Towards Ontology Development Based on Relational Database

Information and resources available on the Web are growing increasingly and web users need to have a common understanding of them. The Semantic Web whose most important role is to help machine to understand and analyze the existing data on the Web, has not been used commonly, yet. The foundation of the Semantic Web are ontologies. Ontologies play the main role in the exchange of information and development of the Lexical Web to the Semantic Web. Manual construction of ontologies is time-consuming, expensive, and dependent on the knowledge of domain engineers. Also, Ontologies that have been extracted automatically from corpus on the Web might have incomplete information. The main objective of this study is describing a method to improve and expand the information of the ontologies. Therefore, this study first discusses the automatic construction of prototype ontology in animals‟ domain from Wikipedia and then a method is presented to improve the built ontology. The proposed method of improving ontology expands ontology concepts through Bootstrapping methods using a set of concepts and relations in initial ontology and with the help of the Google search engine. A confidence measure was considered to choose the best option from the returned results by Google. Finally, the experiments showed the information that was obtained using the proposed method is twice more accurate than the information that was obtained at the stage of automatic construction of ontology from Wikipedia.

The explosive growth of textual data on the web coupled with the increase on demand for ontologies to promote the semantic web, have made the automatic ontology construction from the text a very promising research area. Ontology learning (OL) from text is a process that aims to (semi-) automatically extract and represent the knowledge from text in machine-readable form. Ontology is considered one of the main cornerstones of representing the knowledge in a more meaningful way on the semantic web. Usage of ontologies has proven to be beneficial and efficient in different applications (e.g. information retrieval, information extraction, and question answering). Nevertheless, manually construction of ontologies is time-consuming as well extremely laborious and costly process. In recent years, many approaches and systems that try to automate the construction of ontologies have been developed. This paper reviews various approaches, systems, and challenges of automatic ontology construction from the text. In addition, it also discusses ways the ontology construction process could be enhanced in the future by presenting techniques from shallow learning to deep learning (DL).

With the proliferation of very large data repositories hidden behind web interfaces, e.g., keyword search, form-like search and hierarchical/graph-based browsing interfaces for Amazon.com, eBay.com, etc., efficient ways of searching, exploring and/or mining such web data are of increasing importance.

From the beginning it was understood that the success of the Semantic Web hinges on integrating the vast amount of data stored in Relational Databases. This manuscript reflects on the last 10 years of our research results to integrate Relational Databases with the Semantic Web. Since 2007, our research has led us to answer the following question: How and to what extent can Relational Databases be Integrated with the Semantic Web? The answer comes in two parts. We start by presenting how to get from Relational Databases to the Semantic Web via mappings, such as the W3C Direct Mapping and R2RML standards. Subsequently, we present how the Semantic Web can access Relational Databases. We finalize with how Relational Databases and Semantic Web technologies are being used practice for data integration and discuss open challenges.

The objective of data mining is to find the useful information from the huge amounts of data. Many researchers have been proposed the different algorithms to find the useful patterns but one of the most important drawbacks they have found that data mining techniques works for single data table. This technique is known as traditional data mining technique. In this era almost all data available in the form of relational database which have multiple tables and their relationships. The new data mining technique has emerged as an alternative for describing structured data such as relational data base, since they allow applying data mining in multiple tables directly, which is known as Multi Relational data mining. To avoid the more number joining operations as well as the semantic losses the researchers bound to use Multi Relational Data Mining approaches. In this paper MRDM focuses multi relational association rule, Multi relational decision tree construction, Inductive logic program (ILP) as well three statistical approaches. We emphasize each MR-Classification approach as well as their characteristics, comparisons as per the statistical values and finally found the most research challenging problems in MRDM.

