern in order to build training set. We use a
random sample among 245 categories of ACM to evaluate our results. Results generated show that our
system yields superior performance.
Association Rule Mining Based Extraction of Semantic Relations Using Markov ...dannyijwest
Ontology may be a conceptualization of a website into a human understandable, however machine-
readable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the
intentional aspects of a site, whereas the denotative part is provided by a mental object that contains
assertions about instances of concepts and relations. Semantic relation it might be potential to extract the
whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations
describe the linguistics relationships among the entities involve that is beneficial for a higher
understanding of human language. The relation can be identified from the result of concept hierarchy
extraction. The existing ontology learning process only produces the result of concept hierarchy extraction.
It does not produce the semantic relation between the concepts. Here, we have to do the process of
constructing the predicates and also first order logic formula. Here, also find the inference and learning
weights using Markov Logic Network. To improve the relation of every input and also improve the relation
between the contents we have to propose the concept of ARSRE.
Association Rule Mining Based Extraction of Semantic Relations Using Markov L...IJwest
Ontology may be a conceptualization of a website into a human understandable, however machine-readable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the intentional aspects of a site, whereas the denotative part is provided by a mental object that contains assertions about instances of concepts and relations. Semantic relation it might be potential to extract the whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations describe the linguistics relationships among the entities involve that is beneficial for a higher understanding of human language. The relation can be identified from the result of concept hierarchy extraction. The existing ontology learning process only produces the result of concept hierarchy extraction. It does not produce the semantic relation between the concepts. Here, we have to do the process of constructing the predicates and also first order logic formula. Here, also find the inference and learning weights using Markov Logic Network. To improve the relation of every input and also improve the relation between the contents we have to propose the concept of ARSRE. This method can find the frequent items between concepts and converting the extensibility of existing lightweight ontologies to formal one. The experimental results can produce the good extraction of semantic relations compared to state-of-art method.
Ontology languages are used in modelling the semantics of concepts within a particular domain and the relationships between those concepts. The Semantic Web standard provides a number of modelling languages that differ in their level of expressivity and are organized in a Semantic Web Stack in such a way that each language level builds on the expressivity of the other. There are several problems when one attempts to use independently developed ontologies. When existing ontologies are adapted for new purposes it requires that certain operations are performed on them. These operations are currently performed in a semi-automated manner. This paper seeks to model categorically the syntax and semantics of RDF ontology as a step towards the formalization of ontological operations using category theory.
Top 5 MOST VIEWED LANGUAGE COMPUTING ARTICLE - International Journal on Natur...kevig
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
ONTOLOGICAL MODEL FOR CHARACTER RECOGNITION BASED ON SPATIAL RELATIONSsipij
In this paper, we present a set of spatial relations between concepts describing an ontological model for a
new process of character recognition. Our main idea is based on the construction of the domain ontology
modelling the Latin script. This ontology is composed by a set of concepts and a set of relations. The
concepts represent the graphemes extracted by segmenting the manipulated document and the relations are
of two types, is-a relations and spatial relations. In this paper we are interested by description of second
type of relations and their implementation by java code.
This paper proposes Natural language based Discourse Analysis method used for extracting
information from the news article of different domain. The Discourse analysis used the Rhetorical Structure
theory which is used to find coherent group of text which are most prominent for extracting information
from text. RST theory used the Nucleus- Satellite concept for finding most prominent text from the text
document. After Discourse analysis the text analysis has been done for extracting domain related object
and relates this object. For extracting the information knowledge based system has been used which
consist of domain dictionary .The domain dictionary has a bag of words for domain. The system is
evaluated according gold-of-art analysis and human decision for extracted information.
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
Information residing in relational databases and delimited file systems are inadequate for reuse and sharing over the web. These file systems do not adhere to commonly set principles for maintaining data harmony. Due to these reasons, the resources have been suffering from lack of uniformity, heterogeneity as well as redundancy throughout the web. Ontologies have been widely used for solving such type of problems, as they help in extracting knowledge out of any information system. In this article, we focus on extracting concepts and their relations from a set of CSV files. These files are served as individual concepts and grouped into a particular domain, called the domain ontology. Furthermore, this domain ontology is used for capturing CSV data and represented in RDF format retaining links among files or concepts. Datatype and object properties are automatically detected from header fields. This reduces the task of user involvement in generating mapping files. The detail analysis has been performed on Baseball tabular data and the result shows a rich set of semantic information.
TRANSFORMATION RULES FOR BUILDING OWL ONTOLOGIES FROM RELATIONAL DATABASEScscpconf
Relational Databases (RDB) are used as the backend database by most of information systems. RDB encapsulate conceptual model and metadata needed in the ontology construction. Schema mapping is a technique that is used by all existing approaches for ontology building from RDB.However, most of those methods use poor transformation rules that prevent advanced database mining for building rich ontologies. In this paper, we propose transformation rules for building owl ontologies from RDBs. It allows transforming all possible cases in RDBs into ontological constructs. The proposed rules are enriched by analyzing stored data to detect disjointness and
totalness constraints in hierarchies, and calculating the participation level of tables in n-ary relations. In addition, our technique is generic; hence it can be applied to any RDB. The
proposed rules were evaluated using a normalized and open RDB. The obtained ontology is richer in terms of non- taxonomic relationships.
The increased potential of the ontologies to reduce the human interference has wide range of applications. This paper identifies requirements for an ontology development platform to innovate artificially intelligent web. To facilitate this process, RDF and OWL have been developed as standard formats for the sharing and integration of data and knowledge. The knowledge in the form of rich conceptual schemas called ontologies. Based on the framework, an architectural paradigm is put forward in view of ontology engineering and development of ontology applications and a development portal designed to support ontology engineering, content authoring and application development with a view to maximal scalability in size and complexity of semantic knowledge and flexible reuse of ontology models and ontology application processes in a distributed and collaborative engineering environment.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
More Related Content
Similar to Ontology engineering of automatic text processing methods
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information
Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology
is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic
concepts that characterizes the domain as well as their definitions and interrelationships. This paper will
describe some algorithms for identifying semantic relations and constructing an Information Technology
Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed
based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our
algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language
Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences.
