With the growth of data-oriented research in humanities, a large number of research datasets have been
created and published through web services. However, how to discover, integrate and reuse these distributed
heterogeneous research datasets is a challenging task. Ontology is the soul between series digital humanities
resources, which provides a good way for people to discover and understand these datasets. With the release
of more and more linked open data and knowledge bases, a large number of ontologies have been produced
at the same time. These ontologies have different publishing formats, consumption patterns, and interactions
ways, which are not conductive to the user’s understanding of the datasets and the reuse of the ontologies.
The Ontology Service Center platform consists of Ontology Query Center and Ontology Validation Center,
mainly using linked data and ontology-based technologies. The Ontology Query Center realizes the functions
of ontology publishing, querying, data interaction and online browsing, while the Ontology Validation
Center can verify the status of using certain ontologies in the linked datasets. The empirical part of the paper
uses the Confucius portrait as an example of how OSC can be used in the semantic annotation of images. In
a word, the purpose of this paper is to construct the applied ecology of ontology to promote the development
of knowledge graphs and the spread of ontology.
A FRAMEWORK FOR BUILDING A MULTILINGUAL INDUSTRIAL ONTOLOGY: METHODOLOGY AND ...IJwest
As Web 3.0 is blooming, ontologies augment semantic Web with semi–structured knowledge. Industrial
ontologies can help in improving online commercial communication and marketing. In addition,
conceptualizing the enterprise knowledge can improve information retrieval for industrial applications.
Having ontologies combine multiple languages can help in delivering the knowledge to a broad sector of
Internet users. In addition, multi-lingual ontologies can also help in commercial transactions. This
research paper provides a framework model for building industrial multilingual ontologies which include
Corpus Determination, Filtering, Analysis, Ontology Building, and Ontology Evaluation. It also addresses
factors to be considered when modeling multilingual ontologies. A case study for building a bilingual
English-Arabic ontology for smart phones is presented. The ontology was illustrated using an ontology
editor and visualization tool. The built ontology consists of 67 classes and 18 instances presented in both
Arabic and English. In addition, applications for using the ontology are presented. Future research
directions for the built industrial ontology are presented.
Building an Ontology in Educational Domain Case Study for the University of P...IJRES Journal
The current web is based on HTML which cannot be demoralized by information retrieval techniques and therefore processing of information on the web is generally restricted to manual keyword searches which results in unrelated information retrieval, so the semantic web was founded to resolve this problem; furthermore, ontology is used to capture knowledge about any domain of interest with the goal of integrating the machine understandable data on the current human-readable web. Web Ontology Language (OWL) is a semantic markup language for sharing ontologies on the web. In this paper, the education domain and the development of a University Ontology using Protégé 4.1 Editor is considered. The University of Palestine was chosen as an example for the Ontology Development and the diverse aspects: super class and sub class hierarchy, creating a sub class, instances for classes illustration, query retrieval process using the Unified Process for Building the Ontology (UPON) technique.
Ontology Construction from Text: Challenges and TrendsCSCJournals
Ontology is one of the most popular representation model used for knowledge representation, sharing and reusing. In light of the importance of ontology, different methodologies for building ontologies have been proposed. Ontology construction is a difficult and time-consuming process. Many efforts have been made to help ontology engineers to construct ontologies and to overcome the bottleneck of knowledge acquisition. The aim of this paper is to give a brief overview of ontology learning approaches and to review some of ontology extraction systems and tools followed by a summarizing comparison of them. Also some of the current issues and main trends of ontology construction from texts will be discussed.
Named entity recognition using web document corpusIJMIT JOURNAL
This paper introduces a named entity recognition approach in textual corpus. This Named Entity (NE)
can be a named: location, person, organization, date, time, etc., characterized by instances. A NE is
found in texts accompanied by contexts: words that are left or right of the NE. The work mainly aims at identifying contexts inducing the NE’s nature. As such, The occurrence of the word "President" in a text, means that this word or context may be followed by the name of a president as President "Obama". Likewise, a word preceded by the string "footballer" induces that this is the name of a
footballer. NE recognition may be viewed as a classification method, where every word is assigned to
a NE class, regarding the context. The aim of this study is then to identify and classify the contexts that are most relevant to recognize a NE, those which are frequently found with the NE. A learning approach using training corpus: web documents, constructed from learning examples is then suggested. Frequency representations and modified tf-idf representations are used to calculate the context weights associated to context frequency, learning example frequency, and document frequency in the corpus.
The logic-based machine-understandable framework of the Semantic Web often challenges naive users when they try to query ontology-based knowledge bases. Existing research efforts have approached this problem by introducing Natural Language (NL) interfaces to ontologies. These NL interfaces have the ability to construct SPARQL queries based on NL user queries. However, most efforts were restricted to queries expressed in English, and they often benefited from the advancement of English NLP tools. However, little research has been done to support querying the Arabic content on the Semantic Web by using NL queries. This paper presents a domain-independent approach to translate Arabic NL queries to SPARQL by leveraging linguistic analysis. Based on a special consideration on Noun Phrases (NPs), our approach uses a language parser to extract NPs and the relations from Arabic parse trees and match them to the underlying ontology. It then utilizes knowledge in the ontology to group NPs into triple-based representations. A SPARQL query is finally generated by extracting targets and modifiers, and interpreting them into SPARQL. The interpretation of advanced semantic features including negation, conjunctive and disjunctive modifiers is also supported. The approach was evaluated by using two datasets consisting of OWL test data and queries, and the obtained results have confirmed its feasibility to translate Arabic NL queries to SPARQL.
Named Entity Recognition Using Web Document CorpusIJMIT JOURNAL
This paper introduces a named entity recognition approach in textual corpus. This Named Entity (NE) can be a named: location, person, organization, date, time, etc., characterized by instances. A NE is found in texts accompanied by contexts: words that are left or right of the NE. The work mainly aims at identifying contexts inducing the NE’s nature. As such, The occurrence of the word "President" in a text, means that this word or context may be followed by the name of a president as President "Obama". Likewise, a word preceded by the string "footballer" induces that this is the name of a footballer. NE recognition may be viewed as a classification method, where every word is assigned to a NE class, regarding the context.