Malware or malicious applications can cause catastrophic damage to not only computer systems but also data centers, web, and mobile applications from various industries; the Ministry of Interior, in particular, is the most important educational institution because they are more vulnerable to security breaches. Keeping stakeholder data safe from unwanted actors is a big concern that brings us to the concept of malware detection and prevention. Deep learning and data mining using artificial intelligence (AI) can be an efficient approach for developing anti-malware systems. Following suit, this study gave a thorough examination of malware detection methodologies and procedures. Initially, we attempted to provide a comprehensive description of malware, artificial intelligence, and data mining, as well as a listing of these technologies. The suggested system was described (whether this data is files, photographs, videos, or import limitations and is processed and identified by mining and deep learning data, and the system was trained on data). So far, our findings suggest that artificial intelligence and data mining can be used to construct anti-malware systems to detect and prevent malware assaults or security threats in software applications geared toward technological wonderland and its real-world application in the Ministry of Interior. To conclude, we outline dozens of possibilities for overcoming the observed restrictions and intend to expressly continue our efforts toward significant advancements in malware detection and prevention by implementing this proposal. We give a detailed look at the current ways to find malware, their flaws, and ways to make them more effective. We also explain how we're working on integrating the system. Our study shows that adopting future approaches to developing malware detection applications should provide significant advantages. Understanding this structure should help researchers do more research on malware detection and prevention using AI and data mining.

In contracts to the isolated data silos of the conventional Web, the Semantic Web interconnects open data, so that all datasets contribute to a global data integration, connecting data from diverse domains, such as people, companies, books, scientific publications, films, music, reviews, television and radio programs, medicine, statistics, online communities, and scientific data. The union of the structured datasets forms the Linked Open Data Cloud, the decentralized core of the Semantic Web, where software agents can automatically find relationships between entities and make new discoveries. Linked Data browsers allow users to browse a data source, and by using special (typed) links, navigate along links into other related data sources. Linked Data search engines crawl the Web of Data by following links between data sources and provide expressive query capabilities over aggregated data. To support data processing for new types of applications, Linked Open Data (LOD) is used by search engines, governments, social media, publishing agencies, media portals, researchers, and individuals.

The rapid growth of available data arises the need for more sophisticated techniques for semantic access to information. It has been proved that using conceptual model or ontology over relational data sources is necessary to overcome many problems related with accessing the structured data. However, the task of wrapping the data residing in a database by means of an ontology is mainly done manually.The research we are carrying out studies the reuse of relational sources in the context of semantics-based access to information. This problem is tackled in two phases: (i) extracting semantics hidden in the relational sources by wrapping them by means of an ontology, (ii) understanding the methodology for semantic extension of such ontologies. In this paper we focus on the first sub-problem and present an automatic procedure for extracting from a relational database schema its conceptual view. The semantic mapping between the database and its conceptualization is captured by associating views over the data source to elements of the extracted ontology. To represent the extracted conceptual model we use an ontology language, rather that a graphical notation, in order to provide precise formal semantics. Our approach uses heuristics based on ideas of standard relational schema design and normalization. Under this we formally prove that our technique preserves the semantics of constraints in the database. Therefore, there is no data loss, and the extracted model constitutes a faithful wrapper of the relational database.

The explosion of very large databases has created extraordinary opportunities for monitoring, analyzing and predicting global economical, geographical, demographic, medical, political, and other processes in the world. Statistical analysis and data mining techniques have emerged for these purposes. Data mining is the process of discovering previously unknown but potentially useful patterns, rules, or associations from huge quantity of data. Data mining can be performed on different data repositories such as relational databases, data warehouses, transactional databases, sequence databases, spatial databases, spatio-temporal databases, and text databases, etc. Typically, data mining functionalities can be classified into two categories: descriptive and predictive. Descriptive mining tasks aim at characterizing the general properties of the data in the databases, while predictive mining tasks perform inherence on the current data in order to make prediction in future.KeywordsRoot Mean Square ErrorData MiningAssociation RuleIntrusion DetectionAnomaly DetectionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The storage of data is a key issue of information systems, which is an important foundation for data query and data mining. Relational database model has been proven to be a very useful data-storage technique. As information is stored as data in relational databases, the induction of concepts from data is a pivotal topic in the data mining field. Formal Concept Analysis (FCA) turns out to be a perfect instrument for a meaningful and conceptual exploration of the stored data. In FCA, conceptual scaling provides a complete framework for transforming any many-valued context (i.e., relation/table) into a context (called a derived context), in which each many valued attribute is given a scale. The attributes in a scale basically describe meaningful features of the values of the initial attribute. From the logical point of view, complement operation plays a very important role in relational databases and data query systems. In this paper, we provide the connections between the concepts of binary relations and those of complementary binary relations, and propose an approach toward normalizing (complementary) scales, i.e., each (complementary)scale can be represented by a set of statements. One advantage of normalizing scales is to avoid generating huge derived relations, and hence this approach reduces storage cost. By the normalization, the concept lattice of the complement of a derived relation is reduced to a combination of the concept lattice of the derived relation and a set of statements.

