Semantic Web Mining Research Articles

E-commerce Webpage Recommendation Scheme Base on Semantic Mining and Neural Networks

In e-commerce websites, web mining web page recommendation technology has been widely used. However, recommendation solutions often cannot meet the actual application needs of online shopping users. To address this problem, this paper proposes an e-commerce web page recommendation solution that combines semantic web mining and BP neural networks. First, the web logs of user searches are processed and 5 features are extracted: content priority, time consumption priority, online shopping users' explicit/implicit feedback on the website, recommendation semantics and input deviation amount. Then, these features are used as input features of the BP neural network to classify and identify the priority of the final output web page. Finally, the web pages are sorted according to priority and recommended to users. This project uses book sales webpages as samples for experiments. The results show that this solution can quickly and accurately identify the webpages required by users.

Big Data Analytics, Processing Models, Taxonomy of Tools, V’s, and Challenges: State-of-Art Review and Future Implications

In the current digital era, data is budding tremendously from various sources like banks, businesses, education, entertainment, etc. Due to its significant consequence, it became a prominent proceeding for numerous research areas like the semantic web, machine learning, computational intelligence, and data mining. For knowledge extraction, several corporate sectors depend on tweets, blogs, and social data to get adequate analysis. It helps them predict the customer’s tastes and preferences, optimize the usage of resources. In some cases, the same data creates complications that lead to a problem named as big data. To solve this, so many researchers have given various solutions. Based on literature analysis formulated 6-s simulation towards big data, detailed information about characteristics, a taxonomy of tools, and discussed various processing paradigms. No one tool can truly fit for all solutions, so this paper helps to make decisions smoothly by providing enough information and discussing major privacy issues and future directions.

PoWareMatch: A Quality-aware Deep Learning Approach to Improve Human Schema Matching

Schema matching is a core task of any data integration process. Being investigated in the fields of databases, AI, Semantic Web, and data mining for many years, the main challenge remains the ability to generate quality matches among data concepts (e.g., database attributes). In this work, we examine a novel angle on the behavior of humans as matchers, studying match creation as a process. We analyze the dynamics of common evaluation measures (precision, recall, and f-measure), with respect to this angle and highlight the need for unbiased matching to support this analysis. Unbiased matching, a newly defined concept that describes the common assumption that human decisions represent reliable assessments of schemata correspondences, is, however, not an inherent property of human matchers. In what follows, we design PoWareMatch that makes use of a deep learning mechanism to calibrate and filter human matching decisions adhering to the quality of a match, which are then combined with algorithmic matching to generate better match results. We provide an empirical evidence, established based on an experiment with more than 200 human matchers over common benchmarks, that PoWareMatch predicts well the benefit of extending the match with an additional correspondence and generates high-quality matches. In addition, PoWareMatch outperforms state-of-the-art matching algorithms.

The Role of Self-Improving Tutoring Systems in Fostering Pre-Service Teacher Self-Regulated Learning.

Computer-based learning environments serve as a valuable asset to help strengthen teacher preparation and preservice teacher self-regulated learning. One of the most important advantages is the opportunity to collect ambient data unobtrusively as observable indicators of cognitive, affective, metacognitive, and motivational processes that mediate learning and performance. Ambient data refers to teacher interactions with the user interface that include but are not limited to timestamped clickstream data, keystroke and navigation events, as well as document views. We review the claim that computers designed as metacognitive tools can leverage the data to serve not only teachers in attaining the aims of instruction, but also researchers in gaining insights into teacher professional development. In our presentation of this claim, we review the current state of research and development of a network-based tutoring system called nBrowser, designed to support teacher instructional planning and technology integration. Network-based tutors are self-improving systems that continually adjust instructional decision-making based on the collective behaviors of communities of learners. A large part of the artificial intelligence resides in semantic web mining, natural language processing, and network algorithms. We discuss the implications of our findings to advance research into preservice teacher self-regulated learning.

