Construction and Application of Fuzzy Ontology

World Wide Web (WWW) is a platform that explores a wide range of information used for the development of web applications. Some examples of these applications include social network analysis, personalized item recommendations, and web page classification and ranking. Among these applications, search engines and web page ranking are particularly important as they consistently index and store billions of web pages on the internet. The main objective of this paper is to create an innovative framework for the classification and re-ranking of web pages using intelligent techniques. The framework is structured into two key phases: classification and re-ranking-based retrieval. In the initial classification phase, a series of pre-processing steps are implemented, including the elimination of HTML tags, punctuation, stop words, and the application of stemming. After pre-processing, a word-to-vector conversion is performed, followed by feature extraction utilizing Principal Component Analysis (PCA). This sequence of actions leads to optimal feature selection, which is vital for the precise classification of web pages. Given the multitude of features present in web pages that can compromise classification accuracy, this study employs a novel meta-heuristic algorithm, the Opposition Based-Tunicate Swarm Algorithm (O-TSA), to facilitate optimal feature selection. The refined features are subsequently processed through the Enhanced Convolutional-Recurrent Neural Network (E-CRNN), enhanced by O-TSA, resulting in the effective classification of diverse web page categories. In the second phase, the re-ranking process is executed using O-TSA, which establishes the objective function based on a similarity function (correlation) for URL matching, leading to optimal re-ranking of web.

Construction of fuzzy ontologies from fuzzy XML models

Web Crawlers ‘browse’ the World Wide Web (WWW) on behalf of search engine, to collect web pages from numerous collections of billions of documents. Metacrawler is similar to that of a meta search engine that combines the top web search results from popular search engines. World Wide Web is growing rapidly. This possesses great challenges to general purpose crawlers. This paper introduces an architectural framework of a Metacrawler. This crawler enables the user to retrieve information that is relevant to the topic from more than one traditional web search engines. The crawler works in such a way that it fetches only the pages that are relevant to the topic. The PageRank algorithm is often used in ranking web pages. But, the ranking causes the problem of topic-drift. So, modified PageRank algorithm is used to rank the retrieved web pages in such a way that it reduces this problem. The clustering method is used to combine the search results so that the user can easily select web pages from the clustered results based upon the requirement. Experimental results show the effectiveness of the Metacrawler.

People are using social media to share their opinions and thoughts about transportation. Sentiment analysis can study these opinions and emotions for the evaluation and improvement of transportation features and services. However, the transportation-related data on social media are unstructured, short length and with a lot of dynamic topics. In addition, the existing systems are discovering sentiments at sentence or document level. These systems are inefficient to extract relevant features, identify polarity orientation of features, and classify the sentiment of features. Therefore, we present a new approach of sentiment analysis for feature extraction and polarity classification. The proposed system is based on fuzzy ontology that presents the relations between concepts semantically in the domain of transportation. The semantic knowledge is employed to identify features in document. The polarity of these features is computed by assigning their opinionated words in document into SentiWordNet. We use logistic regression and multi-layer perceptron along with fuzzy ontology. The experimental results show that fuzzy ontology with learning algorithm is more effective than classifiers without ontology.

Ontology learning has become a popular research field recently. However, the typical ontology may not be sufficient to represent uncertainty information. Fuzzy ontology is proposed to solve the uncertainty reasoning problems. But the construction of fuzzy ontology is still a tedious and painstaking task. The cognitive model of fuzzy ontology learning is an automatic model of fuzzy ontology construction that simulates the process of human being recognizing the world. Induction is an important step of the model. In this paper, we present a strategy for the fuzzy ontology induction in the cognitive model of ontology learning which includes some generalization principles and the corresponding induction operators. It generates induction hypothesis through a series of operations. As a result, induction hypothesis are generated from the present ontology and it can generalize the existing ontology.

<span lang="EN-US">Search engine optimization (SEO) is an important internet marketing strategy and process that facilitates maximizing an intended website’s visibility with search engine results. It is widely employed nowadays to improve traffic volume or quality from search engines to a particular website. Even though a significant number of publications imply the essential aspects of SEO, only a few provide generalized ideas to deal with the complex structure of the web. Also, the critical issues of content quality, site popularity, keyword density, and publicity factors were not much considered in the traditional ranking algorithms during SEO processes. This has negatively influenced the retrieval rate in the existing SEO techniques, and consequently, inadequate search results were obtained through search engines. Hence, the study considers web page ranking as a theoretical basis for the research and addresses these limitations in the existing system. It further improves SEO performance by introducing a unique web-page ranking strategic design to gain higher page rank results. The results of the investigational study show that the proposed system effectively contributes towards SEO with an improved page ranking strategy and also provides higher accuracy in calculating the importance score of web pages which is comparable with popular ranking algorithms such as hyperlink-induced topic search (HITS) and PageRank.</span>

The interest of this paper is focused on the detection of some potential contradictions in fuzzy ontology. A high level net approach integrated with uncertainty inference is proposed for this purpose. It makes use of a State Controlled Coloured Petri Net (SCCPN), which has been proposed for ontology representation and verification in our previous work. In this paper, the SCCPN model designed to handle the case of typical ontology with crisp logic is extended and modified to model the fuzzy ontology. Uncertainty inference is integrated in the net to deal with the imprecise or vague information. This work presents a formal definition of the extended SCCPN for modeling fuzzy ontologies and the knowledge inference with uncertainty as well as formulating the formal verification of some potential contradictions in fuzzy ontology.