The aim of this study is then to identify and classify the contexts that are most relevant to recognize a NE, those which are frequently found with the NE. A learning approach using training corpus: web documents, constructed from learning examples is then suggested. Frequency representations and modified tf-idf representations are used to calculate the context weights associated to context frequency, learning example frequency, and document frequency in the corpus.
A FRAMEWORK FOR BUILDING A MULTILINGUAL INDUSTRIAL ONTOLOGY: METHODOLOGY AND ...IJwest
As Web 3.0 is blooming, ontologies augment semantic Web with semi–structured knowledge. Industrial
ontologies can help in improving online commercial communication and marketing. In addition,
conceptualizing the enterprise knowledge can improve information retrieval for industrial applications.
Having ontologies combine multiple languages can help in delivering the knowledge to a broad sector of
Internet users. In addition, multi-lingual ontologies can also help in commercial transactions. This
research paper provides a framework model for building industrial multilingual ontologies which include
Corpus Determination, Filtering, Analysis, Ontology Building, and Ontology Evaluation. It also addresses
factors to be considered when modeling multilingual ontologies. A case study for building a bilingual
English-Arabic ontology for smart phones is presented. The ontology was illustrated using an ontology
editor and visualization tool. The built ontology consists of 67 classes and 18 instances presented in both
Arabic and English. In addition, applications for using the ontology are presented. Future research
directions for the built industrial ontology are presented.
Building an Ontology in Educational Domain Case Study for the University of P...IJRES Journal
The current web is based on HTML which cannot be demoralized by information retrieval techniques and therefore processing of information on the web is generally restricted to manual keyword searches which results in unrelated information retrieval, so the semantic web was founded to resolve this problem; furthermore, ontology is used to capture knowledge about any domain of interest with the goal of integrating the machine understandable data on the current human-readable web. Web Ontology Language (OWL) is a semantic markup language for sharing ontologies on the web. In this paper, the education domain and the development of a University Ontology using Protégé 4.1 Editor is considered. The University of Palestine was chosen as an example for the Ontology Development and the diverse aspects: super class and sub class hierarchy, creating a sub class, instances for classes illustration, query retrieval process using the Unified Process for Building the Ontology (UPON) technique.
Ontology Construction from Text: Challenges and TrendsCSCJournals
Ontology is one of the most popular representation model used for knowledge representation, sharing and reusing. In light of the importance of ontology, different methodologies for building ontologies have been proposed. Ontology construction is a difficult and time-consuming process. Many efforts have been made to help ontology engineers to construct ontologies and to overcome the bottleneck of knowledge acquisition. The aim of this paper is to give a brief overview of ontology learning approaches and to review some of ontology extraction systems and tools followed by a summarizing comparison of them. Also some of the current issues and main trends of ontology construction from texts will be discussed.
Named entity recognition using web document corpusIJMIT JOURNAL
This paper introduces a named entity recognition approach in textual corpus. This Named Entity (NE)
can be a named: location, person, organization, date, time, etc., characterized by instances. A NE is
found in texts accompanied by contexts: words that are left or right of the NE. The work mainly aims at identifying contexts inducing the NE’s nature. As such, The occurrence of the word "President" in a text, means that this word or context may be followed by the name of a president as President "Obama". Likewise, a word preceded by the string "footballer" induces that this is the name of a
footballer. NE recognition may be viewed as a classification method, where every word is assigned to
a NE class, regarding the context. The aim of this study is then to identify and classify the contexts that are most relevant to recognize a NE, those which are frequently found with the NE. A learning approach using training corpus: web documents, constructed from learning examples is then suggested. Frequency representations and modified tf-idf representations are used to calculate the context weights associated to context frequency, learning example frequency, and document frequency in the corpus.
The logic-based machine-understandable framework of the Semantic Web often challenges naive users when they try to query ontology-based knowledge bases. Existing research efforts have approached this problem by introducing Natural Language (NL) interfaces to ontologies. These NL interfaces have the ability to construct SPARQL queries based on NL user queries. However, most efforts were restricted to queries expressed in English, and they often benefited from the advancement of English NLP tools. However, little research has been done to support querying the Arabic content on the Semantic Web by using NL queries. This paper presents a domain-independent approach to translate Arabic NL queries to SPARQL by leveraging linguistic analysis. Based on a special consideration on Noun Phrases (NPs), our approach uses a language parser to extract NPs and the relations from Arabic parse trees and match them to the underlying ontology. It then utilizes knowledge in the ontology to group NPs into triple-based representations. A SPARQL query is finally generated by extracting targets and modifiers, and interpreting them into SPARQL. The interpretation of advanced semantic features including negation, conjunctive and disjunctive modifiers is also supported. The approach was evaluated by using two datasets consisting of OWL test data and queries, and the obtained results have confirmed its feasibility to translate Arabic NL queries to SPARQL.
Named Entity Recognition Using Web Document CorpusIJMIT JOURNAL
This paper introduces a named entity recognition approach in textual corpus. This Named Entity (NE) can be a named: location, person, organization, date, time, etc., characterized by instances. A NE is found in texts accompanied by contexts: words that are left or right of the NE. The work mainly aims at identifying contexts inducing the NE’s nature. As such, The occurrence of the word "President" in a text, means that this word or context may be followed by the name of a president as President "Obama". Likewise, a word preceded by the string "footballer" induces that this is the name of a footballer. NE recognition may be viewed as a classification method, where every word is assigned to a NE class, regarding the context.
The aim of this study is then to identify and classify the contexts that are most relevant to recognize a NE, those which are frequently found with the NE. A learning approach using training corpus: web documents, constructed from learning examples is then suggested. Frequency representations and modified tf-idf representations are used to calculate the context weights associated to context frequency, learning example frequency, and document frequency in the corpus.
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.
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.