We then extract these sentences based on English pattern in order to build training set. We use a
random sample among 245 categories of ACM to evaluate our results. Results generated show that our
system yields superior performance.
Association Rule Mining Based Extraction of Semantic Relations Using Markov ...dannyijwest
Ontology may be a conceptualization of a website into a human understandable, however machine-
readable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the
intentional aspects of a site, whereas the denotative part is provided by a mental object that contains
assertions about instances of concepts and relations. Semantic relation it might be potential to extract the
whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations
describe the linguistics relationships among the entities involve that is beneficial for a higher
understanding of human language. The relation can be identified from the result of concept hierarchy
extraction. The existing ontology learning process only produces the result of concept hierarchy extraction.
It does not produce the semantic relation between the concepts. Here, we have to do the process of
constructing the predicates and also first order logic formula. Here, also find the inference and learning
weights using Markov Logic Network. To improve the relation of every input and also improve the relation
between the contents we have to propose the concept of ARSRE.
Association Rule Mining Based Extraction of Semantic Relations Using Markov L...IJwest
Ontology may be a conceptualization of a website into a human understandable, however machine-readable format consisting of entities, attributes, relationships and axioms. Ontologies formalize the intentional aspects of a site, whereas the denotative part is provided by a mental object that contains assertions about instances of concepts and relations. Semantic relation it might be potential to extract the whole family-tree of a outstanding personality employing a resource like Wikipedia. In a way, relations describe the linguistics relationships among the entities involve that is beneficial for a higher understanding of human language. The relation can be identified from the result of concept hierarchy extraction. The existing ontology learning process only produces the result of concept hierarchy extraction. It does not produce the semantic relation between the concepts. Here, we have to do the process of constructing the predicates and also first order logic formula. Here, also find the inference and learning weights using Markov Logic Network. To improve the relation of every input and also improve the relation between the contents we have to propose the concept of ARSRE. This method can find the frequent items between concepts and converting the extensibility of existing lightweight ontologies to formal one. The experimental results can produce the good extraction of semantic relations compared to state-of-art method.
Ontology languages are used in modelling the semantics of concepts within a particular domain and the relationships between those concepts. The Semantic Web standard provides a number of modelling languages that differ in their level of expressivity and are organized in a Semantic Web Stack in such a way that each language level builds on the expressivity of the other. There are several problems when one attempts to use independently developed ontologies. When existing ontologies are adapted for new purposes it requires that certain operations are performed on them. These operations are currently performed in a semi-automated manner. This paper seeks to model categorically the syntax and semantics of RDF ontology as a step towards the formalization of ontological operations using category theory.
Top 5 MOST VIEWED LANGUAGE COMPUTING ARTICLE - International Journal on Natur...kevig
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
ONTOLOGICAL MODEL FOR CHARACTER RECOGNITION BASED ON SPATIAL RELATIONSsipij
In this paper, we present a set of spatial relations between concepts describing an ontological model for a
new process of character recognition. Our main idea is based on the construction of the domain ontology
modelling the Latin script. This ontology is composed by a set of concepts and a set of relations. The
concepts represent the graphemes extracted by segmenting the manipulated document and the relations are
of two types, is-a relations and spatial relations. In this paper we are interested by description of second
type of relations and their implementation by java code.
This paper proposes Natural language based Discourse Analysis method used for extracting
information from the news article of different domain. The Discourse analysis used the Rhetorical Structure
theory which is used to find coherent group of text which are most prominent for extracting information
from text. RST theory used the Nucleus- Satellite concept for finding most prominent text from the text
document. After Discourse analysis the text analysis has been done for extracting domain related object
and relates this object. For extracting the information knowledge based system has been used which
consist of domain dictionary .The domain dictionary has a bag of words for domain. The system is
evaluated according gold-of-art analysis and human decision for extracted information.
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
Information residing in relational databases and delimited file systems are inadequate for reuse and sharing over the web. These file systems do not adhere to commonly set principles for maintaining data harmony. Due to these reasons, the resources have been suffering from lack of uniformity, heterogeneity as well as redundancy throughout the web. Ontologies have been widely used for solving such type of problems, as they help in extracting knowledge out of any information system. In this article, we focus on extracting concepts and their relations from a set of CSV files. These files are served as individual concepts and grouped into a particular domain, called the domain ontology. Furthermore, this domain ontology is used for capturing CSV data and represented in RDF format retaining links among files or concepts. Datatype and object properties are automatically detected from header fields. This reduces the task of user involvement in generating mapping files. The detail analysis has been performed on Baseball tabular data and the result shows a rich set of semantic information.
TRANSFORMATION RULES FOR BUILDING OWL ONTOLOGIES FROM RELATIONAL DATABASEScscpconf
Relational Databases (RDB) are used as the backend database by most of information systems. RDB encapsulate conceptual model and metadata needed in the ontology construction. Schema mapping is a technique that is used by all existing approaches for ontology building from RDB.However, most of those methods use poor transformation rules that prevent advanced database mining for building rich ontologies. In this paper, we propose transformation rules for building owl ontologies from RDBs. It allows transforming all possible cases in RDBs into ontological constructs. The proposed rules are enriched by analyzing stored data to detect disjointness and
totalness constraints in hierarchies, and calculating the participation level of tables in n-ary relations. In addition, our technique is generic; hence it can be applied to any RDB. The
proposed rules were evaluated using a normalized and open RDB. The obtained ontology is richer in terms of non- taxonomic relationships.
