A State-of-the-Art Survey on Semantic Web Mining
---
paper_title: Finding association rules in semantic web data

paper_content:

The amount of ontologies and semantic annotations available on the Web is constantly growing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/(S) and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of query patterns. Initial experiments performed on semantic data of a biomedical application show the usefulness and efficiency of the approach.
---
paper_title: “Hello world”, web mining for e-learning

paper_content:

Abstract As the internet and mobile applications are getting an important role in our lives, usage of mobile services also took place in educational field since the internet is widespread, which is usually called by the terms “e-learning” or “distance learning”. A known issue on e-learning is all the content’s being online and less face-to-face communication than traditional learning; this brings the problem of chasing student’s success, and advising and managing student’s way of studying. Hence, a recent hot topic, data mining, can be applied on student’s data left on e-learning portals to guide the instructor and advisors to help students’ being more successful. Recent researches done on this topic showed that e-learning combined with data mining can decrease the gap between itself and traditional learning — referred as semantic web mining in general.
---
paper_title: Using Ontologies in Semantic Data Mining with SEGS and g-SEGS

paper_content:

With the expanding of the SemanticWeb and the availability of numerous ontologies which provide domain background knowledge and semantic descriptors to the data, the amount of semantic data is rapidly growing. The data mining community is faced with a paradigm shift: instead of mining the abundance of empirical data supported by the background knowledge, the new challenge is to mine the abundance of knowledge encoded in domain ontologies, constrained by the heuristics computed from the empirical data collection. We address this challenge by an approach, named semantic data mining, where domain ontologies define the hypothesis search space, and the data is used as means of constraining and guiding the process of hypothesis search and evaluation. The use of prototype semantic data mining systems SEGS and g-SEGS is demonstrated in a simple semantic data mining scenario and in two reallife functional genomics scenarios of mining biological ontologies with the support of experimental microarray data.
---
paper_title: Development of semantic decision tree

paper_content:

The Semantic Web is an evolving development of the World Wide Web in which the meaning of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content. RDF and OWL is published by W3C for the standard of Semantic Web languages which can define relationships and structures between Web resources. Since the start of Semantic Web, the semantic web data is incrementally increased by Portal, Search Engines and Linking Open Data project. Therefore, the necessity of mining useful knowledge from huge size ontology is highly expected. However, the semantic web ontology has special characteristics to apply traditional decision tree algorithm which is the most popular in classification data mining. To overcome these problems, we proposed Semantic Decision Tree Algorithm which can perform on the semantic web ontology. This algorithm will help to mine the covered knowledge in the semantic web data.
---
paper_title: “Hello world”, web mining for e-learning

paper_content:

Abstract As the internet and mobile applications are getting an important role in our lives, usage of mobile services also took place in educational field since the internet is widespread, which is usually called by the terms “e-learning” or “distance learning”. A known issue on e-learning is all the content’s being online and less face-to-face communication than traditional learning; this brings the problem of chasing student’s success, and advising and managing student’s way of studying. Hence, a recent hot topic, data mining, can be applied on student’s data left on e-learning portals to guide the instructor and advisors to help students’ being more successful. Recent researches done on this topic showed that e-learning combined with data mining can decrease the gap between itself and traditional learning — referred as semantic web mining in general.
---
paper_title: Handbook of Semantic Web Technologies

paper_content:

After years of mostly theoretical research, Semantic Web Technologies are now reaching out into application areas like bioinformatics, eCommerce, eGovernment, or Social Webs. Applications like genomic ontologies, semantic web services, automated catalogue alignment, ontology matching, or blogs and social networks are constantly increasing, often driven or at least backed up by companies like Google, Amazon, YouTube, Facebook, LinkedIn and others. The need to leverage the potential of combining information in a meaningful way in order to be able to benefit from the Web will create further demand for and interest in Semantic Web research. This movement, based on the growing maturity of related research results, necessitates a reliable reference source from which beginners to the field can draw a first basic knowledge of the main underlying technologies as well as state-of-the-art application areas. This handbook, put together by three leading authorities in the field, and supported by an advisory board of highly reputed researchers, fulfils exactly this need. It is the first dedicated reference work in this field, collecting contributions about both the technical foundations of the Semantic Web as well as their main usage in other scientific fields like life sciences, engineering, business, or education.
---
paper_title: Secure and Intelligent Decision Making in Semantic Web Mining

paper_content:

With the huge amount of information available online the World Wide Web is a fertile area for data mining research. The Web has become a major vehicle in performing research and education related activities for researches and students. Web mining is the use of data mining technologies to automatically interact and discover information from web documents, which can be in structured, unstructured or semistructured form. We present an enterprise framework regarding semantic web mining in distance learning, which can be used to not only improve the quality of web mining results but also enhances the functions and services and the interoperability of long distance educational information systems and standards in the educational field. For on line distance education system we propose an Ontology-based approach to share online data and retrieve all relevant data about students and their courses. Thus semantic web ontology help build better web mining analysis in educational institute and web mining in-turns helps contract basis more powerful ontology in distance learning. Since the majority of the online data considered as private data we need various mechanism for privacy preservation and control over the online presence data. We propose privacy protection in semantic web mining using role back access control.
---
paper_title: Development of semantic decision tree

paper_content:

The Semantic Web is an evolving development of the World Wide Web in which the meaning of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content. RDF and OWL is published by W3C for the standard of Semantic Web languages which can define relationships and structures between Web resources. Since the start of Semantic Web, the semantic web data is incrementally increased by Portal, Search Engines and Linking Open Data project. Therefore, the necessity of mining useful knowledge from huge size ontology is highly expected. However, the semantic web ontology has special characteristics to apply traditional decision tree algorithm which is the most popular in classification data mining. To overcome these problems, we proposed Semantic Decision Tree Algorithm which can perform on the semantic web ontology. This algorithm will help to mine the covered knowledge in the semantic web data.
---
paper_title: Handbook of Semantic Web Technologies

paper_content:

After years of mostly theoretical research, Semantic Web Technologies are now reaching out into application areas like bioinformatics, eCommerce, eGovernment, or Social Webs. Applications like genomic ontologies, semantic web services, automated catalogue alignment, ontology matching, or blogs and social networks are constantly increasing, often driven or at least backed up by companies like Google, Amazon, YouTube, Facebook, LinkedIn and others. The need to leverage the potential of combining information in a meaningful way in order to be able to benefit from the Web will create further demand for and interest in Semantic Web research. This movement, based on the growing maturity of related research results, necessitates a reliable reference source from which beginners to the field can draw a first basic knowledge of the main underlying technologies as well as state-of-the-art application areas. This handbook, put together by three leading authorities in the field, and supported by an advisory board of highly reputed researchers, fulfils exactly this need. It is the first dedicated reference work in this field, collecting contributions about both the technical foundations of the Semantic Web as well as their main usage in other scientific fields like life sciences, engineering, business, or education.
---
paper_title: Development of semantic decision tree

paper_content:

The Semantic Web is an evolving development of the World Wide Web in which the meaning of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content. RDF and OWL is published by W3C for the standard of Semantic Web languages which can define relationships and structures between Web resources. Since the start of Semantic Web, the semantic web data is incrementally increased by Portal, Search Engines and Linking Open Data project. Therefore, the necessity of mining useful knowledge from huge size ontology is highly expected. However, the semantic web ontology has special characteristics to apply traditional decision tree algorithm which is the most popular in classification data mining. To overcome these problems, we proposed Semantic Decision Tree Algorithm which can perform on the semantic web ontology. This algorithm will help to mine the covered knowledge in the semantic web data.
---
paper_title: Finding association rules in semantic web data

paper_content:

The amount of ontologies and semantic annotations available on the Web is constantly growing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/(S) and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of query patterns. Initial experiments performed on semantic data of a biomedical application show the usefulness and efficiency of the approach.
---
paper_title: Secure and Intelligent Decision Making in Semantic Web Mining

paper_content:

With the huge amount of information available online the World Wide Web is a fertile area for data mining research. The Web has become a major vehicle in performing research and education related activities for researches and students. Web mining is the use of data mining technologies to automatically interact and discover information from web documents, which can be in structured, unstructured or semistructured form. We present an enterprise framework regarding semantic web mining in distance learning, which can be used to not only improve the quality of web mining results but also enhances the functions and services and the interoperability of long distance educational information systems and standards in the educational field. For on line distance education system we propose an Ontology-based approach to share online data and retrieve all relevant data about students and their courses. Thus semantic web ontology help build better web mining analysis in educational institute and web mining in-turns helps contract basis more powerful ontology in distance learning. Since the majority of the online data considered as private data we need various mechanism for privacy preservation and control over the online presence data. We propose privacy protection in semantic web mining using role back access control.
---
paper_title: Research on semantic Web mining

paper_content:

A semantic-based Web mining is mentioned by many people in order to improve Web service levels and address the existing Web services which is supported by the lack of semantic problem. Semantic-based Web data mining is a combination of the semantic Web and Web mining. Web mining results help to build the semantic Web. The knowledge of Semantic Web makes Web mining easier to achieve, but also can improve the effectiveness of Web mining. This paper firstly introduces the related knowledge of Semantic Web and Web mining, and then discusses the semantic-based Web mining, finally proposes to build a semantic-based Web mining model under the framework of the Agent.
---
paper_title: Mining association rules from semantic web data

paper_content:

The amount of ontologies and semantic annotations available on the Web is constantly increasing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/S and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive just the appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of a query pattern. Initial experiments performed on real world semantic data enjoy promising results and show the usefulness of the approach.
---
paper_title: Handbook of Semantic Web Technologies

paper_content:

After years of mostly theoretical research, Semantic Web Technologies are now reaching out into application areas like bioinformatics, eCommerce, eGovernment, or Social Webs. Applications like genomic ontologies, semantic web services, automated catalogue alignment, ontology matching, or blogs and social networks are constantly increasing, often driven or at least backed up by companies like Google, Amazon, YouTube, Facebook, LinkedIn and others. The need to leverage the potential of combining information in a meaningful way in order to be able to benefit from the Web will create further demand for and interest in Semantic Web research. This movement, based on the growing maturity of related research results, necessitates a reliable reference source from which beginners to the field can draw a first basic knowledge of the main underlying technologies as well as state-of-the-art application areas. This handbook, put together by three leading authorities in the field, and supported by an advisory board of highly reputed researchers, fulfils exactly this need. It is the first dedicated reference work in this field, collecting contributions about both the technical foundations of the Semantic Web as well as their main usage in other scientific fields like life sciences, engineering, business, or education.
---
paper_title: Secure and Intelligent Decision Making in Semantic Web Mining

paper_content:

With the huge amount of information available online the World Wide Web is a fertile area for data mining research. The Web has become a major vehicle in performing research and education related activities for researches and students. Web mining is the use of data mining technologies to automatically interact and discover information from web documents, which can be in structured, unstructured or semistructured form. We present an enterprise framework regarding semantic web mining in distance learning, which can be used to not only improve the quality of web mining results but also enhances the functions and services and the interoperability of long distance educational information systems and standards in the educational field. For on line distance education system we propose an Ontology-based approach to share online data and retrieve all relevant data about students and their courses. Thus semantic web ontology help build better web mining analysis in educational institute and web mining in-turns helps contract basis more powerful ontology in distance learning. Since the majority of the online data considered as private data we need various mechanism for privacy preservation and control over the online presence data. We propose privacy protection in semantic web mining using role back access control.
---
paper_title: Finding association rules in semantic web data

paper_content:

The amount of ontologies and semantic annotations available on the Web is constantly growing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/(S) and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of query patterns. Initial experiments performed on semantic data of a biomedical application show the usefulness and efficiency of the approach.
---
paper_title: “Hello world”, web mining for e-learning

paper_content:

Abstract As the internet and mobile applications are getting an important role in our lives, usage of mobile services also took place in educational field since the internet is widespread, which is usually called by the terms “e-learning” or “distance learning”. A known issue on e-learning is all the content’s being online and less face-to-face communication than traditional learning; this brings the problem of chasing student’s success, and advising and managing student’s way of studying. Hence, a recent hot topic, data mining, can be applied on student’s data left on e-learning portals to guide the instructor and advisors to help students’ being more successful. Recent researches done on this topic showed that e-learning combined with data mining can decrease the gap between itself and traditional learning — referred as semantic web mining in general.
---
paper_title: Research on semantic Web mining

paper_content:

A semantic-based Web mining is mentioned by many people in order to improve Web service levels and address the existing Web services which is supported by the lack of semantic problem. Semantic-based Web data mining is a combination of the semantic Web and Web mining. Web mining results help to build the semantic Web. The knowledge of Semantic Web makes Web mining easier to achieve, but also can improve the effectiveness of Web mining. This paper firstly introduces the related knowledge of Semantic Web and Web mining, and then discusses the semantic-based Web mining, finally proposes to build a semantic-based Web mining model under the framework of the Agent.
---
paper_title: Mining association rules from semantic web data

paper_content:

The amount of ontologies and semantic annotations available on the Web is constantly increasing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/S and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive just the appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of a query pattern. Initial experiments performed on real world semantic data enjoy promising results and show the usefulness of the approach.
---
paper_title: Development of semantic decision tree

paper_content:

The Semantic Web is an evolving development of the World Wide Web in which the meaning of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content. RDF and OWL is published by W3C for the standard of Semantic Web languages which can define relationships and structures between Web resources. Since the start of Semantic Web, the semantic web data is incrementally increased by Portal, Search Engines and Linking Open Data project. Therefore, the necessity of mining useful knowledge from huge size ontology is highly expected. However, the semantic web ontology has special characteristics to apply traditional decision tree algorithm which is the most popular in classification data mining. To overcome these problems, we proposed Semantic Decision Tree Algorithm which can perform on the semantic web ontology. This algorithm will help to mine the covered knowledge in the semantic web data.
---
paper_title: Finding association rules in semantic web data

paper_content:

The amount of ontologies and semantic annotations available on the Web is constantly growing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/(S) and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of query patterns. Initial experiments performed on semantic data of a biomedical application show the usefulness and efficiency of the approach.
---
paper_title: “Hello world”, web mining for e-learning

paper_content:

Abstract As the internet and mobile applications are getting an important role in our lives, usage of mobile services also took place in educational field since the internet is widespread, which is usually called by the terms “e-learning” or “distance learning”. A known issue on e-learning is all the content’s being online and less face-to-face communication than traditional learning; this brings the problem of chasing student’s success, and advising and managing student’s way of studying. Hence, a recent hot topic, data mining, can be applied on student’s data left on e-learning portals to guide the instructor and advisors to help students’ being more successful. Recent researches done on this topic showed that e-learning combined with data mining can decrease the gap between itself and traditional learning — referred as semantic web mining in general.
---
paper_title: Research on semantic Web mining

paper_content:

A semantic-based Web mining is mentioned by many people in order to improve Web service levels and address the existing Web services which is supported by the lack of semantic problem. Semantic-based Web data mining is a combination of the semantic Web and Web mining. Web mining results help to build the semantic Web. The knowledge of Semantic Web makes Web mining easier to achieve, but also can improve the effectiveness of Web mining. This paper firstly introduces the related knowledge of Semantic Web and Web mining, and then discusses the semantic-based Web mining, finally proposes to build a semantic-based Web mining model under the framework of the Agent.
---
paper_title: Using data mining techniques for exploring learning object repositories

paper_content:

Purpose – This paper aims to show the results obtained from the data mining techniques application to learning objects (LO) metadata.Design/methodology/approach – A general review of the literature was carried out. The authors gathered and pre‐processed the data, and then analyzed the results of data mining techniques applied upon the LO metadata.Findings – It is possible to extract new knowledge based on learning objects stored in repositories. For example it is possible to identify distinctive features and group learning objects according to them. Semantic relationships can also be found among the attributes that describe learning objects.Research limitations/implications – In the first section, four test repositories are included for case study. In the second section, the analysis is focused on the most complete repository from the pedagogical point of view.Originality/value – Many publications report results of analysis on repositories mainly focused on the number, evolution and growth of the learning...
---
paper_title: Mining association rules from semantic web data

paper_content:

The amount of ontologies and semantic annotations available on the Web is constantly increasing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/S and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive just the appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of a query pattern. Initial experiments performed on real world semantic data enjoy promising results and show the usefulness of the approach.
---
paper_title: Context and target configurations for mining RDF data

paper_content:

Association rule mining has been widely studied in the context of basket analysis and sale recommendations [1]. In fact, the concept can be applied to any domain with many items or events in which interesting relationships can be inferred from co-occurrence of those items or events in existing subsets (transactions). The increasing amount of Linked Open Data (LOD) in the World Wide Web raises new opportunities and challenges for the data mining community [5]. LOD is often represented in the Resource Description Framework (RDF) data model. In RDF, data is represented by a triple structure consisting of subject, predicate, and object (SPO). Each triple represents a statement/fact. We propose an approach that applies association rule mining at statement level by introducing the concept of mining configurations.
---
paper_title: Extracting significant Website Key Objects: A Semantic Web mining approach

paper_content:

Web mining has been traditionally used in different application domains in order to enhance the content that Web users are accessing. Likewise, Website administrators are interested in finding new approaches to improve their Website content according to their users' preferences. Furthermore, the Semantic Web has been considered as an alternative to represent Web content in a way which can be used by intelligent techniques to provide the organization, meaning, and definition of Web content. In this work, we define the Website Key Object Extraction problem, whose solution is based on a Semantic Web mining approach to extract from a given Website core ontology, new relations between objects according to their Web user interests. This methodology was applied to a real Website, whose results showed that the automatic extraction of Key Objects is highly competitive against traditional surveys applied to Web users.
---
paper_title: Development of semantic decision tree

paper_content:

The Semantic Web is an evolving development of the World Wide Web in which the meaning of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content. RDF and OWL is published by W3C for the standard of Semantic Web languages which can define relationships and structures between Web resources. Since the start of Semantic Web, the semantic web data is incrementally increased by Portal, Search Engines and Linking Open Data project. Therefore, the necessity of mining useful knowledge from huge size ontology is highly expected. However, the semantic web ontology has special characteristics to apply traditional decision tree algorithm which is the most popular in classification data mining. To overcome these problems, we proposed Semantic Decision Tree Algorithm which can perform on the semantic web ontology. This algorithm will help to mine the covered knowledge in the semantic web data.
---
paper_title: Finding association rules in semantic web data

paper_content:

The amount of ontologies and semantic annotations available on the Web is constantly growing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/(S) and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of query patterns. Initial experiments performed on semantic data of a biomedical application show the usefulness and efficiency of the approach.
---
paper_title: Context and target configurations for mining RDF data

paper_content:

Association rule mining has been widely studied in the context of basket analysis and sale recommendations [1]. In fact, the concept can be applied to any domain with many items or events in which interesting relationships can be inferred from co-occurrence of those items or events in existing subsets (transactions). The increasing amount of Linked Open Data (LOD) in the World Wide Web raises new opportunities and challenges for the data mining community [5]. LOD is often represented in the Resource Description Framework (RDF) data model. In RDF, data is represented by a triple structure consisting of subject, predicate, and object (SPO). Each triple represents a statement/fact. We propose an approach that applies association rule mining at statement level by introducing the concept of mining configurations.
---
paper_title: Secure and Intelligent Decision Making in Semantic Web Mining

paper_content:

With the huge amount of information available online the World Wide Web is a fertile area for data mining research. The Web has become a major vehicle in performing research and education related activities for researches and students. Web mining is the use of data mining technologies to automatically interact and discover information from web documents, which can be in structured, unstructured or semistructured form. We present an enterprise framework regarding semantic web mining in distance learning, which can be used to not only improve the quality of web mining results but also enhances the functions and services and the interoperability of long distance educational information systems and standards in the educational field. For on line distance education system we propose an Ontology-based approach to share online data and retrieve all relevant data about students and their courses. Thus semantic web ontology help build better web mining analysis in educational institute and web mining in-turns helps contract basis more powerful ontology in distance learning. Since the majority of the online data considered as private data we need various mechanism for privacy preservation and control over the online presence data. We propose privacy protection in semantic web mining using role back access control.
---
paper_title: Using Ontologies in Semantic Data Mining with SEGS and g-SEGS

paper_content:

With the expanding of the SemanticWeb and the availability of numerous ontologies which provide domain background knowledge and semantic descriptors to the data, the amount of semantic data is rapidly growing. The data mining community is faced with a paradigm shift: instead of mining the abundance of empirical data supported by the background knowledge, the new challenge is to mine the abundance of knowledge encoded in domain ontologies, constrained by the heuristics computed from the empirical data collection. We address this challenge by an approach, named semantic data mining, where domain ontologies define the hypothesis search space, and the data is used as means of constraining and guiding the process of hypothesis search and evaluation. The use of prototype semantic data mining systems SEGS and g-SEGS is demonstrated in a simple semantic data mining scenario and in two reallife functional genomics scenarios of mining biological ontologies with the support of experimental microarray data.
---
paper_title: “Hello world”, web mining for e-learning

paper_content:

Abstract As the internet and mobile applications are getting an important role in our lives, usage of mobile services also took place in educational field since the internet is widespread, which is usually called by the terms “e-learning” or “distance learning”. A known issue on e-learning is all the content’s being online and less face-to-face communication than traditional learning; this brings the problem of chasing student’s success, and advising and managing student’s way of studying. Hence, a recent hot topic, data mining, can be applied on student’s data left on e-learning portals to guide the instructor and advisors to help students’ being more successful. Recent researches done on this topic showed that e-learning combined with data mining can decrease the gap between itself and traditional learning — referred as semantic web mining in general.
---
paper_title: Mining association rules from semantic web data

paper_content:

The amount of ontologies and semantic annotations available on the Web is constantly increasing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/S and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive just the appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of a query pattern. Initial experiments performed on real world semantic data enjoy promising results and show the usefulness of the approach.
---
paper_title: Extracting significant Website Key Objects: A Semantic Web mining approach

paper_content:

Web mining has been traditionally used in different application domains in order to enhance the content that Web users are accessing. Likewise, Website administrators are interested in finding new approaches to improve their Website content according to their users' preferences. Furthermore, the Semantic Web has been considered as an alternative to represent Web content in a way which can be used by intelligent techniques to provide the organization, meaning, and definition of Web content. In this work, we define the Website Key Object Extraction problem, whose solution is based on a Semantic Web mining approach to extract from a given Website core ontology, new relations between objects according to their Web user interests. This methodology was applied to a real Website, whose results showed that the automatic extraction of Key Objects is highly competitive against traditional surveys applied to Web users.
---
paper_title: Development of semantic decision tree

paper_content:

The Semantic Web is an evolving development of the World Wide Web in which the meaning of information and services on the web is defined, making it possible for the web to understand and satisfy the requests of people and machines to use the web content. RDF and OWL is published by W3C for the standard of Semantic Web languages which can define relationships and structures between Web resources. Since the start of Semantic Web, the semantic web data is incrementally increased by Portal, Search Engines and Linking Open Data project. Therefore, the necessity of mining useful knowledge from huge size ontology is highly expected. However, the semantic web ontology has special characteristics to apply traditional decision tree algorithm which is the most popular in classification data mining. To overcome these problems, we proposed Semantic Decision Tree Algorithm which can perform on the semantic web ontology. This algorithm will help to mine the covered knowledge in the semantic web data.
---

Title: A State-of-the-Art Survey on Semantic Web Mining

Section 1: Introduction
Description 1: Introduce the concept of Semantic Web Mining by integrating Semantic Web and Data Mining, emphasizing the growing importance and potential applications of this field.

Section 2: Semantic Web
Description 2: Provide an overview of the Semantic Web, including its definition, reasons for development, representation techniques, and examples from the commercial domain.

Section 3: Data Mining
Description 3: Introduce the concept of data mining, describe popular data mining techniques, and provide a brief overview of Web Mining and its types.

Section 4: Semantic Web Mining
Description 4: Offer a detailed introduction to Semantic Web Mining, discuss challenges faced, propose solutions, and present case studies supporting its usefulness across various domains.

Section 5: Conclusion
Description 5: Summarize the survey findings, highlighting the synergy between Web Mining and the Semantic Web, and conclude with insights into the future potential of Semantic Web Mining.

Section 6: Acknowledgements
Description 6: Acknowledge the support and contributions of individuals and organizations that facilitated the research.