One of the key components which ensures the acceptance of web search service is the web page ranker - a component which is said to have been the main contributing factor to the early successes of Google. It is well established that a machine learning method such as the Graph Neural Network (GNN) is able to learn and estimate Google's page ranking algorithm. This paper shows that the GNN can successfully learn many other Web page ranking methods e.g. TrustRank, HITS and OPIC. Experimental results show that GNN may be suitable to learn any arbitrary web page ranking scheme, and hence, may be more flexible than any other existing web page ranking scheme. The significance of this observation lies in the fact that it is possible to learn ranking schemes for which no algorithmic solution exists or is known.

The Web page rank algorithm is always regarded as the core of the search engine. Firstly, this article analyzes the traditional and classical rank algorithms briefly. Then, it proposes a new rank algorithm, which is called A-PageRank. In this algorithm, the PageRank value of the source page is distributed to its link-out pages according to the topic similarity. Lastly, a new method which uses both the similarity and divergence to weigh the match degree between one web page and one user query is adopted in order to increase the precision and recall rate of the search engine.

One of the main expectations of Web surfers is the inclusion of accurate and fresh result set in search engines' outcomes. Moreover, regarding the limited time and patience of nowadays Web users, they need to be advised on the significance of the Web domain they entered. Due to the exponentially growing and dynamic nature of the World Wide Web, considering these demands for Web crawlers with the existing Web page ranking and importance calculation methods are unachievable, expensive or time-consuming. Therefore, proposing novel heuristics in order to empower the search answer set is the concern of many researchers in this area. In this paper, we employ our previously proposed novel Web page importance metric of LogRank in order to calculate Web site significance value and to include the newly uploaded or infant Web pages into search result set to meet the mentioned expectations of Web surfers. We append our experiment on above matters through analyzing UTM University server log file and over UTM University Web domain.

Liver fibrosis diagnoses is a critical and core research study field due to its importance to the patient's life. Moreover, electronic health records (EHR) contain wealthy semantics connected to liver diseases yet ontological implementation is still a challenge. Ontology however, can play critical roles in E-health as a formalization of medical terminologies and decision support system knowledge base. But since clinical data contains a lot of data that is imprecise and vague, classical approaches of ontology construction would not be fruitful. However, Fuzzy ontology, an extension of the crisp ontology that requires different development methodology, can be implemented in this field due to its previous success in modeling semantic knowledge in various domains. In this article, the authors construct a fuzzy ontology by using a fuzzy extended entity relationship (EER) data model for liver fibrosis diagnosis. The resulting ontology is complete and consistent because it is based on a formal methodology of mapping the EER model into a fuzzy ontology.

Extrapolated splitting methods for multilinear PageRank computations

New learning researches proved that creativity is an essential concern in the arena of education. The best means to evaluate learning outcomes and students’ creativity is essay questions. However, to evaluate these questions is a time-consuming task and subjectivity in scoring assessments remains inevitable. Automated essay evaluation systems (AEE) provide a cost-effective and consistent alternative to human marking. Therefore, numerous automatic essay-grading systems have been developed to lessen the demands of manual essay grading. However, these systems concentrate on syntax and vocabulary, and no consideration is paid to the semantic and coherence of the essay. Moreover, few of the existing systems are able to give informative feedback that is based on extensive domain knowledge to students. In this paper, a system is evolved that uses latent semantic analysis (LSA) and fuzzy ontology to evaluate essays, where LSA will be responsible for checking the semantic. Fuzzy ontology is used to check the consistency and coherence of the essay as it is the best way to overcome the vagueness of the language, and the system will also provide a score with feedback to the student. Experimental results were good in evaluating the essay syntactically and semantically.

Fuzzy ontology is based on the concept that each index object is related to every other object in the ontology, with a degree of membership assigned to that relationship based on fuzzy set theory. This paper proposes use cases based on the related process of the terrorism event extraction using fuzzy ontology, especially the terrorism fuzzy ontology construction methodology. The related use cases are represented using the Web Ontology Language (OWL) which is designed to support the representation ontology relation. Additionally, to make the proposed use cases more relevant to implementing the system, the paper presents linguistic variables which serve as a mean of approximate characterization of fuzzy phenomena concepts and also the appropriate characterization of the terrorism fuzzy relations in their properties.

The focused crawler of a special-purpose search engine aims to selectively seek out pages that are relevant to a pre-defined set of topics, rather than to exploit all regions of the Web. The PageRank algorithm is often used in ranking web pages, and it is also used in URL ordering for focused crawler. It estimates the page's authority by taking into account the link structure of the Web. However, it assigns each outlink the same weight and is independent of topics, resulting in topic-drift. In this paper, we propose an improved PageRank algorithm, which we called To-PageRank, and then we present a crawling strategy using the To-PageRank algorithm combining with the topic similarity of the hyperlink metadata. The experiment in focused crawler shows that the new improved crawling strategy has better performance than the Breath-first and PageRank algorithms.