The increased potential of the ontologies to reduce the human interference has wide range of applications. This paper identifies requirements for an ontology development platform to innovate artificially intelligent web. To facilitate this process, RDF and OWL have been developed as standard formats for the sharing and integration of data and knowledge. The knowledge in the form of rich conceptual schemas called ontologies. Based on the framework, an architectural paradigm is put forward in view of ontology engineering and development of ontology applications and a development portal designed to support ontology engineering, content authoring and application development with a view to maximal scalability in size and complexity of semantic knowledge and flexible reuse of ontology models and ontology application processes in a distributed and collaborative engineering environment.
Similar to Ontology engineering of automatic text processing methods (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Heap Sort (SS).ppt FOR ENGINEERING GRADUATES, BCA, MCA, MTECH, BSC STUDENTS
Ontology engineering of automatic text processing methods
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 6, December 2023, pp. 6620~6628
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i6.pp6620-6628 6620
Journal homepage: http://ijece.iaescore.com
Ontology engineering of automatic text processing methods
Zhanna Sadirmekova1,2
, Jamalbek Tussupov3
, Aslanbek Murzakhmetov1
, Gulkiz Zhidekulova1
,
Aigul Tungatarova1
, Murat Tulenbayev1
, Shynar Akhmetzhanova1
, Zhanar Altynbekova4
,
Gauhar Borankulova1
1
Department of Information Systems, Faculty of Information Technology, M.Kh. Dulaty Taraz Regional University, Taraz, Kazakhstan
2
Institute of Information and Computational Technologies, Committee of Science of the Ministry of Education and Science of the
Republic of Kazakhstan, Almaty, Kazakhstan
3
Department of Information Systems, Faculty of Information Technology, L.N. Gumilyov Eurasian National University,
Astana, Kazakhstan
4
Department of Computer Science, Kazakh National Women’s Teacher Training University, Almaty, Kazakhstan
Article Info ABSTRACT
Article history:
Received Apr 14, 2023
Revised May 3, 2023
Accepted May 6, 2023
Currently, ontologies are recognized as the most effective means of
formalizing and systematizing knowledge and data in scientific subject area
(SSA). Practice has shown that using ontology design patterns is effective in
developing the ontology of scientific subject areas. This is due to the fact
that scientific subject areas ontology, as a rule, contains a large number of
typical fragments that are well described by patterns of ontology design. In
the paper, we present an approach to ontology engineering of automatic text
processing methods based on ontology design patterns. In order to get an
ontology that would describe automatic text processing sufficiently fully, it
is required to process a large number of scientific publications and
information resources containing information from modeling area. It is
possible to facilitate and speed up the process of updating ontology with
information from such sources by using lexical and syntactic patterns of
ontology design. Our ontology of automatic text processing will become the
conceptual basis of an intelligent information resource on modern methods
of automatic text processing, which will provide systematization of all
information on these methods, its integration into a single information space,
convenient navigation through it, as well as meaningful access to it.
Keywords:
Automatic text processing
methods
Domain ontology
Ontology design
patterns
Content patterns
Ontology completion
Ontology engineering
This is an open access article under the CC BY-SA license.
Corresponding Author:
Zhanna Sadirmekova
Department of Information Systems, Faculty of Information Technology, M.Kh. Dulaty Taraz Regional
University
60 Tole bi str., Taraz, 080000, Kazakhstan
Email: janna_1988@mail.ru
1. INTRODUCTION
Ontologies are extensively used to formalize knowledge in the areas of scientific subjects. With the
aid of ontology, it is possible to assure their uniform and consistent description as well as the convenient
presentation of all the required ideas of the simulated domain. A scientific subject area (SSA) is understood
as a subject area (SA) that covers a specific scientific discipline or knowledge, including its objects and
subjects of research, characteristics and used research methods. Currently numerous strategies and
approaches are suggested to speed up the time-consuming process of developing an ontology for any topic
area [1]–[4]. Accordingly, intensively developing an approach based on the ontology design patterns
application (ODP) [5]–[8]. According to this approach, ODP is documented descriptions of proven solutions
to typical problems of ontology modeling [9]. They are developed to assist and streamline the creation of
2. Int J Elec & Comp Eng ISSN: 2088-8708
Ontology engineering of automatic text processing methods (Zhanna Sadirmekova)
6621
ontologies and aid developers in avoiding common blunders in ontology modeling. Despite the fact that using
ontology design patterns reduces the need for human resources and raises the standard of ontologies being
created, currently only one method for building ontologies i.e. eXtreme design methodology [10], suggested
within the NeOn project [11], openly declares the use of ODP.
Note also that there are not many ontology development tools that support the use of ODP. These
include, a plugin for NeOn project development tools of ontology, as well as a plugin for the web protégé
ontology editor [12], [13]. However, these funds cover only a part of possible problems associated with
patterns. So, there are no instruments supporting the patterns searching, construction and extraction from
ontologies, and very few instruments supporting the patterns collection, discussion and also dissemination.
To some extent, the latter include of ontology design patterns catalogs [14]–[17], which have also actively
developed now.
The paper considers an approach to the implementation of such kind of ontology design patterns as
content patterns [18], which play an important role in the development of ontologies of modern methods of
automatic text processing (ATP) proposed by the authors. The ontology of ATP modern methods includes
both classical ATP methods and methods using machine learning. The papers [19]–[21] existing ontologies
containing ATP methods were analyzed. At the moment, there is an ontology of machine learning [22], [23]
which contains a small set of ATP methods based on machine learning. However, existing ontologies cannot
give an idea of the whole variety of this type of method. In addition, many new methods and models have
recently appeared that have not yet been reflected in previously developed ontologies. To systematize data
and information resources, to organize meaningful access to them, the ontology of the subject area
“automatic text processing” developed within the framework of this paper will be used, and software basis
will be used as a standard and tool of semantic web technologies [24].