Semantic Web Mining for Analyzing Retail Environment Using Word2Vec and CNN-FK

Digital Technology is becoming increasingly essential to organizations. Related knowledge is important for a company to allow optimal use of its IT services. The use of Big Data is relatively new to this field. Handling Big data is not, at this stage, a problem for large business organizations in particular; it has also become a challenge for small and medium-sized businesses. Although Semantic Web analysis is largely focused on fundamental advances that are expected to make the Semantic Web a reality, there has not been much work done to demonstrate the feasibility and effect of the Semantic Web on business issues. The infrastructure of electronic information executives and business types has provided various enhancements for companies, such as the automated process of buying and selling products. Nevertheless, undertakings are checked for the multifaceted nature of the extension required to deal with an ever-increasing number of electronic details and procedures. This paper suggests a model with a neural network design and a word representation system named Word2Vec for analyzing retail environment. Firstly, Word2vec manages the text data and shows it as a function diagram and a feature map is given to the Convolution Neural Network (CNN) that extracts the features and classifies them. The IMDB dataset, the Cornell dataset, the Amazon Products Dataset and the Twitter dataset were analyzed in the proposed model. The proposed Convolution Neural Network Fisher Kernel (CNN-FK) model is compared with the existing SVM model for analyzing retail environment in semantic web mining. The new approach has increased efficiency when compared to existing models.

An Art of Web Services using Web Mining and Semantic Web Techniques

Todays web browsers serve as an easy access to numerous sources of text and multimedia data. More than a billion pages are indexed by search engines, and finding the desired information is not an easiest task. Over the last decade, there is an explosive growth in the information available on the World Wide Web (WWW). The objective of this paper is to provide an outline of web mining, its various classifications, its subtasks, and to give a perspective to the research community about the potential of applying techniques to extract meaningful patterns. This paper also gives information in the area of web services, semantic web mining and comparison of traditional web applications and semantic web applications thereby providing the guidelines for future research in the area of web services using web mining and semantic web.

Semantic Network

Knowledge representation is an emerging field of research in Artificial Intelligence, Big data analytics, Semantic web, and Data Mining. Knowledge represented in an effective way helps in easy traversal, searching, reasoning, prediction, and inference. There are number of approaches, algorithms, techniques, and models that have been proposed for the same. Every approach has their own pros and cons. Hence our aim is to propose a simple and extremely effective way to represent knowledge, which has a greater expressiveness compared to logic and reverberates within the methods of people process data. In this paper we address the implementation of semantics network with simple yet powerful method, which despite the vague nature of English language, proves to give accurate results. The patterns discovered in sentences are listed in a defined order throughout the paper. The usage of Natural Language Toolkit and Posing tagging simplifies the most crucial task of tagging a word with its part of speech.

Semantic Web Mining by Combining Convolutional Neural Network and LSTM for Knowledge Discovery in Stock Market

Semantic Web Mining by Combining Convolutional Neural Network and LSTM for Knowledge Discovery in Stock Market

ADnEV

Schema matching is a process that serves in integrating structured and semi-structured data. Being a handy tool in multiple contemporary business and commerce applications, it has been investigated in the fields of databases, AI, Semantic Web, and data mining for many years. The core challenge still remains the ability to create quality algorithmic matchers, automatic tools for identifying correspondences among data concepts ( e.g. , database attributes). In this work, we offer a novel post processing step to schema matching that improves the final matching outcome without human intervention. We present a new mechanism, similarity matrix adjustment , to calibrate a matching result and propose an algorithm (dubbed ADnEV) that manipulates, using deep neural networks, similarity matrices, created by state-of-the-art algorithmic matchers. ADnEV learns two models that iteratively adjust and evaluate the original similarity matrix. We empirically demonstrate the effectiveness of the proposed algorithmic solution for improving matching results, using real-world benchmark ontology and schema sets. We show that ADnEV can generalize into new domains without the need to learn the domain terminology, thus allowing cross-domain learning. We also show ADnEV to be a powerful tool in handling schemata which matching is particularly challenging. Finally, we show the benefit of using ADnEV in a related integration task of ontology alignment.

An Enhancement of Svm Based Semantically Enriched Variable Length Confidence Pruned Markov Chain Model Based Web Page Recommendation System

Semantic Variable Length Markov Chain Length Model (SVLMC) is a web page recommendation system which combined the fields of semantic web and web usage mining by the Markov transition probability matrix with rich semantic information extracted from web pages. Though it has high prediction accuracy, it has problem of high state space complexity. The high space complexity reduce the execution speed and reduce the performance of the system, which was resolved by Semantic Variable Length confidence pruned Markov Chain Model (SVLCPMC) model that provides high user satisfied recommendation and Confidence Pruned Markov Model (CPMM). The time consumption of CPMM was reduced by Support Vector Machine (SVM). But still the recommendation accuracy is still below the user satisfaction. So in this paper, quickest change detection using Kullback-Leibler Divergence method is introduced to improve the accuracy of recommendation generation by developing a scalable quickest change detection schemes that can be implemented recursively in a more complicated scenario of Markov model and it is included in the training data of SVM. Then the performance of web page recommendation is improved by ranking the web pages using page ranking technique. Thus the performance of web page recommendation generation system has been improved. The experiments are conducted to prove the effectiveness of the proposed work in terms of prediction accuracy, precision, recall, F1-measure, coverage and R measure.