Swoogle: Showcasing the Significance of Semantic SearchIDES Editor
The World Wide Web hosts vast repositories of
information. The retrieval of required information from the
Internet is a great challenge since computer applications
understand only the structure and layout of web pages and
they do not have access to their intended meaning. Semantic
web is an effort to enhance the Internet, so that computers
can process the information presented on WWW, interpret
and communicate with it, to help humans find required
essential knowledge. Application of Ontology is the
predominant approach helping the evolution of the Semantic
web. The aim of our work is to illustrate how Swoogle, a
semantic search engine, helps make computer and WWW
interoperable and more intelligent. In this paper, we discuss
issues related to traditional and semantic web searching. We
outline how an understanding of the semantics of the search
terms can be used to provide better results. The experimental
results establish that semantic search provides more focused
results than the traditional search.
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.
A Comparative Study Ontology Building Tools for Semantic Web Applications IJwest
Ontologies have recently received popularity in the area of knowledge management and knowledge sharing,
especially after the evolution of the Semantic Web and its supporting technologies. An ontology defines the terms
and concepts (meaning) used to describe and represent an area of knowledge.The aim of this paper is to identify all
possible existing ontologies and ontology management tools (Protégé 3.4, Apollo, IsaViz & SWOOP) that are freely
available and review them in terms of: a) interoperability, b) openness, c) easiness to update and maintain, d)
market status and penetration. The results of the review in ontologies are analyzed for each application area, such
as transport, tourism, personal services, health and social services, natural languages and other HCI-related
domains. Ontology Building/Management Tools are used by different groups of people for performing diverse tasks.
Although each tool provides different functionalities, most of the users just use only one, because they are not able
to interchange their ontologies from one tool to another. In addition, we considered the compatibility of different
ontologies with different development and management tools. The paper is also concerns the detection of
commonalities and differences between the examined ontologies, both on the same domain (application area) and
among different domains.
LOANONT-A RULE BASED ONTOLOGY FOR PERSONAL LOAN ELIGIBILITY EVALUATIONIJwest
In recent years, significant attention has been given to understand and implement banking solutions. The
global competitive business environment and advancement in Information Technology and in particular
internet technologies has facilitated the carrying out of banking activities outside the brick and mortar
premise of the banks. Credit availing schemes are the core of the banking industry. Many agencies are
working on it so as to make this facility hassle free for the customers and also to minimize the losses
incurred by the banks in the form of bad debts. The challenge has been, and still is, to recognize,
communicate and steadily improvise the banking solutions. The internet technologies are a potential
candidates to overcome these challenges. The paper describes LoanOnt Ontology with the associated
implementation toolset for creating an interoperable and sustainable personal loan calculation solution
which would provide an intercommunication platform to facilitate integration and interoperation of
information across interacting applications in banking scenarios.
MULTILINGUAL INFORMATION RETRIEVAL BASED ON KNOWLEDGE CREATION TECHNIQUESijcseit
As the information access across languages increases, the importance of a system that supports querybased
searching with the presence of multilingual also grows. Gathering the information in different
natural language is the most difficult task, which requires huge resources like database and digital
libraries. Cross language information retrieval (CLIR) enables to search in multilingual document
collections using the native language which can be supported by the different data mining techniques. This
paper deals with various data mining techniques that can be used for solving the problems encountered in
CLIR.
The Process of Information extraction through Natural Language ProcessingWaqas Tariq
Information Retrieval (IR) is the discipline that deals with retrieval of unstructured data, especially textual documents, in response to a query or topic statement, which may itself be unstructured, e.g., a sentence or even another document, or which may be structured, e.g., a boolean expression. The need for effective methods of automated IR has grown in importance because of the tremendous explosion in the amount of unstructured data, both internal, corporate document collections, and the immense and growing number of document sources on the Internet.. The topics covered include: formulation of structured and unstructured queries and topic statements, indexing (including term weighting) of document collections, methods for computing the similarity of queries and documents, classification and routing of documents in an incoming stream to users on the basis of topic or need statements, clustering of document collections on the basis of language or topic, and statistical, probabilistic, and semantic methods of analyzing and retrieving documents. Information extraction from text has therefore been pursued actively as an attempt to present knowledge from published material in a computer readable format. An automated extraction tool would not only save time and efforts, but also pave way to discover hitherto unknown information implicitly conveyed in this paper. Work in this area has focused on extracting a wide range of information such as chromosomal location of genes, protein functional information, associating genes by functional relevance and relationships between entities of interest. While clinical records provide a semi-structured, technically rich data source for mining information, the publications, in their unstructured format pose a greater challenge, addressed by many approaches.
This is our presentation at International Conference of Data Mining and Knowledge Engineering "ICDMKE 13'" held at London, UK, 03-05 July 2013. the paper is available at: http://www.iaeng.org/publication/WCE2013/WCE2013_pp1595-1600.pdf
A Semi-Automatic Ontology Extension Method for Semantic Web ServicesIDES Editor
this paper provides a novel semi-automatic ontology
extension method for Semantic Web Services (SWS). This is
significant since ontology extension methods those existing
in literature mostly deal with semantic description of static
Web resources such as text documents. Hence, there is a need
for methods that can serve dynamic Web resources such as
SWS. The developed method in this paper avoids redundancy
and respects consistency so as to assure high quality of the
resulting shared ontologies.
Concept hierarchy is the backbone of ontology, and the concept hierarchy acquisition has been a hot topic in the field of ontology learning. this paper proposes a hyponymy extraction method of domain ontology concept based on cascaded conditional random field(CCRFs) and hierarchy clustering. It takes free text as extracting object, adopts CCRFs identifying the domain concepts. First the low layer of CCRFs is used to identify simple domain concept, then the results are sent to the high layer, in which the nesting concepts are recognized. Next we adopt hierarchy clustering to identify the hyponymy relation between domain ontology concepts. The experimental results demonstrate the proposed method is efficient.
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.
A Review on Evolution and Versioning of Ontology Based Information Systemsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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.
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.
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.
