A novel model for mining association rules from semantic web data

With the introduction and standardization of the semantic web as the third generation of the Web, this technology has attracted and received more human attention than ever and thus the amount of semantic web data is constantly growing. These semantic web data are a rich source of useful knowledge for feeding data mining techniques. Semantic web data have some complexities, such as the heterogeneous structure of the data, the lack of exactlydefined transactions, the existence of typed relationships between entities etc. One of the data mining techniques is association rule mining, the goal of which is to find interesting rules based on frequent item-sets. In this paper we propose a novel method that considers the complex nature of semantic web data and, without end-user involvement and any data conversion to traditional forms, mines association rules directly from semantic web datasets at the instance level. This method assumes that data have been stored in triple format (Subject, Predicate, and Object) in a single dataset. For evaluation purposes the proposed method has been applied to a drugs dataset that experiments results show the ability of the proposed algorithm in mining ARs from semantic web data without end-user involvement.

2020

With the introduction and standardization of the semantic web as the third generation of the web, this technology has attracted and received more human attention than ever. Thus, the amount of semantic web data is continuously growing, which makes them a rich source of useful data for data mining techniques. Semantic web data have some complexities, such as the heterogeneous structure of data, the lack of well-defined transactions, and the existence of typed relations between items. In this paper, a new technique named SWApriori is presented, which by using both entities and relations in the extraction of frequent itemsets, generates a new class of association rules (ARs) from semantic web data. The proposed technique by considering the complex heterogeneous nature of semantic web data, without any need to a domain expert, and without any data conversion to transactional data format extracts ARs from semantic web data directly. For evaluation, the proposed technique is applied to Fa...

International Journal of Engineering Research and Technology (IJERT), 2012

https://www.ijert.org/mining-association-rules-using-ontologies https://www.ijert.org/research/mining-association-rules-using-ontologies-IJERTV1IS7457.pdf Association rule mining is considered as one of the most important tasks in Knowledge Discovery in Databases. Among sets of items in transaction databases, it aims at discovering implicative tendencies that can be valuable information for the decision-maker. The rules generated by the existing methods are in more number. To reduce the number of rules several post processing methods and many techniques were developed but they are not effective. This paper aims to develop a new frame work called Mining Interest Rules Using Ontologies for extracting association rules based on user interest and also implementing a real time web semantic engine using an extended robust framework.

International Journal of Computer Applications

One of the major concerns in the field of knowledge discovery is the interestingness problem and the unreasonable number of association rules being mined. The past studies confirm that although a large number of rules are mined for each query, they do not seem to satisfy user’s expectations. The methods already proposed in the literature like post-processing and algorithms to reduce itemsets and nonredundant rules do not always guarantee mining of interesting rules for the user. In conventional Data Mining, the usefulness of association rules is limited by the huge amount of delivered rules. In this paper we propose a new interactive approach ‘Onto-Mine’ to trim and filter the discovered rules. We propose to integrate user knowledge in association rule mining by combining Domain Ontology and interactive intelligence. First, we use Domain and Background Ontology with user knowledge and this interactive intelligence of Onto-Mine assists the user throughout the analyzing task and helps...

Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007), 2007

The problem of mining association rules incorporated with domain knowledge has been studied recently. Previous work was conducted individually on two types of knowledge, classification and composition. In this paper, we revisit this problem from a more unified viewpoint. We consider the problem of mining association rules with ontological information that presents not only classification but also composition relationship. Two effective algorithms are proposed with empirical evaluation displayed.

With the rise of the Internet and the development of various electronic information resources, mining useful information from large databases has become one of the most important issues in information research for users. Data warehousing plays the key role in providing data for data mining tools to explore knowledge. However, there are still many problems that cause users to spend extra time to get genuine knowledge. In this paper, we explore the problems with contemporary association rule mining in data warehousing systems, explain the essence and propose a framework that incorporates ontologies to resolve the problems. Some interesting research issues and technical challenges on realizing such ontology-incorporated association rule mining in data warehousing systems are pointed out.

This paper proposes an integrated framework for extracting Constraint-based Multi-level Association Rules with an ontology support. The system permits the definition of a set of domain-specific constraints on a specific domain ontology, and to query the ontology for filtering the instances used in the association rule mining process. This method can improve the quality of the extracted associations rules in terms of relevance and understandability.

International Journal of Advanced Computer Science and Applications

Data mining is used for extracting related data. The association rules approach is one of the used methods for analyzing, discovering and extracting knowledge and mining the relationships among raw data. Commonly, it is important to understand and discover such knowledge directly from huge records of items stored in a relational database. This paper proposes an approach for generating human-like fuzzy association rules based on fuzzy ontology. It focuses on enhancing the process of extracting association rules from a huge database respecting a predefined domain fuzzy ontology. Commonly, association rules mining based on crisp ontology is found to be more flexible than classical ones as it considers the relationships between concepts or items. Yet, crisp ontology suffers from the problem of information losing resulted from the rigid boundaries of crisp relationships, which are approximated to be 0 or 1, between concepts. In contrast, the smooth boundaries of fuzzy sets make it able to represent partial relationships that range from 0 to 1 between concepts in an ontology in a more flexible human-like manner. Consequently, generating fuzzy association rules based on fuzzy ontology makes it more human-like and reliable compared with other previous ones. An illustrative case study, on two different data sets, shows the added value of the proposed approach compared with some other recent approaches.

2007

This paper proposes an integrated framework for the extraction of constraint-based multi-level association rules with the aid of an ontology. The latter, which represents an enriched taxonomy, is used to describe the application domain by means of data properties. Defining or updating these properties is a simple task and does not imply changing the items hierarchy, or the implementation level of our framework. The system enables the definition of domain-specific constraints, by using the ontology to filter the instances used in the association rule mining process. This can improve the quality of the extracted association rules and make them more interesting and easy to understand. We describe our framework, also including examples of queries based on real-data.

2008 IEEE International Conference on Data Mining Workshops, 2008

In Data Mining, the usefulness of association rules is strongly limited by the huge amount of delivered rules. In this paper we propose a new approach to prune and filter discovered rules. Using Domain Ontologies, we strengthen the integration of user knowledge in the postprocessing task. Furthermore, an interactive and iterative framework is designed to assist the user along the analyzing task. On the one hand, we represent user domain knowledge using a Domain Ontology over database. On the other hand, a novel technique is suggested to prune and to filter discovered rules. The proposed framework was applied successfully over the client database provided by Nantes Habitat 1 .