2. PROBLEM STATEMENT AND ONTOLOGY MAIN DEFINITIONS
Let be given the SA ontology, the replenishing rules of this ontology, the syntactic and semantic
model of the SA language, the terms dictionary and input data in final text form in a natural language
containing information for replenishing the ontology. We consider that ontology 𝑂 subject area includes the
following elements: i) a finite non-empty set of classes 𝐶𝑂 that describe the subject area concepts; ii) a finite
set of data domains 𝐷𝑂; and iii) a finite set of attributes with names from the set 𝐷𝑎𝑡𝑂∪𝑅𝑒𝑙𝑂, while the data
attributes from 𝐷𝑎𝑡𝑂 accept values from some data domain in 𝐷𝑂, and the values of relationship attributes
from 𝑅𝑒𝑙𝑂 that model relationships between classes are instances of classes from 𝐶𝑂.
Every class 𝑐 ∈ 𝐶𝑜 determined by set attributes: 𝑐 = (𝐷𝑎𝑡𝑐, 𝑅𝑒𝑙𝑐), where each data attribute
𝛼 ∈ 𝐷𝑎𝑡𝑐 ⊆ 𝐷𝑎𝑡𝑜 mapped domain 𝑑𝛼
𝑐
⊆ 𝐷𝑜 with values in 𝑉𝑑𝛼
𝑐 , and every attribute relationship 𝑝 ∈ 𝑅𝑒𝑙𝑐 ⊆
𝑅𝑒𝑙𝑜 accepts values classes 𝑐𝑝 ⊆ 𝐶𝑜. All attributes set in class 𝑐 denoted as 𝐴𝑡𝑟𝑐 = 𝐷𝑎𝑡𝑐 ∪ 𝑅𝑒𝑙𝑐. For
attribute 𝛾 his class is denoted as 𝑐𝛾
and his values set as 𝐷𝛾
. Among class attributes, singled out non-empty
of key attributes set 𝐴𝑡𝑟𝑐
𝐾
, which can be attributes of both data and relationships. Set 𝑎 = (𝑐𝑎, 𝐷𝑎𝑡𝑎, 𝑅𝑒𝑙𝑎) is
an instance of class 𝑐𝑎 = (𝐷𝑎𝑡𝑐𝑎
, 𝑅𝑒𝑙𝑐𝑎
)(𝑎 ∈ 𝑐𝑎), if and only if every attribute data in 𝐷𝑎𝑡𝑎 has name 𝛼𝑎 ∈
𝐷𝑎𝑡𝑐𝑎
with values 𝑉𝛼𝑎
from 𝑉𝑑𝛼𝑎
𝑐𝑎 , and every attribute relationship in 𝑅𝑒𝑙𝑎 has name 𝑝𝑎
∈ 𝑅𝑒𝑙𝑐𝑎
with values
𝑉𝑝𝑎
as instances of classes from 𝑐𝑝. Key attributes data are always unambiguous, i.e. every key attribute in
each instance of ontology maybe have only one value. Key attribute relations correspond to bijective
relations. We consider ontology without synonyms classes and attributes data, i.e. ∀𝛼1, 𝛼2 ∈ 𝐷𝑎𝑡𝑜: 𝑑𝛼1
≠
𝑑𝛼2
and ∀𝑐1, 𝑐2 ∈ 𝐶𝑜: 𝐴𝑡𝑟𝑐1
≠ 𝐴𝑡𝑟𝑐2
. Class 𝑐2 inherits class 𝑐1 if and only if ∀𝑎 ∈ 𝑐2: 𝑎 ∈ 𝑐1.
Informational content 𝐼𝐶𝑜 ontology 𝑂; this a set of copies classes of this ontology. Problem
replenishment ontology is the calculation of informational content by given input data for a given ontology.
There we define a set of 𝐴 information objects (i: objects) retrieved from input data and relevant copies
ontology. Every informational object 𝑎 ∈ 𝐴 has a view (𝑐𝑎, 𝐷𝑎𝑡𝑎, 𝑅𝑒𝑙𝑎, 𝐺𝑎, 𝑃𝑎), where:
a. Class 𝑐𝑎 ∈ 𝐶𝑜
b. 𝐷𝑎𝑡𝑜 is a set of attributes data 𝛼𝑎 = (𝛼, 𝑉𝑎𝑙𝛼𝑎
), where
− Name 𝛼 ∈ 𝐷𝑎𝑡𝑐𝑎
− 𝑉𝑎𝑙𝛼𝑎
is set of attributes data 𝑣
̅ = (𝑣𝑣
̅, 𝑠𝑣
̅), where:
values data 𝑣𝑣
̅ ∈ 𝑑𝛼
𝑐𝑎
and 𝑉𝛼𝑎
= {𝑣𝑣
̅|𝑣
̅ ∈ 𝑉𝑎𝑙𝛼𝑎
} and 𝑠𝑣
̅ is structural information (position in input
data)
3. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 6, December 2023: 6620-6628
6622
c. 𝑅𝑒𝑙𝑎 is set of attributes relations 𝑝𝑎
= (𝑝, 𝑉𝑝𝑎
), where
− name 𝑝 ∈ 𝑅𝑒𝑙𝑐𝑎
− 𝑉𝑝𝑎
is i - objects set of class 𝑐𝑜
̅ ∈ 𝑐𝑝𝑎
d. 𝐺𝑎 is grammatical information (morphological and syntactic signs);
e. 𝑃𝑎 is structural information (many positions in input data).
Denote a set of all attributes i-object 𝑎 as 𝐴𝑡𝑟𝑎 = 𝐷𝑎𝑡𝑎 ∪ 𝑅𝑒𝑙𝑎. Every i-object natural way
corresponds to some instance ontology: if 𝑎 = (𝑐𝑎, 𝐷𝑎𝑡𝑎, 𝑅𝑒𝑙𝑎, 𝐺𝑎, 𝑃𝑎) is some i-object, that his
corresponding copy ontology is 𝑎′ = (𝑐𝑎, 𝐷𝑎𝑡𝑎′, 𝑅𝑒𝑙𝑎′). Every attribute 𝛼′ ∈ 𝐷𝑎𝑡𝑎′ has values 𝑉𝛼𝑎
. Every 𝑝 ∈
𝑅𝑒𝑙𝑎′ has values 𝑉𝑝𝑎
.