Swoogle: Showcasing the Significance of Semantic SearchIDES Editor
The World Wide Web hosts vast repositories of
information. The retrieval of required information from the
Internet is a great challenge since computer applications
understand only the structure and layout of web pages and
they do not have access to their intended meaning. Semantic
web is an effort to enhance the Internet, so that computers
can process the information presented on WWW, interpret
and communicate with it, to help humans find required
essential knowledge. Application of Ontology is the
predominant approach helping the evolution of the Semantic
web. The aim of our work is to illustrate how Swoogle, a
semantic search engine, helps make computer and WWW
interoperable and more intelligent. In this paper, we discuss
issues related to traditional and semantic web searching. We
outline how an understanding of the semantics of the search
terms can be used to provide better results. The experimental
results establish that semantic search provides more focused
results than the traditional search.
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.
A Comparative Study Ontology Building Tools for Semantic Web Applications IJwest
Ontologies have recently received popularity in the area of knowledge management and knowledge sharing,
especially after the evolution of the Semantic Web and its supporting technologies. An ontology defines the terms
and concepts (meaning) used to describe and represent an area of knowledge.The aim of this paper is to identify all
possible existing ontologies and ontology management tools (Protégé 3.4, Apollo, IsaViz & SWOOP) that are freely
available and review them in terms of: a) interoperability, b) openness, c) easiness to update and maintain, d)
market status and penetration. The results of the review in ontologies are analyzed for each application area, such
as transport, tourism, personal services, health and social services, natural languages and other HCI-related
domains. Ontology Building/Management Tools are used by different groups of people for performing diverse tasks.
Although each tool provides different functionalities, most of the users just use only one, because they are not able
to interchange their ontologies from one tool to another. In addition, we considered the compatibility of different
ontologies with different development and management tools. The paper is also concerns the detection of
commonalities and differences between the examined ontologies, both on the same domain (application area) and
among different domains.
LOANONT-A RULE BASED ONTOLOGY FOR PERSONAL LOAN ELIGIBILITY EVALUATIONIJwest
In recent years, significant attention has been given to understand and implement banking solutions. The
global competitive business environment and advancement in Information Technology and in particular
internet technologies has facilitated the carrying out of banking activities outside the brick and mortar
premise of the banks. Credit availing schemes are the core of the banking industry. Many agencies are
working on it so as to make this facility hassle free for the customers and also to minimize the losses
incurred by the banks in the form of bad debts. The challenge has been, and still is, to recognize,
communicate and steadily improvise the banking solutions. The internet technologies are a potential
candidates to overcome these challenges. The paper describes LoanOnt Ontology with the associated
implementation toolset for creating an interoperable and sustainable personal loan calculation solution
which would provide an intercommunication platform to facilitate integration and interoperation of
information across interacting applications in banking scenarios.
MULTILINGUAL INFORMATION RETRIEVAL BASED ON KNOWLEDGE CREATION TECHNIQUESijcseit
As the information access across languages increases, the importance of a system that supports querybased
searching with the presence of multilingual also grows. Gathering the information in different
natural language is the most difficult task, which requires huge resources like database and digital
libraries. Cross language information retrieval (CLIR) enables to search in multilingual document
collections using the native language which can be supported by the different data mining techniques. This
paper deals with various data mining techniques that can be used for solving the problems encountered in
CLIR.
The Process of Information extraction through Natural Language ProcessingWaqas Tariq
Information Retrieval (IR) is the discipline that deals with retrieval of unstructured data, especially textual documents, in response to a query or topic statement, which may itself be unstructured, e.g., a sentence or even another document, or which may be structured, e.g., a boolean expression. The need for effective methods of automated IR has grown in importance because of the tremendous explosion in the amount of unstructured data, both internal, corporate document collections, and the immense and growing number of document sources on the Internet.. The topics covered include: formulation of structured and unstructured queries and topic statements, indexing (including term weighting) of document collections, methods for computing the similarity of queries and documents, classification and routing of documents in an incoming stream to users on the basis of topic or need statements, clustering of document collections on the basis of language or topic, and statistical, probabilistic, and semantic methods of analyzing and retrieving documents. Information extraction from text has therefore been pursued actively as an attempt to present knowledge from published material in a computer readable format. An automated extraction tool would not only save time and efforts, but also pave way to discover hitherto unknown information implicitly conveyed in this paper. Work in this area has focused on extracting a wide range of information such as chromosomal location of genes, protein functional information, associating genes by functional relevance and relationships between entities of interest. While clinical records provide a semi-structured, technically rich data source for mining information, the publications, in their unstructured format pose a greater challenge, addressed by many approaches.
This is our presentation at International Conference of Data Mining and Knowledge Engineering "ICDMKE 13'" held at London, UK, 03-05 July 2013. the paper is available at: http://www.iaeng.org/publication/WCE2013/WCE2013_pp1595-1600.pdf
A Semi-Automatic Ontology Extension Method for Semantic Web ServicesIDES Editor
this paper provides a novel semi-automatic ontology
extension method for Semantic Web Services (SWS). This is
significant since ontology extension methods those existing
in literature mostly deal with semantic description of static
Web resources such as text documents. Hence, there is a need
for methods that can serve dynamic Web resources such as
SWS. The developed method in this paper avoids redundancy
and respects consistency so as to assure high quality of the
resulting shared ontologies.
Concept hierarchy is the backbone of ontology, and the concept hierarchy acquisition has been a hot topic in the field of ontology learning. this paper proposes a hyponymy extraction method of domain ontology concept based on cascaded conditional random field(CCRFs) and hierarchy clustering. It takes free text as extracting object, adopts CCRFs identifying the domain concepts. First the low layer of CCRFs is used to identify simple domain concept, then the results are sent to the high layer, in which the nesting concepts are recognized. Next we adopt hierarchy clustering to identify the hyponymy relation between domain ontology concepts. The experimental results demonstrate the proposed method is efficient.
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.
A Review on Evolution and Versioning of Ontology Based Information Systemsiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
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.
