Final Remarks for the Investigation in Ontology in IS and Possible Future Directions

This study is concluded in this chapter. The research problem and questions derived from it are answered. In addition, the achievements and the limitations of this study are discussed. The research started with identifying the problem. To achieve these objectives, the OIS was designed and developed. Feedback and evaluation from the domain's experts has led to constant improvement in the ontology's development. The current version of the OIS ontology is presented in this research. At the end of this chapter, possible research leads for the future are suggested. The study aimed at the creation of OIS ontology of Information Science domain to visualise its knowledge, in order to be integrated with other ontologies to be applied for a specific application. The resulting ontology covers three main areas of domain knowledge: library science, archival science and computing science. The vocabularies of these branches are formalised in class hierarchy with relations which are interconnecting concepts from all these areas, in order to define a sufficient model of the Information Science domain.

Modelling Design of OIS Ontology

This chapter presents the development of OIS ontology and the main elements that formalised in OWL-DL. The OIS ontology followed Methontology as a general framework of methodology. The main result will be introduced, namely, the modelling design of OIS ontology which follows the description of the activities involved in designing the OIS ontology model. The OIS ontology model identifies the terms and definitions in the IS domain. Also, designing the ontocop system and how it can be a useful platform for supporting and assessing the OIS ontology. It starts by introducing OIS designing methodology. At the end of this chapter we will discuss how this tool will help to develop the OIS ontology to be modelled in a comprehensive and consistent manner.

Debunking Intermediary Censorship Framework in Social Media via a Content Retrieval and Classification Software

Following the enactment of computer crime law in Thailand, online service providers are compelled to control illegal content including content that is deemed harmful or problematic. This situation leads to self-censorship of intermediaries, often resulting in overblocking to avoid violating the law. Such filtering flaw both infringes users' freedom of expression and impedes the business of OSPs in Thailand. The Innovative Retrieval System (IRS) is thus developed to investigate intermediary censorship in online discussion forum, Pantip.com, as a case study of social media. The result shows that there is no consistency of censorship pattern on the website at all. The censorship criteria depend on type of content in each forum. Overblocking is also high, over 70% of removed content, due to intimidation of governmental agencies, lawsuits from business organizations, and fear of intermediary liability. Website administrator admitted that he would cut off some users to avoid business troubles.

Knowledge Discovery for Large Databases in Education Institutes

This project presents the patterns and relations between attributes of Iran Higher Education data gained from the use of data mining techniques to discover knowledge and use them in decision making system of IHE. Large dataset of IHE is difficult to analysis and display, since they are significant for decision making in IHE. This study utilized the famous data mining software, Weka and SOM to mine and visualize IHE data. In order to discover worthwhile patterns, we used clustering techniques and visualized the results. The selected dataset includes data of five medical university of Tehran as a small data set and Ministry of Science - Research and Technology's universities as a larger data set. Knowledge discovery and visualization are necessary for analyzing of these datasets. Our analysis reveals some knowledge in higher education aspect related to program of study, degree in each program, learning style, study mode and other IHE attributes. This study helps to IHE to discover knowledge in a visualize way; our results can be focused more by experts in higher education field to assess and evaluate more.

Securing Financial XML Transactions Using Intelligent Fuzzy Classification Techniques

The eXtensible Markup Language (XML) has been widely adopted in many financial institutions in their daily transactions. This adoption was due to the flexible nature of XML providing a common syntax for systems messaging in general and in financial messaging in specific. Excessive use of XML in financial transactions messaging created an aligned interest in security protocols integrated into XML solutions in order to protect exchanged XML messages in an efficient yet powerful mechanism. However, financial institutions (i.e. banks) perform large volume of transactions on daily basis which require securing XML messages on large scale. Securing large volume of messages will result performance and resource issues. Therefore, an approach is needed to secure specified portions of an XML document, syntax and processing rules for representing secured parts. In this research we have developed a smart approach for securing financial XML transactions using effective and intelligent fuzzy classification techniques. Our approach defines the process of classifying XML content using a set of fuzzy variables. Upon fuzzy classification phase, a unique value is assigned to a defined attribute named “Importance Level”. Assigned value indicates the data sensitivity for each XML tag. The research also defines the process of securing classified financial XML message content by performing element-wise XML encryption on selected parts defined in fuzzy classification phase. Element-wise encryption is performed using symmetric encryption using AES algorithm with different key sizes. Key size of 128-bit is being used on tags classified with “Medium” importance level; a key size of 256-bit is being used on tags classified with “High” importance level. An implementation has been performed on a real-life environment using online banking system in Jordan Ahli Bank one of the leading banks in Jordan to demonstrate its flexibility, feasibility, and efficiency. Our experimental results of the system verified tangible enhancements in encryption efficiency, processing-time reduction, and resulting XML message sizes. Finally, our proposed system was designed, developed, and evaluated using a live data extracted from an internet banking service in one of the leading banks in Jordan. The results obtained from our experiments are promising, showing that our model can provide an effective yet resilient support for financial systems to secure exchanged financial XML messages.