3. DEVELOPMENT OF ONTOLOGIES SUBJECT AREA “AUTOMATIC TEXT PROCESSING”
The ontology of “automatic text processing” subject areas as shown in Figure 1 includes the
systematization of modern ATP methods, a specification of properties, relationships between them,
techniques and areas of their publications, and application. Systematization of all information on the
specified methods can be carried out on the next basics: by purpose (solved applied problem types), and by
areas of use. The core of the ATP ontology is formed by the ATP class, which defines the main properties of
the ATP methods, and its subclasses, which are used to represent the types of solutions to problems using
methods. Such classes are machine translation, abstracting, annotation, sentiment analysis, rubrication,
classification and text pasteurization, and building knowledge bases.
Figure 1. Ontology of the subject area “automatic text processing”
To build an ontology and its initial content, a technique was used to develop ontologies using basic
ontologies that include only the most general entities that do not depend on a specific subject area and ODP
[25], [26] which are documented descriptions of proven solutions to typical problems of ontology modeling
in practice. The use of such patterns not only improves the quality but also greatly facilitates the development
of an ontology since it can involve experts in the modeled area who do not have the skills of ontology
modeling. To assess the quality of the ontology was developed a methodology [27], on the basis of which the
involved experts carried out an experimental assessment of the created ontology, including an assessment of
the degree of agreement of the experts. Metrics for evaluating various ontology properties that do not require
expert work are also considered. As a result of the research, we propose a methodology for the development
of intelligent information resource of automatic text processing (IIR ATP), it offers the architecture and
algorithm for the development of IIR ATP. The principles and approaches underlying the methodology
determine the following main features: i) focus on semi-formalized software; ii) independence from software;
iii) focus on the maximum use of ready-made developments (both copyright and third-party);
iv) use of semantic web technologies and service-oriented approach, information system supporting scientific
and educational activities (ISSEA) development technologies; v) use of the ISSEA shell as a framework for
4. Int J Elec & Comp Eng ISSN: 2088-8708
Ontology engineering of automatic text processing methods (Zhanna Sadirmekova)
6623
the future IIR ATP; and vi) openness and scalability of the proposed tools; convenience and low entry
threshold for the use of the proposed funds. The format for describing ATP methods is supplemented with
elements that serve to describe the context development and use of ODP. For these purposes, the ontology of
ATP methods includes the following classes: scope, activity, task, publication, person, organization, and
information resource. To associate methods with instances of these classes, the ontology of ATP includes
relations that allow link ATP with SA, persons, organizations, and projects in which they are used, as well as
with publications and information resources where they are described. The ontology describes most fully the
ATP methods implemented in the proposed IIR ATP system [28] using the following ODP templates:
structural logical patterns, content patterns, presentation patterns, and lexico-syntactic patterns (LSP)
[29]–[31].
Necessity of use structural logical patterns arose due to the lack of expressive means in the web
ontology language (OWL) [32] for representing complex entities and constructions that are relevant in the
construction of ATP ontologies, in particular, many-place and attributed relations (binary relations with
attributes), as well as ranges of valid values determined by the developer of the ontology. Pattern
specialization can consist of renaming, in specifying the names and values of its properties (attributes and
relations). Figure 2 shows the specialization of patterns on the example of the structural logical pattern
“binary attributed relation”. The central place in this pattern is occupied by the auxiliary class Relation with
attributes, with which the base classes that model the arguments of a binary relation are associated, through
the relationships “is an argument” and “has an argument”. At the same time, in the pattern (in link labels) it is
indicated that there should be one such argument. The attributes of a binary attributed relation are modeled
by the properties of the relation class with the attributes “has an attribute” and “has an attribute from
domain”. In general, such a relationship may have no attributes, as reflected in the link labels that represent
those properties. The concretization (meaning) of the pattern consists in substituting specific property values
into it.
Figure 2. Binary attributed relationship patterns and its specialization
The pattern “area of allowable values” is intended to set the possible values of any property of the
class, when is known in advance the whole values set (usually string) and can be stated at the stage of
develop. Content patterns are designed to uniform provide and consistent of concepts representation used in
ATP and their properties. Content templates are to provide a uniform and consistent representation of ATP
concepts and their properties. Such patterns were developed for concepts that are typical for most SSA:
subject of research, object of study, section of science, task, method, scientific result, project, activity,
organization, person, publication, and information resource. For each of these patterns, a set of proficiency
testing questions is defined. With these questions, the optional and mandatory compositions of pattern
elements ontology are identified and requirements for them are described, which are presented in the
restrictions and axioms forms. For each pattern representing the concept of SA, a set of key attributes has
been compiled that uniquely identify concept specific instance. Figure 3 shows a pattern for representing
“ATP methods” concept. The pattern description elements are represented by the obligatory classes of the
ontology task, science section, organization and person, optional classes activity, and scientific result, and the
relations “solves”, “used in”, “implemented in”, and “has an author”. In pattern representing the concept of
“ATP methods”, there is one key attribute “name”.
5. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 6, December 2023: 6620-6628
6624
Figure 3. “Method” concept patterns
Competency examples assessment questions representing ATP methods content pattern: “what is
methods name?”, “who is methods author?”, “when was method proposed?”, “what problems are solved
using the method?”, “what activity uses the method?”, “in what scientific results is the method
implemented?”, “who is using the method?”, “what organizations use the method?”.