A Comparative Study Ontology Building Tools for Semantic Web Applications dannyijwest
Ontologies have recently received popularity in the area of knowledge management and knowledge sharing, especially after the evolution of the Semantic Web and its supporting technologies. An ontology defines the terms and concepts (meaning) used to describe and represent an area of knowledge.The aim of this paper is to identify all possible existing ontologies and ontology management tools (Protégé 3.4, Apollo, IsaViz & SWOOP) that are freely available and review them in terms of: a) interoperability, b) openness, c) easiness to update and maintain, d) market status and penetration. The results of the review in ontologies are analyzed for each application area, such as transport, tourism, personal services, health and social services, natural languages and other HCI-related domains. Ontology Building/Management Tools are used by different groups of people for performing diverse tasks. Although each tool provides different functionalities, most of the users just use only one, because they are not able to interchange their ontologies from one tool to another. In addition, we considered the compatibility of different ontologies with different development and management tools. The paper is also concerns the detection of commonalities and differences between the examined ontologies, both on the same domain (application area) and among different domains.
A Comparative Study of Ontology building Tools in Semantic Web Applications dannyijwest
Ontologies have recently received popularity in the area of knowledge management and knowledge sharing,
especially after the evolution of the Semantic Web and its supporting technologies. An ontology defines the terms
and concepts (meaning) used to describe and represent an area of knowledge.The aim of this paper is to identify all
possible existing ontologies and ontology management tools (Protégé 3.4, Apollo, IsaViz & SWOOP) that are freely
available and review them in terms of: a) interoperability, b) openness, c) easiness to update and maintain, d)
market status and penetration. The results of the review in ontologies are analyzed for each application area, such
as transport, tourism, personal services, health and social services, natural languages and other HCI-related
domains. Ontology Building/Management Tools are used by different groups of people for performing diverse tasks.
Although each tool provides different functionalities, most of the users just use only one, because they are not able
to interchange their ontologies from one tool to another. In addition, we considered the compatibility of different
ontologies with different development and management tools. The paper is also concerns the detection of
commonalities and differences between the examined ontologies, both on the same domain (application area) and
among different domains.
Research Inventy : International Journal of Engineering and Scienceresearchinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
Recruitment Based On Ontology with Enhanced Security Featurestheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
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
ICBO 2018 Poster - Current Development in the Evidence and Conclusion Ontolog...dolleyj
The Evidence & Conclusion Ontology (ECO) has been developed to provide standardized descriptions for types of evidence within the biological domain. Best
practices in biocuration require that when a biological assertion is made (e.g. linking a Gene Ontology (GO) term for a molecular function to a protein), the type of evidence
supporting it is captured. In recent development efforts, we have been working with other ontology groups to ensure that ECO classes exist for the types of curation they
support. These include the Ontology for Microbial Phenotypes and GO. In addition, we continue to support user-level class requests through our GitHub issue tracker. To
facilitate the addition and maintenance of new classes, we utilize ROBOT (a command line tool for working with Open Biomedical Ontologies) as part of our standard workflow.
ROBOT templates allow us to define classes in a spreadsheet and convert them to Web Ontology Language (OWL) axioms, which can then be merged into ECO. ROBOT is
also part of our automated release process. Additionally, we are engaged in ongoing work to map ECO classes to Ontology for Biomedical Investigation classes using logical
definitions. ECO is currently in use by dozens of groups engaged in biological curation and the number of ECO users continues to grow. The ontology, in OWL and Open
Biomedical Ontology (OBO) formats, and associated resources can be accessed through our GitHub site (https://github.com/evidenceontology/evidenceontology) as well as
the ECO web page (http://evidenceontology.org/).
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
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.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
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.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
ONTOLOGY SERVICE CENTER: A DATAHUB FOR ONTOLOGY APPLICATION
1. International Journal of Web & Semantic Technology (IJWesT) Vol.12, No.2/3, July 2021
DOI : 10.5121/ijwest.2021.12301 1
ONTOLOGY SERVICE CENTER: A DATAHUB FOR
ONTOLOGY APPLICATION
CHEN Tao1
, SU Rina2
, ZHANG Yongjuan3
, YIN Xin4
and ZHU Rui5
1
School of Information Management, Sun Yat-sen University, Guangzhou,
Guangdong, China.
2
Library, Sun Yat-sen University, Guangzhou, Guangdong, China.
3
Shanghai Information Center for Life Sciences, Chinese Academy of Sciences,
Shanghai, China.
4
School of Information Resource Management, Remin University of China,
Beijing, China.
5
Department of Library, Information and Archives, Shanghai University,
Shanghai, China.
ABSTRACT
With the growth of data-oriented research in humanities, a large number of research datasets have been
created and published through web services. However, how to discover, integrate and reuse these distributed
heterogeneous research datasets is a challenging task. Ontology is the soul between series digital humanities
resources, which provides a good way for people to discover and understand these datasets. With the release
of more and more linked open data and knowledge bases, a large number of ontologies have been produced
at the same time. These ontologies have different publishing formats, consumption patterns, and interactions
ways, which are not conductive to the user’s understanding of the datasets and the reuse of the ontologies.
The Ontology Service Center platform consists of Ontology Query Center and Ontology Validation Center,
mainly using linked data and ontology-based technologies. The Ontology Query Center realizes the functions
of ontology publishing, querying, data interaction and online browsing, while the Ontology Validation
Center can verify the status of using certain ontologies in the linked datasets. The empirical part of the paper
uses the Confucius portrait as an example of how OSC can be used in the semantic annotation of images. In
a word, the purpose of this paper is to construct the applied ecology of ontology to promote the development
of knowledge graphs and the spread of ontology.