Despite all the efforts to build up a Semantic Web, where each machine can understand and interpret the data it processes, information is usually still stored in ordinary relational databases. Semantic Web applications needing access to such semantically unexploited data, have to create their own manual relational database to Semantic Web mappings. In this paper we analyze, whether the combination of Relational.OWL as a Semantic Web representation of relational databases and a semantic query language like SPARQL could be an alternative. The benefits of such an approach are clear, since it enables Semantic Web applications to access and query data actually stored in relational databases using their own built-in functionality.

This paper describes the relationship between data mining (DM) and artificial intelligence (AI) and how both can improve future military information systems. As these information systems take advantage of the technology trends, they will derive their upgrades from expert systems, knowledge-bases (KBs), inference engines, robotics, natural language, remote sensing, knowledge discovery (KD), and data mining over networks. This will result in an expansion to all echelons of present capabilities that now are available to warfighters only at the higher echelons. Moreover, it will result in an improved battlefield awareness for all warfighters beyond anything that is implemented today.

The purpose of the workis to explore the current problems and prospects of mining solution, big web data in real time, as well as the possibility of practical implementation of Web Mining technology for big web data on a practical example.Materials and methods.The study included a review of bibliographic sources on big data mining. We used Web Mining technology for associative analysis of large web data, as well as computer modeling of the practical task of transaction analysis using a general-purpose scripting language (PHP).Results.During the work, the specifics of the Data Mining technology were described, and a modern approach to the analysis of large web data –Web Mining was analyzed. A brief classification of tasks solved using Web Mining technology is given. The problem of data mining of large web data in a general-purpose scripting language (PHP) has been solved: the lack of libraries for data mining, the difficult normalization of data to the form necessary for associative analysis, interaction with the database management system. Also, an example showing an approach to the mining of large web data was implemented. Based on the understanding of Web Mining technology and the described difficulties of analyzing web data in the PHP language, methods for effectively solving the practical problem of analyzing web data based on transactions committed in a dynamic web application have been proposed. A module for associative analysis of customer transactions in the programming language PHP was developed. The module includes an intelligent data processing class. The structural scheme of the module and system architecture were developed. The constructed module allows us to solve the main part of the problem of associative analysis of large web data using Web Mining technology in order to solve the problem of identifying patterns in a large array of web data. Associative analysis of web data is much faster because of the combination of a general-purpose scripting language and an object-oriented approach.Conclusion.According to the results of the study, it can be argued that the current state of the technology for the analysis of large web data allows efficiently process data objects, identify patterns, obtain hidden data and receive complete statistical data in real time. The results can be used both for the purpose of the initial research of technologies for analyzing large web data, and as an addition to the content management system for the intelligent analysis of web data. The usage of the technology of associative analysis and the created universal handler class makes the created module flexible, while the possibility of manual integration makes this module universal. With manual integration, the database management system is not important. Algorithm methods work with selected data. This factor greatly simplifies the further development of program code.

Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g. point-dimension, observation-variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, where as the SPJ method does not. In a relational database, especially with normalized tables, a significant effort is required to prepare a summary data set that can be used as input for a data mining or statistical algorithm. Most algorithms require as input a data set with a horizontal layout, with several Records and one variable or dimension per column. That is the case with models like clustering, classification, regression and PCA; consult. Each research discipline uses different terminology to describe the data set. In data mining the common terms are point-dimension. Statistics literature generally uses observation-variable. Machine learning research uses instance-feature. This article introduces a new class of aggregate functions that can be used to build data sets in a horizontal layout (denormalized with aggregations), automating SQL query writing and extending SQL capabilities. We show evaluating horizontal aggregations is a challenging and interesting problem and we introduced alternative methods and optimizations for their efficient evaluation. II. MOTIVATION As mentioned above, building a suitable data set for data mining purposes is a time- consuming task. This task generally requires writing long SQL statements or customizing SQL Code if it is automatically generated by some tool. There are two main ingredients in such SQL code: joins and aggregations; we focus on the second one. The most widely- known aggregation is the sum of a column over groups of rows. Some other aggregations return the average, maximum, minimum or row count over groups of rows. There exist many aggregations functions and operators in SQL. Unfortunately, all these aggregations have limitations to build data sets for data mining purposes. The main reason is that, in general, data sets that are stored in a relational database (or a data warehouse) come

The rapid advancement of information technology has led to an upsurge in cybercriminal activities. As technology continues to evolve, so do the tactics used by individuals involved in digital offenses. Trends in complex, distributed, and internet[1]based computing have raised significant concerns regarding information security and privacy. Cyber infrastructures, in particular, are highly susceptible to intrusions and various threats. Traditional security measures like sensors and detectors are inadequate for safeguarding these infrastructures, necessitating the development of more sophisticated IT solutions capable of modeling normal behaviors and identifying anomalies. To address these challenges effectively, cyber defense systems must exhibit traits such as flexibility, adaptability, and robustness. They should be able to detect a wide range of threats while making intelligent real-time decisions. Given the sheer volume and speed of cyberattacks, relying solely on human intervention is inadequate for prompt analysis and response. Many of these attacks are orchestrated by intelligent agents like computer worms and viruses, making it essential to combat them using intelligent, semi-autonomous agents that can promptly detect, evaluate, and respond to cyber threats. These computer-generated forces must manage the entire process of responding to attacks, encompassing the identification of the attack type, its targets, the appropriate response, and the prioritization of secondary attack prevention. Furthermore, cyber intrusions are not confined to a single location; they represent a global menace to computer systems worldwide. The expansion of the internet has made knowledge and tools for cybercrime readily accessible to a wide audience, no longer limited to educated specialists. Traditional, rigid algorithms with hard-wired logic have proven ineffective in countering dynamically evolving cyberattacks. This underscores the importance of innovative approaches, particularly the application of Artificial Intelligence (AI), to enhance our capability to combat cybercrimes. AI introduces flexibility and learning capabilities to software, thereby assisting humans in the fight against cybercrimes. Various AI techniques, inspired by nature, including Computational Intelligence, Neural Networks, Intelligent Agents, Artificial Immune Systems, Machine Learning, Data Mining, Pattern Recognition, Fuzzy Logic, and Heuristics, are playing an increasingly crucial role in the detection and prevention of cybercrimes. AI empowers the design of autonomic computing solutions that can adapt to their usage context, employing methods such as self-management, self-tuning, self[1]configuration, self-diagnosis, and self[1]healing. In the realm of information security, AI represents a promising area of research with a focus on enhancing cybersecurity measures in cyberspace. The term "Artificial Intelligence" is used to describe a machine's ability to emulate human-like activities, including problem solving and learning, a concept often referred to as machine learning. The next generation of cybersecurity products is increasingly incorporating Artificial Intelligence and Machine Learning technologies. By analyzing extensive datasets of cybersecurity, network, and physical information, providers of cybersecurity solutions aim to identify and thwart abnormal behavior. Various approaches are employed to utilize AI for cybersecurity. Some applications analyze raw network data to detect irregularities, while others focus on user-entity behavior to identify deviations from the norm. The choice of approach depends on the type of data streams and the level of effort required by analysts