4. ARCHITECTURE OF AN INTELLIGENT RESOURCE BASED ON MODERN ATP METHODS
IIR ATP consists of the following components as shown in Figure 4; an ontology of ATP methods, a
repository of ATP methods, basic ontologies repository, a dictionary of scientific lexicon, data and ontology
editors, a subsystem to automatic replenishment of an LSP based ontology. The repository of ATP methods is
built on basis of ATP methods ontology and includes realizations of ODP. At the same time, structural-
logical patterns presentation patterns, content patterns are implemented by OWL language means, while LSP
is presented in a description language on specialized template [33].
The automated ontology building system (AOBS) supports the building methods of SSA ontology
based on basic ontologies that contain the most general concepts that are typical for most SSA. For this
reason, the system consists of a repository of basic ontologies such as: scientific knowledge ontology,
scientific activity ontology, the basic ontology of problems and basic ontology of information resources [34].
All base ontologies have characteristics in OWL language. Content patterns have been developed and
included in the ATP repository for the most important basic ontologies concepts. The developed ontology
model was implemented in the Protégé 5.5.0 ontology editor, Figure 5.
Figure 4. Architecture of the automated ontology building system
6. Int J Elec & Comp Eng ISSN: 2088-8708
Ontology engineering of automatic text processing methods (Zhanna Sadirmekova)
6625
Figure 5. Protege editor
The system includes data editor for convenient use of ATP methods, that enables replenishing the
ontology of the SSA by concrete definition of content patterns included in ATP methods repository. The
dictionary of scientific lexicon contains semantically marked terms used in scientific texts to describe the
essence of various ATP methods. It is used to extract subject vocabulary from texts and automatically
generate an SA dictionary, as well as for subsequent automatic text analysis using LSP. The subsystem of
automatic ontology replenishment is intended to enter information extracted from texts in natural language
into SSA ontology. For this, LSP is used, built on content patterns basis and general scientific lexicon
dictionary intelligent information resource is designed to systematize information about modern methods of
automatic text processing and provide meaningful access to it. The work of the resource is organized on ATP
ontology basics, which is its conceptual basis.
The left side of Figure 6 shows the class hierarchy of the ATP ontology. The right side shows a
description of the ATP method, which includes the name of the method, a description of its purpose, a link to
the OWL view, a link to a graphical representation, a set of questions for assessing competence, and links to
projects in which it was developed and used. In addition, IIR ATP is an AOBS user interface that provides
users with access to all repositories and editors that support the development of the SSA ontology, as well as
the subsystem of automatic ontology replenishment based on LSP.
Figure 6. Intelligent information resource
7. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 6, December 2023: 6620-6628
6626
5. CONCLUSION
This paper describes the ontology model of an intelligent information resource developed by the
authors according to modern methods of automatic text processing. The ontology systematizes information
about the area of knowledge “Automatic text processing” and provides developers of IIR ATP with a single
conceptual basis. The Ontology Design Patterns used in this approach appeared as a result of solving
ontology modeling problems that the authors of the paper encountered in the process of developing
ontologies for various scientific subject areas. The use of ontology design patterns makes it possible to
provide a uniform and consistent representation of all the entities of the scientific subject areas of ontology,
to reduce the number of errors in ontology modeling, to increase the “comprehensibility” of the ontology by
developers, and thus to provide the possibility of collective development of ontologies. Since the use of
Ontology Design Patterns greatly simplifies and facilitates the development of the ontology of the scientific
subject areas, it can involve experts in a particular scientific subject area who do not have the skills of
ontology modeling, which can significantly speed up the development of the ontology. Our further research
is aimed at the full-scale implementation of the subsystem for automatic ontology replenishment based on
lexico-syntactic patterns.
ACKNOWLEDGEMENTS
This research was funded by a grant for Financing scientific and technical projects for 2022-2024,
from the Science Committee of the Ministry of Science and higher education of the Republic of Kazakhstan,
grant number “AP14972834”, (Grant No. AP14972834).
REFERENCES
[1] A. Benarab, J. Sun, F. Rafique, and A. Refoufi, “Global ontology entities embeddings,” IEEE Transactions on Knowledge and
Data Engineering, pp. 1–12, 2023, doi: 10.1109/TKDE.2023.3235779.
[2] A. De Nicola and M. Missikoff, “A lightweight methodology for rapid ontology engineering,” Communications of the ACM,
vol. 59, no. 3, pp. 79–86, Feb. 2016, doi: 10.1145/2818359.
[3] S. Arsovski, B. Markoski, P. Pecev, D. Lacmanović, and N. Petrovački, “Advantages of using an ontological model of the state
development funds,” International Journal of Computers, Communications and Control, vol. 9, no. 3, pp. 261–275, Apr. 2014,
doi: 10.15837/ijccc.2014.3.260.
[4] Y. Zagorulko and O. Borovikova, “Technology of Ontology Building for Knowledge Portals on Humanities,” in Lecture Notes in
Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6581,
Springer Berlin Heidelberg, 2011, pp. 203–216.
[5] Y. A. Zagorulko, O. I. Borovikova, and G. B. Zagorulko, “Application of ontology design patterns in the development of the
ontologies of scientific subject domains (in Russian),” in Data Analytics and Management in Data Intensive Domains: XIX
International Conference, 2017, pp. 332–340.
[6] A. Gangemi and V. Presutti, “Ontology design patterns,” in Handbook on Ontologies, 2009, pp. 221–243.
[7] A. Sattar, E. Salwana, M. Nazir, M. Ahmad, and A. Kamil, “Comparative analysis of methodologies for domain ontology
development: a systematic review,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 5,
pp. 99–108, 2020, doi: 10.14569/IJACSA.2020.0110515.
[8] M. N. Asim, M. Wasim, M. U. G. Khan, W. Mahmood, and H. M. Abbasi, “A survey of ontology learning techniques and
applications,” The Journal of Biological Databases and Curation, Jan. 2018, doi: 10.1093/database/bay101.