KEYWORDS
Ontology Service Center, Knowledge Graph, Linked Data, International Image Interoperability Framework
(IIIF), Image Semantic Annotation
1. INTRODUCTION
With the growth of data-oriented research in humanities, a large number of research datasets have
been created and published through web services. A large part of these datasets is published on
web as linked open data. As we all know, the purpose of Linked Data is to aggregate, harmonize,
integrate, enrich and publish data for reuse on the Web in a cost-efficient way when using Semantic
Web technologies (Bizer et al., 2009; Tom et al., 2011; Gandon, 2018). However, how to discover,
integrate and reuse these distributed heterogeneous research datasets is a great challenge. As
ontology becomes the logical backbone for managing research datasets in the humanities,
developing ontology repositories to archive semantic interoperability among research datasets
becomes a viable solution to address this issue. Although many datasets publish their corresponding
2. International Journal of Web & Semantic Technology (IJWesT) Vol.12, No.2/3, July 2021
2
ontologies, the others don’t publish their ontologies simultaneously. The lack of ontology
description creates a comprehension barrier of the datasets use, also consumers cannot clearly
understand the data structure of the datasets. Finland scholars have proposed the extending of the
5-Star Model proposed by Tim Berners-Lee in 2010 into a 7-Star Model, intending to encourage
data publishers to provide their data with explicit metadata schemas (Hyvönen et al., 2014).
There are many studies on ontology, which can be divided into the following research spots. First,
there are a large number of articles on how to design and construct ontologies and knowledge
graphs. Wang Xiaoguang et al. (2020) construct an ontology model to regulate the entities,
attributes and relationships of Dunhuang cultural heritage knowledge. In recent years, Shanghai
Library has published several digital humanities-related ontologies (Xia C.J. et al., 2014; 2021).
Muir K. et al. (2018) discuss ontology-based information retrieval approaches and techniques by
taking into consideration the aspects of ontology modeling, processing and the translation of
ontological knowledge into database search requests.
Second, there are also many scholars studying ontological specifications and recommendations to
ensure the efficient publishing of ontology, such as the OBO Foundry (Smith B. et al., 2007)
establishes a set of principles for ontology development for creating a suite of
interoperable reference ontologies in the biomedical domain. Chen T. et al. (2019) and Feitosa, D.
et al. (2018) give several specifications and recommendations on Linked Data publishing. Garijo
D. et al. (2020) describes guidelines and best practices for creating accessible, understandable, and
reusable ontologies on the Web.
Besides, there’re studies are focusing on how to build a unified platform to manage and maintain
multiple ontologies in the bio-medical field. BioPortal (Whetzel, P.L. et al., 2009; Noy, N.-F. et
al., 2011; Vescovo, C.D. et al., 2011) is an open-source online community-based ontology
repository that has been used as a critical component of semantic infrastructure in several domains,
including biomedicine and bio-geochemical. By reusing the National Center for Biomedical
Ontologies (NCBO) BioPortal technology, Jonquet C. et al. (2018) have designed AgroPortal, a
vocabulary and ontology repository for agronomy, food, plant sciences, and biodiversity. Codescu
M. et al. (2017) proposed Ontohub, a semantic repository for heterogeneous ontologies. Linked
Open Vocabularies (LOV) (Vandenbussche, P.-Y. et al., 2017) is a high-quality catalogue of
reusable vocabularies for the description of data on the Web that aims to promote and facilitate the
reuse of well-documented vocabularies in the Linked Data Ecosystem.
The above analysis mainly revolves around the ontology design principles, design methods and
maintenance management, and in this paper, we will focus on the application of ontologies. As
everyone knows, a larger number ontologies are used for digital humanities projects in the way of
knowledge organization, some of which are based on common ontologies, many of which are
designed according to project requirements. After design ontologies, most of these ontologies are
still stored separately in different structures and datasets published by dataset providers, which will
lead to a variety of data formats and consumption methods for the published ontologies. In terms
of data formats, some are in RDF/XML formats while others are in TTL, N3, or JSON-LD in terms
of acquisition methods, some provides ontologies online browse, and can only be downloaded via
the static ontology files. Therefore, we design and implement the Ontology Service Center (OSC)
platform, which belongs to the third category of ontology research discussed above.
In general, the OSC platform has many same functions with LOV platform, such as ontology or
vocabulary registry, publishing, querying and download. However, OSC platform provide some
advanced and more convenient functions and services:
3. International Journal of Web & Semantic Technology (IJWesT) Vol.12, No.2/3, July 2021
3
1) In addition to the retrieval of ontology and properties, the OSC platform also provides a
variety of online browsing methods for ontology, while the ontology file needs to be
downloaded in LOV.
2) OSC has more diverse and richer ontology interaction formats and these different
serialization formats will be converted automatically as requested and the ontology format
in LOV is mainly N3.
3) LOV is mainly used for ontology searching, OSC can also be used for dataset verification,
which is mainly used to verify the usage status of ontologies in the dataset.
The rest of this article is organized as follows. In the next section, we describe the framework of
the OSC platform and the storage mechanism of ontology. In Section 3, we introduce the ontology
query service module and explain the principle and algorithm of property inheritance. Moreover,
we describe how to validate instance data with ontology in Section 4. In Section 5, we illustrate
how to use OSC for image semantic annotation in conjunction with the IIIF-IIP platform.
2. SYSTEM COMPONENTS AND FEATURES
2.1. Ontology Service Center (OSC) Framework
Figure 1. Ontology Service Center Framework
The Ontology Service Center (OSC) Framework, shown in figure 1, is composed of four main
components: data dump, data publishing/view, data archive and data search. Based on these
features, the system provides two major ontology services, Ontology Query Center (OQC) and
Ontology Validation Center (OVC). OQC focuses on the ontology management process, which is
used to store and publish ontologies, while OVC validate ontology, which is used to verify the use
of ontologies in linked open data. It is important to note that OVC does not perform syntax checks
about ontology. When ontology has a syntax error, it cannot be registered in OQC.
Data Dump. OSC provides four data dumps, RDF/XML, TTL, RDF/JSON and NT, which will be
generated automatically in real-time of ontology (RDF) model which is a set of Statements of this
ontology.
4. International Journal of Web & Semantic Technology (IJWesT) Vol.12, No.2/3, July 2021
4
Data Publishing/View. This system supports four kinds of ontology view methods, Class View
(C.V.), List View (L.V.), Graph View (G.V.) and Reuse View (R.V.).
Class View. This view uses a tree structure to display the ontology, which makes it easy to
understand the hierarchical relationship between classes in ontology.
List View. This view presents the entire vocabulary on one page with an ordered list of
classes and properties, followed by more detailed information panels further down the
document.