Methodology of Creating Ontology of Information Science (OIS)

In the previous chapter we have discussed the main fields related to the research: ontological engineering, knowledge management, and Virtual communities of practice. As stated before, our concern is representing domain knowledge by creating OIS ontology. After reviewing the ontology literature to find an appropriate theoretical perspective focusing on the content-related variables for theoretical model construction, we found that theories can help to define formal ontological properties that contribute to characterising the concepts. Meanwhile, ontologists nowadays have a choice of formal frameworks which derive from formal logic, algebra, category theory, set theory and Mereotopology. However, to gain a better understand of OIS ontology development and its role in semantic web, the framework is established to describe the main theoretical base. The theoretical base of our framework is based on ontology theoretic.

The Impact of the Mode of Data Representation for the Result Quality of the Detection and Filtering of Spam

Spam is now of phenomenal proportions since it represents a high percentage of total emails exchanged on the Internet. In the fight against spam, we are using this article to develop a hybrid algorithm based primarily on the probabilistic model in this case, Naïve Bayes, for weighting the terms of the matrix term -category and second place used an algorithm of unsupervised learning (K-means) to filter two classes, namely spam and ham (legitimate email). To determine the sensitive parameters that make up the classifications we are interested in studying the content of the messages by using a representation of messages using the n-gram words and characters independent of languages (because a message may be received in any language) to later decide what representation to use to get a good classification. We have chosen several metrics as evaluation to validate our results.

Findings for Ontology in IS and Discussion

Ontology development is meaningful and useful for both users and IR; therefore, it needs to be evaluated. In this chapter, we are going to test and evaluate the results produced in the research, which is the development of the OIS ontology life cycle. It describes the testing and validation which was applied to the whole model from the initial implementation to ensure consistency of modelled knowledge. The evaluation objective was to collect feedback on OIS ontology by using our evaluation system. The Ontocop system is a platform that has been implemented to get feedback from the IS community. The feedback is assessing and eliciting further details that support the ontology development. The evaluation and discussion will be at two levels based on Gòmez-Pérez's view.

User Query Enhancement for Behavioral Targeting

Ad targeting has been receiving more and more attention in the online publishing world, where advertisers want their ads to be seen by potential consumers at the right time. This chapter aims to address the major challenges with user queries in the context of behavioral targeting advertising by proposing a user intent representation strategy and a query enhancement mechanism. The authors focus on investigating the intent based user classification performance and the effectiveness of user segmentation under a topic model that helps explore semantic relation between user queries in behavioral targeting. In addition, the authors propose an alternative to define user's search intent for the evaluation purpose, in the case that the dataset is sanitized.

Spatial Databases

Spatial databases maintain space information which is appropriate for applications where there is need to monitor the position of an object or event over space. Spatial databases describe the fundamental representation of the object of a dataset that comes from spatial or geographic entities. A spatial database supports aspects of space and offers spatial data types in its data model and query language. The spatial or geographic referencing attributes of the objects in a spatial database permits them to be positioned within a two (2) dimensional or three (3) dimensional space. This chapter looks into the fundamentals of spatial databases and describes their basic component, operations and architecture. The study focuses on the data models, query Language, query processing, indexes and query optimization of a spatial databases that approves spatial databases as a necessary tool for data storage and retrieval for multidimensional data of high dimensional spaces.