[9] A. Bogdanchikov, D. Ayazbayev, and I. Varlamis, “Classification of scientific documents in the Kazakh language using deep neural
networks and a fusion of images and text,” Big Data and Cognitive Computing, vol. 6, no. 4, 2022, doi: 10.3390/bdcc6040123.
[10] E. Blomqvist, K. Hammar, and V. Presutti, “Engineering ontologies with patterns - the eXtreme design methodology,” Ontology
Engineering with Ontology Design Patterns, pp. 23–50, 2016.
[11] “NeOn project,” NeOn Project, 2018. http://neon-project.org/nw/Welcome_to_the_NeOn_Project.html (accessed Feb. 17, 2022).
[12] “A free, open-source ontology editor and framework for building intelligent systems,” Protégé. http://protege.stanford.edu/
(accessed Feb. 18, 2022).
[13] K. Hammar, “Ontology design patterns in WebProtégé,” in CEUR Workshop Proceedings, 2015, vol. 1486.
[14] S. Sandugash, T. Jamalbek, S. Madina, Y. Akbota, and A. Ainur, “Building a standard model of an information system for
working with documents on scientific and educational activities,” International Journal of Advanced Computer Science and
Applications, vol. 12, no. 9, pp. 445–455, 2021.
[15] L. Dodds and I. Davis, “Linked data patterns: A pattern catalogue for modelling, publishing, and consuming linked data,” 2022.
http://patterns.dataincubator.org/book (accessed Feb. 21, 2022).
[16] “Ontology design patterns (ODPs) public catalog,” ODPs, 2009. http://odps.sourceforge.net (accessed Feb. 25, 2022).
[17] Z. Sadirmekova, A. Yerimbetova, and A. Ibraimkulov, “Development of an information model of the portal of scientific
knowledge by means of semantic web technology,” in 2022 7th International Conference on Computer Science and Engineering
(UBMK), Sep. 2022, pp. 182–187, doi: 10.1109/UBMK55850.2022.9919463.
[18] V. Presutti, E. Daga, A. Gangemi, and E. Blomqvist, “Extreme design with content ontology design patterns,” CEUR Workshop
Proceedings, vol. 516, pp. 83–97, 2009.
[19] V. S. Sadanand, K. R. R. Guruvyas, P. P. Patil, J. J. Acharya, and S. G. Suryakanth, “An automated essay evaluation system using
natural language processing and sentiment analysis,” International Journal of Electrical and Computer Engineering (IJECE),
vol. 12, no. 6, pp. 6585–6593, Dec. 2022, doi: 10.11591/ijece.v12i6.pp6585-6593.
[20] T. Gherasim, M. Harzallah, G. Berio, and P. Kuntz, “Methods and tools for automatic construction of ontologies from textual
resources: a framework for comparison and its application,” in Studies in Computational Intelligence, vol. 471, Springer Berlin
Heidelberg, 2013, pp. 177–201.
8. Int J Elec & Comp Eng ISSN: 2088-8708
Ontology engineering of automatic text processing methods (Zhanna Sadirmekova)
6627
[21] O. Zhezherun and M. Ryepkin, “Automatic generation of ontologies based on articles written in Ukrainian language,” NaUKMA
Research Papers. Computer Science, vol. 5, pp. 12–15, Feb. 2023, doi: 10.18523/2617-3808.2022.5.12-15.
[22] J. Braga, J. L. R. Dias, and F. Regateiro, “A machine learning ontology,” Preprint, Frenxiv, vol. 5, pp. 2–10, Oct. 2021, doi:
10.31226/osf.io/rc954.
[23] J. Luo, D. Yu, and Z. Dai, “A latent dirichlet allocation and fuzzy clustering based machine learning model for text thesaurus,”
International Journal Of Computers Communications and Control, vol. 15, no. 2, pp. 1–16, Mar. 2020, doi:
10.15837/ijccc.2020.2.3811.
[24] “W3C.” https://www.w3.org/Consortium/membership.html (accessed Jan. 19, 2022).
[25] B. T. Victorovna, T. S. Zhaksylykbayevna, M. F. Alexandrovich, Y. A. Sembekovna, S. S. Kairolliyevna, and B. A. Muratovna,
“Link grammar parser for Turkic languages and algorithms for estimation the relevance of documents,” in 2016 IEEE 10th
International Conference on Application of Information and Communication Technologies (AICT), Oct. 2016, pp. 1–4, doi:
10.1109/ICAICT.2016.7991663.
[26] A. Е. Misnik, “Metagraphs for ontological engineering of complex systems,” Journal of Applied Informatics, vol. 17, no. 2,
pp. 120–132, Mar. 2022, doi: 10.37791/2687-0649-2022-17-2-120-132.
[27] A. G. Batyrkhanov, Z. B. Sadirmekova, M. A. Sambetbayeva, A. N. Nurgulzhanova, Z. S. Ismagulova, and A. S. Yerimbetova,
“Development of methods and technologies for creating intelligent scientific and educational internet resources,” Bulletin of
Electrical Engineering and Informatics, vol. 11, no. 5, pp. 2968–2977, Oct. 2022, doi: 10.11591/eei.v11i5.3075.
[28] S. Belov, D. Zrelova, P. Zrelov, and V. Korenkov, “Overview of methods for automatic natural language text processing,” System
Analysis in Science and Education, no. 3, pp. 8–22, Sep. 2020, doi: 10.37005/2071-9612-2020-3-8-22.
[29] R. De Almeida Falbo, “SABiO: systematic approach for building ontologies,” in CEUR Workshop Proceedings, 2014, vol. 1301.
[30] A. Fedotov, V. Barakнnin, A. Murzakhmetov, and I. Milyuk, “Modelling of process information dissemination and its impact
dynamics to mass consciousness,” Journal of Theoretical and Applied Information Technology, vol. 98, no. 23, pp. 3691–3702,
2020.