Graph View. This view visually displays the ontology with WebVOWL (Lohmann, S. et al.,
2015; Wiens, V. et al., 2018), which is a web application for the interactive visualization of
ontologies. The visualization is automatically generated from the ontology graph.
Reuse View. This view is achieved through the WebVOWL Editor application, which is
designed to serve the skills and needs of domain experts with limited knowledge of ontology
modeling.
Data Archive. The archived ontology is served as a file on disk, and the file name will contain the
version number of the archive. In other words, only the latest ontology will be stored in RDF
format. The system will provide the results of the comparison between the archived version and
the latest one. If necessary, the archived ontology can be rolled back and restored to the latest
version in the ontology graph.
Data Search. OSC enables searching for vocabulary terms (class, object property, data property),
term comments, catalogues, namespace prefix and contributor.
2.2. Ontology Storage Mechanism Design
In the OSC system, we use OpenLink Virtuoso (Triple Store) to store the latest ontologies. Figure
2 shows the storage mechanism of this system. Every ontology with the latest version is stored
independently in a named graph. That is, as many ontologies, there are so many named graphs in
the triple store. At the same time, we need to establish a management graph of datasets called
datasets graph here, to manage the basic information of ontologies, such as ontology publisher,
publishing time, version number, rights and license. This dataset graph is the core hub of the entire
ontology management mechanism which is used to manage all ontologies and versions.
Figure 2. Ontology Storage Mechanism
6. International Journal of Web & Semantic Technology (IJWesT) Vol.12, No.2/3, July 2021
6
3. ONTOLOGY QUERY CENTER/SERVICE (OQC)
Ontology Query Center provides the function of acquiring and viewing ontology. Figure 3 shows
the function snapshots of Schema.org ontology in the OSC platform. Figure 3-A to 3-D are the
different ways to browse this ontology. 3-A is the list view (L.V) mode, and 3-B is the class view
(C.V) mode. 3-C and 3-D modes are performed with the WEBVOWL plug-in. Class view mode
not only shows the hierarchical relationship between different classes of ontology, but also shows
the inheritance relationships between properties. Next, we will introduce the property path
calculation algorithm. Figure 4 is the diagram of property inheritance and assuming that each layer
class contains its private properties in addition to the properties of the previous classes. At the same
time, the class can also inherit from multiple parent classes.
(a) List View (b) Class View
(c) Graph View (d) Reuse View
Figure 3. Schema.org Ontology in Onthub Center
7. International Journal of Web & Semantic Technology (IJWesT) Vol.12, No.2/3, July 2021
7
Figure 4. Diagram of Property Inheritance
During implementation, we need to find the top-level classes of the ontology. The top-level class
means that the class is only inherited by other subclasses, and doesn’t inherit other classes. With
these top-level classes, the inherited classes will be found in turn. And the SPARQL of the core
part is as follows:
//Get top-level classes
SELECT DISTINCT ?class ?label
FROM <graph_uri>
WHERE {
VALUES ?type {owl:Class rdfs:Class}
{
?class a ?type .
FILTER (isIRI(?class))
} UNION {
?a a ?type ; rdfs:subClassOf* ?class .
FILTER (isIRI(?a)) FILTER (isIRI(?class))
}
OPTIONAL {?class rdfs:label ?label .}
FILTER NOT EXISTS{?class rdfs:subClassOf ?sub}
} ORDER BY ASC(STR(?class))
//Get subclasses, which need to execute recursively in programming
SELECT DISTINCT ?class ?label
FROM <graph_uri>
WHERE {
?class rdfs:subClassOf <class_uri> .
OPTIONAL {?class rdfs:label ?label . }
}
The SPARQL in the first part explains how to get the top-level classes of all non-blank nodes in
the ontology graph. The types of top-level classes here are mainly owl:Class and rdfs:Class. The
second SPARQL is used to obtain the inherited classes of a certain class (<class_uri> should be
replaced by the special class URI). It should be noted that when the algorithm is implemented, the
query statement needs to be executed recursively until the end. After obtaining all the classes and
structure levels, you can use some javascript libraries to display the ontology tree in the site. The
following example of “shlnames: Shanghai Library Names Authority Ontology” illustrates the
practical application of property inheritance shown in figure 5. The current class is shl:Person
which is the subclass of shl:Agent and foaf:Person and the foaf:Person is also the subclass of
rdfs:Resource. As can be seen in this figure, we have defined many properties and only rel:childOf,
rel:friendOf and rel:influenceBy are listed here. In the property inheritance section, we see that
8. International Journal of Web & Semantic Technology (IJWesT) Vol.12, No.2/3, July 2021
8
there are two levels here, shl:Agent (up one level) and rdfs:Resource (upper two levels). The
foaf:Person class has no relevant properties, therefore, it is not rendered in this section. As
accessing the related inherited parent class, you can see the related properties.
Figure 5. shlnames Ontology in Tree View
4. ONTOLOGY VALIDATION CENTER/SERVICE (OVC)
Using ontology validation centers can solve two common types of scenarios. One is ontology
validation, aiming to validate the classes and properties in RDF data according to the published
ontology file. Data structures in the RDF data published by the dataset often change as data
increases. However, the published ontology is not synchronized due to a failure to update it
promptly. Data consumers are confused when they access these RDF data because there is no
description of these newly added or discarded properties in the published ontology. The other
scenario is ontology statistics, using a validation service to count the number of instances
corresponding to the classes and properties in the ontology. With statistical information, it is
convenient for consumers to have a holistic understanding of the dataset and to make a correct
assessment of it.
The ontology validation center contains two parameters, "SPARQL endpoint" and "ontology". The
ontology URI should be selected from the ontology query center. When both parameters have input
values, the ontology validation process is started. If only enter the SPARQL endpoint and don't
enter the ontology URI, ontology statistics are performed. Now, we will use an example to illustrate
this transaction logic. The China Biographical Database (CBDB) is a free accessible relational
database with biographical information about approximately 422,600 individuals, primarily from
the 7th through 19th centuries. In the ontology query center, we have created CBDB ontology
which is used in CBDB Linked Data (CBDB_LD) dataset, and the ontology URI is
"http://ont.library.sh.cn/graph/cbdb". CBDB_LD provides a SPARQL endpoint with the address,
"http://cbdb.library.sh.cn/sparql". It is also important to note that all graphs for multiple projects
are often stored in the same RDF Store. Therefore, we need to set the target graphs before executing
validations or statistics.