Effective information retrieval and feature minimization technique for semantic web data

The Internet contains both structured and unstructured data. The enormous flow of Internet data creates challenges in relation to effective information retrieval. Semantic Web Mining explores Web addresses using ontological and semantic structures. For effective information retrieval in Web Mining and Text Mining, text feature extraction plays an important role. The effectiveness of the text processing is determined by the complexity and dimensionality reduction of the feature vector. In this paper, a new approach is proposed based on the semantic structure of the Web data. It combines both feature extraction and feature selection techniques for data mapping and retrieval, involving standard features for effective text mapping. This process reduces the dimension complexity in the feature vector for effective information retrieval.

Semantic Web Mining in Retail Management System using ANN

Now a day, online shopping is being one of the most common things in the daily lives. To satisfy the customers’ requirements knowing the consumer behaviour and interests are more important in the e-commerce environment. Generally, the user behaviour information's are stored on the website server. Data mining approaches are widely preferred for the analysis of user's behaviour. But, the static characterization and sequence of actions are not considered in conventional techniques. In the retail management system, this type of considerations is essential. Based on these considerations, this paper gives detail review about a Semantic web mining based Artificial Neural Network (ANN) for the retail management system. For this review, many sentimental analysis and prediction techniques are observed and compared based on their performance. This survey also focused the dynamic data on the user behaviour. Furthermore, the future direction in big data analytics field is also discussed.

Building Intelligent Semantic Educational System (ISES) Based on Ontology and Semantic Web Mining

Smart cities are new and evolving concept based on innovative information and communication technologies. Thinks and Internet of Thinks (IoT) must be core technologies in smart cities. Thinks’ services and maintenance must be managed through digital planes. Location based service (LBS) are service targeted to a wide range of users. The increasing number of LBS will have a bad impact on transitions. One of the objectives of building smart cities is to reduce the traffic congestion and transportation demand. This paper utilizes Geographic Information System (GIS) and spatial analysis techniques for decision support of LBS. the objective is the advance determination of the best service location for covering services requirement based on three factors: location, time and type of service. Education is the greatest service required for humanity. This paper focuses on considering service factors and client requirements by applying a developed GIS platform to inform service provider by the assets required to cover customer needs and meets smart cities goals. While 1st stage of primary schools accepts students in age of 6 years, the suggested platform determines schools’ plane for 5 years in advance, according to: student location, date of birth, and education type requirement. The suggested platform was applied on Mansoura city, Egypt. That platform was designed by: using geoprocessing tools, and Python programming language of GIS (arcPy) for implementing functions and procedures (“split by attribute”, “periodic service plane”). Geocoding processing is used for converting students’ addresses to spatial location, which is necessary for geo-relational analysis.

Building Intelligent Semantic Educational System (ISES) Based on Ontology and Semantic Web Mining.

Building Intelligent Semantic Educational System (ISES) Based on Ontology and Semantic Web Mining.

Building Intelligent Semantic Educational System (ISES) Based on Ontology and Semantic Web Mining

This research presents a method to build an intelligent semantic education system based on two novel techniques namely ontology and semantic web mining. Ontologies are a powerful mechanism to solve a retrieval problem that is exacerbated rapidly within the modern educational systems, when there are individual differences between instructors and learners in the previous backgrounds and levels of knowledge. In intelligent education systems, four types of ontology are identified that represent knowledge: students, content, education, and structure. There is a promising approach for satisfying the intelligent education systems requirements, which is one of the most prominent semantic Web ideas, based on commonly shared meaning (ontology) and metadata processed automatically. This approach can be supported using semantic querying and conceptual navigation for learning materials.One of the important results of this paper is the depth of most of the inferences derived from learning ontologies. Relations such as has_Part and is_Part_Of and their interplay can be easy dealing by the users. Therefore, this type of thinking can be presented through an automatic mechanism, and semantic information is necessary for reasoning about an automatic occurrence.