[31] A. Murzakhmetov, A. Dyusembaev, U. Umbetov, M. Abdimomynova, and K. Shekeyeva, “Study of the innovations diffusion on
the base of naming game mathematical model,” Compusoft, vol. 9, no. 1, pp. 3547–3551, 2020, doi: 10.6084/ijact.v9i1.1036.
[32] G. Antoniou and F. van Harmelen, Web ontology language: OWL BT - handbook on ontologies. 2004.
[33] Z. H. B. Sadirmekova, J. A. Tussupov, M. A. Sambetbaveva, A. S. Yerimbetova, and Y. A. Zaeorulko, “Features of the
development of intelligent scientific and educational internet resources,” in 2021 6th International Conference on Computer
Science and Engineering (UBMK), Sep. 2021, pp. 389–394, doi: 10.1109/UBMK52708.2021.9558999.
[34] E. Sidorova, “Ontology-based approach to modeling the process of extracting information from text,” Ontology of Designing,
vol. 8, no. 1, pp. 134–151, 2018, doi: 10.18287/2223-9537-2018-8-1-134-151.
BIOGRAPHIES OF AUTHORS
Zhanna Sadirmekova holds a Ph.D. in Information Systems from L.N.
Gumilyov Eurasian National University, Astana, Kazakhstan. Successfully defended her thesis
on “Development of technology for integration of information systems to support scientific
and educational activities based on metadata and ontological model of the subject area”. She is
currently pursuing postgraduate studies at the Federal State Autonomous Educational
Institution of Higher Education “Novosibirsk National Research State University” in Physics
and Astronomy. Currently, she is Associate Professor of Information Systems Department at
M.Kh. Dulaty Taraz Regional University, Taraz, Kazakhstan. Research interests: information
technology in education, artificial intelligence, search, digital library, ontology. She has more
than 50 publications. h-index 3. She can be contacted at email: janna_1988@mail.ru
Jamalbek Tussupov Doctor of Physical and Mathematical Sciences, place of
defense - Kazakh National University, specialty code-01.01.06 “Mathematical logic, algebra
and number theory”. Dissertation topic: “Problems of definability and algorithmic complexity
of relations over algebraic structures”. Has over 100 publications, including: 1 manual; 4
monographs; 30 articles in journals of Scopus, 30 articles in journals of the list of COXON
and VAK of the Russian Federation; 5 certificates on official registration of computer
programs used in teaching and research practice, and h-index 5. He can be contacted at email:
tussupov@mail.ru.
Aslanbek Murzakhmetov received Ph.D. degree in 2022 from al-Farabi Kazakh
National University in specialty Information Systems. Currently, he is Associate Professor of
Information Systems Department at M.Kh. Dulaty Taraz Regional University, Taraz,
Kazakhstan. He has more than 20 scientific papers. He was a junior researcher at Al-Farabi
KazNU and local coordinator of the LMPI project of the Erasmus+ program at Dulaty
University. Research interests: pattern recognition and classification; optimization systems,
big data processing, multi-agent systems, stochastic programming methods, methods of
operations research. He can be contacted at email: aslanmurzakhmet@gmail.com.
9. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 6, December 2023: 6620-6628
6628
Gulkiz Zhidekulova candidate of technical sciences, currently, she is Associate
Professor of Information Systems Department at M.Kh. Dulaty Taraz Regional University,
Taraz, Kazakhstan. She has more than 110 scientific papers, including 4 papers in Web of
Science and Scopus rating publications, 2 monographs, 7 textbooks and 2 copyright
certificates of intellectual property. H-index–1. She was the executor of the project of search
and initiative research work on the topic “Development of software” Unified information
retrieval system of electronic archive “for the State Archive of Zhambyl region”. She can be
contacted at email: gul2006@mail.ru.
Aigul Tungatarova defended her Ph.D. thesis at the Altynsarin National
Academy of Education, Astana. Currently, she is an Associate Professor of the Department of
Information Systems of M.Kh Dulaty Taraz Regional University, Taraz, Kazakhstan.
Research interests: information technologies in education, information security, information
protection, methods of teaching disciplines of the specialty. She has more than 100
publications, including 12 articles in the journals of the Scopus, 11 articles in the journals of
the Higher Attestation Commission of the Republic of Kazakhstan; H-index-2. She can be
contacted by e-mail: at.tu@mail.ru.
Murat Tulenbayev Doctor of Technical Sciences, Professor of Information
Systems Department at M.Kh. Dulaty Taraz Regional University, Taraz, Kazakhstan. He has
more than 113 scientific papers. Research interests: digital filtering and information
processing (including wavelet technologies), information and measurement systems, computer
simulation and process management, environmental monitoring information systems,
information technologies in education (including distance education technologies). He can be
contacted at email: mtulenbayevt@mail.ru.
Shynar Akhmetzhanova candidate of technical sciences, currently, she is
Associate Professor of Information Systems Department at M.Kh. Dulaty Taraz Regional
University, Taraz, Kazakhstan. She has more than 100 scientific papers, including 2 papers in
Web of Science and Scopus rating publications, 2 monographs, 2 textbooks and 2 copyright
certificates of intellectual property. She can be contacted at email: shina_70@mail.ru.
Zhanar Altynbekova is a 2nd year doctoral student at the Kazakh National
Pedagogical University for Girls 8D01502- Informatics. Has more than 15 scientific works,
including 2 works published in Web of Science and Scopus rating publications. Research
interests: open education system. Department of Informatics and Applied Mathematics,
Almaty, Kazakhstan. She can be contacted by e-mail at janka1930@mail.ru.
Gauhar Borankulova candidate of technical sciences, Associate professor and
Head of Information Systems Department at M.Kh. Dulaty Taraz Regional University. She
has more than 60 scientific papers, including 7 works in the rating publications Web of
Science and Scopus, h-index–2. Research interests: fiber-optic technologies, microprocessor
systems, information systems. Department of Information Systems, Faculty of Information
Technology. Taraz, Kazakhstan. She can be contacted at email: b.gau@mail.ru.