9. International Journal of Web & Semantic Technology (IJWesT) Vol.12, No.2/3, July 2021
9
Figure 6. Ontology Statistics for CBDB_LD Dataset in OVC
Figure 6 shows the statistical results of CBDB_LD, where we only entered the endpoint value.
When the "Query Graph" button is executed, all graphs on the left side of Figure 4 are shown, and
there are 17 graphs in this dataset. We choose the target graph "http://lod.library.sh.cn/graph/cbdb"
and execute "Start". All the classes and properties used in this dataset are listed in tables. It is clear
to see that this dataset involves nine classes, just as: shl:Temporal, shl:Relationship, shl:Name,
shl:Place, et al. Due to the excessive number of the properties, we have intercepted the nine most
used properties in the property statistics table. From this table, the property which use the most is
rdf:type, followed by rd fs:label and shl:temporal.
As we enter the ontology URI and perform the same steps, a different result will come out shown
in figure 7. There is an icon about the variable result after each class and property record in tables.
In the table of the class validation, we can see that all classes are already defined in the ontology
which we want to validate. While in the property checking table, there are two properties,
shl:relationObject and shl:nameType, marked with failure icon which means that these are not
defined in the ontology file. With these validation tables, it is easy to find out whether the published
ontology is synchronized with the RDF dataset.
Figure 7. Ontology Validation for CBDB_LD Dataset in OVC
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The following describes the core algorithm process of the validation module, which mainly relates
to how to manipulate triple data in the ontology graph and the target dataset graph shown in figure
8. These steps are not difficult to implement, therefore, we don’t go into too much detail here.
Figure 8. Validation algorithm process
(1) All classes and properties are extracted from the ontology graph, mainly in step 1 and 2.
(2) Steps 3 and 4 are used to extract the classes and properties used in the instance data in the
target dataset graph.
(3) In steps 5 and 6, the results compare the previously extracted classes and properties to form
comparison results.
5. OSC IN IMAGE SEMANTIC ANNOTATION
The OSC platform currently contains hundreds of ontologies from more than 50 domains and has
been used by many scholars. Most of these ontologies come from LOV, others from our actual
digital humanities programs and the results of other Chinese researchers. In this section, we will
illustrate how to use OSC for semantic annotation of images in conjunction with the IIIF-IIP
platform . IIIF-IIP is a multi-dimensional image smart system based on IIIF (International Image
Interoperability Framework) and this platform mainly uses Image API 2.1, Presentation API 2.1,
Search API 1.0, and Web Annotation Data Model, as well as some linked data technologies. It
provides a convenient IIIF resources publishing process and lowers the technical threshold for the
release and reuse of IIIF resources. IIIF-IIP supports the online publishing of image resources in
multiple formats such as JPG, PNG, TIF, GIF, JPEG, etc. It also supports text recognition, image
re-aggregate, and semantic annotation. IIIF-IIP can carry out online interaction of images on a large
scale to provide a solid technological foundation for the reuse of cultural heritage. Using the
“Confucius Disciples Image” as an example, we first need to mark the edges of the image where
Confucius is, which is shown in figure 9.
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Figure 9. Mark the Edges of Confucius
After tagging the target object in the image, we can connect it to linked open data. When correlated,
the OSC platform can be used to query ontologies and properties. owl:sameAs are often used to
associate the same resources in different datasets. Other properties can also be used to connect the
same resources, such as “related”, so we need to find out which properties contain “related” words.
After the search, there are more than 80 ontologies in OSC that have the “related” in their
properties. Further, we choose Schema.org and skos ontology with properties schema:relatedTo
(DatatypeProperty) and skos:related (ObjectProperty). Table 1 shows the data structure of semantic
annotation for Confucius with DBpedia knowledge base.
Table. 1. Semantic Annotation in IIIF-IIP
Key Value
Property http://www.w3.org/2004/02/skos/core#related (skos:related)
Object http://dbpedia.org/resource/Confucius
Label 孔子(Confucius)
Source DBpedia
Figure 10. Connect Confucius to DBpedia and SinoPedia knowledge bases
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In figure 10, we see that Confucius is precisely marked and when viewing the image, the associated
information to linked open data was displayed in left detail panel. You can see more information
about Confucius when viewing the connected resources. The knowledge graph of Confucius
associated with SinoPedia and DBpedia is shown in figure 11, where the main interests and subjects
of Confucius can be easily seen in the graph. The top left node in the figure is the resource from
SinoPedia (Chen T. et al., 2019) and the middle node is DBpedia’s resource.
Figure 11. Confucius’s Knowledge Graph
6. CONCLUSIONS
In this paper, we describe Ontology Service Center (OSC) platform which mainly includes
Ontology Query Center (OQC) and Ontology Validation Center (OVC) modules. OQC is mainly
used for ontology registry, querying, exporting, editing, and version control, while OVC can be
used for ontology state verification in RDF datasets. Currently, OSC has been successfully applied
in many digital humanities projects in Shanghai Library, which has also been used by other scholars
for ontology publishing and reusing. Similarly, OSC has been used in image semantic annotation
in IIIF-IIP platform to establish a semantic connection between image contents and the external
open datasets.
In addition, some optimization measures in the OSC platform will be considered. First, we will
continue to track ontology resources published on the web, and regularly update ontologies on
OSC. Secondly, the platform function continues to improve, the automatic comparison and rollback
function between different versions of the ontology will be realized. Finally, the user experience
will be enhanced, and the platform will provide more convenient interfaces to query and use
ontology resources.
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ACKNOWLEDGMENTS
This research is granted financial support from National Social Science Fund of China
(19BTQ024).
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