T Structured Semantic Weight Relationship Algorithm Combined with Decision Trees for Data Extraction

In Semantic Web Mining extracting the relevant data from web is the main objective. With the increase in the complexity of the data involved the focus is to achieve better accuracy by extracting the required information. In Semantic web mining the primary input is the user query but the accuracy is based on the domain classification of the data extracted. To achieve accuracy higher a new approach has been proposed where the algorithm same name as article titled maintains a Semantic structure combined with the decision trees. From the training set each word has been tokenized and the relationship between them has been established involving cosine similarity weight as well as to the possibility terms of the same word. Cosine Similarity is calculated not only between the words but also established between the group of the training sentences. The paper explains in detail regarding grouping of training sentences and establishing the weight between them using our proposed approach.

Survey on Web Mining Techniques and Challenges of E-commerce in Online Social Networks

Objectives: In recent years, we have tremendous growth of users in Online Social Networks (OSN) such as Facebook, Google+, twitter etc. This becomes major reason for enabling web as largest market defining E-Commerce. Many companies use OSN as their sales channel as they can reduce operating cost for managing orders significantly compared with traditional channels. Also viral marketing is very popular in OSN. But the major drawback of this channel is if the company doesn’t satisfy the customer, then the same OSN can rapidly propagate a bad reputation of the company affecting the company’s business. Hence for customers it has become very important to identify and filter dishonest recommenders. So it becomes very important to recommend right items to right customers. In this survey, we aim to give a comprehensive review of research related to E-Commerce in OSN. Methods: First, we discuss web usage mining techniques for better shopping websites to satisfy customers. Also we discuss web mining techniques to find dishonest recommenders in OSN. Findings/ Improvements: Our survey explores the existing research highlights and also presents various researches in these topics. Also, we propose recommendation system which uses Semantic Web Mining process integrated with domain ontology which can be used to extract interesting patterns from, complex and heterogeneous data.

Semantic Web Mining in E-Commerce Websites

Semantic web is gaining interest of lot of people due to its great power. Semantic web makes the data understandable for machines. It enables machines to interpret the data published by the web in machine interpretable form. Though lots of businesses are moving towards Ecommerce websites, but there are few problems with the current ecommerce systems due to the lack of proper standards. Daily thousands of people search for the product they want to buy and due to the inefficiency of the systems it wastes lot of time and resource of the users. Semantic web has that power to overcome those problems and it can accelerate the business to further higher level where ecommerce websites are playing very important role. This paper gives the introduction of semantic web mining and its techniques and then problems of the current ecommerce systems and how those problems can be avoided using semantic web. It discusses the detailed semantic web ecommerce architecture.

Mining Semantic Web Data Using K-means Clustering Algorithm

The combination between semantic web and web mining is known as semantic web mining. Semantic web can improve the effectiveness of web mining. The knowledge of semantic web data can be mined using web mining techniques, as semantic web data are rich sources of knowledge to feed data mining techniques. This paper concentrated on how to combine two emergency research areas, namely semantic web and web mining. Firstly, we extract data from RDF file using SPARQL as query language. After that, we are going to cluster the entities of semantic web. One of the techniques is k-means clustering algorithm. Sematic web is about the meaning of the web data and to make machine understandable about it. Moreover, web mining is to extract and discover useful and previously unknown information from web data. This research gives an overview of where semantic web and web mining areas meet today, and how it is useful to combine these two well-known areas to obtain better and more accurate results.

A Novel Approach towards Integration of Semantic Web Mining with Link Analysis to Improve the Effectiveness of the Personalized Web

During the past few years World Wide Web has become a main source of information acquisition. The existence of such abundance of information, in combination with the dynamic and heterogeneous nature of the web, makes web site exploration a difficult process for the user. Websites personalization is the effective way to meet the requirement of efficient web navigation. This paper proposed novel technique that uses the content semantics and the structural properties of a web site in order to improve the effectiveness of web personalization. This paper presents a personalization framework CUMPW (Content & Web Usage Mining for Personalized Web) that integrates web content and web usage data with the user’s navigational patterns and represents the correlation between contents and the usage of the website. In the second part of proposed method, this paper presents a novel approach for enhancing the quality of recommendations based on the underlying structure of a web site. This paper proposed Navigational PageRank (NPR) Algorithm that suggests link analysis in effective manner for web personalization. NPR is applied to navigational graph of user session in order to determine the importance of a web page. The proposed hybrid (CUMPW + NPR) framework provides more representative predictions results than existing techniques that rely solely on usage data.