/pdf/advances-in-clustering-based-on-inter-cluster-mapping-27def0rvf0.pdf

Advances in clustering based on inter-cluster mapping

The information has evolved rapidly over the World Wide Web in the past few years. To satisfy information needs, users mostly submit a query via traditional search engines, which retrieve results on the basis of keyword matching principle. However, a keyword-based search cannot recognize the meanings of keywords and the semantic relationship among the terms in the user’s query; thus, this technique cannot retrieve satisfactory results. The expansion of an initial query with relevant meaningful terms can solve this issue and enhance information retrieval. Generally, query expansion methods consider concepts that are semantically related to query terms within the ontology as candidates in expanding the initial query. An analysis of the correct sense of query terms, rather than only considering semantic relations, is necessary to overcome language ambiguity problems. In this work, we proposed a query expansion framework on the basis of query sense analysis and semantics mining using computer science domain ontology, followed by working prototype of the system. The experts analyzed the results of system prototype over test dataset and Web data, and found a remarkable improvement in the overall search performance. Furthermore, the proposed framework demonstrated better mean average precision and recall values than the baseline method.

/pdf/sensual-semantic-analysis-for-effective-query-expansion-55z01hkl0l.pdf

Sensual Semantic Analysis for Effective Query Expansion

In today's worldwide of e-business marketing, it is important to know about search engine. Search Engine Marketing (SEM) is to use to gain visibility of the website based on the keywords. Consumer often interested in the top list of search results, therefore it is necessary that the webpage can be ranked at the top. Search Engine Optimization (SEO) tends to use varies type of techniques such as White Hat search engine optimization and Black Hat search engine optimization The purpose of the research is to analyze, design and develop a domain website using White Hat SEO techniques. White Hat SEO techniques uses the collected data including the website URL, some keywords or keyword groups that define the content of the web site. The website published using a chosen platform verified by Google Search Console and tested in Google Search engine. The website achieving high ranking results in Google Search which is in the top three of the first page in Google search. The result obtained shows that the White Hat SEO technique was correctly implemented.

Implementing white hat search engine technique in e-business website

Ontology engineering is an important aspect of semantic web vision to attain the meaningful representation of data. Although various techniques exist for the creation of ontology, most of the methods involve the number of complex phases, scenario-dependent ontology development, and poor validation of ontology. This research work presents a lightweight approach to build domain ontology using Entity Relationship (ER) model. Firstly, a detailed analysis of intended domain is performed to develop the ER model. In the next phase, ER to ontology (EROnt) conversion rules are outlined, and finally the system prototype is developed to construct the ontology. The proposed approach investigates the domain of information technology curriculum for the successful interpretation of concepts, attributes, relationships of concepts and constraints among the concepts of the ontology. The experts’ evaluation of accurate identification of ontology vocabulary shows that the method performed well on curriculum data with 95.75% average precision and 90.75% average recall.

/pdf/a-methodology-for-engineering-domain-ontology-using-entity-3bjpcbnbu6.pdf

A Methodology for Engineering Domain Ontology using Entity Relationship Model

In healthcare, organisational changes are suggested and implemented routinely as an answer to increasing patient demands and financial restrictions. Computer simulations offer a way to test these n ...

Specifying an ontology framework to model processes in hospitals

Traditional information retrieval systems retrieve documents based on keyword based matching, thereby incapable to provide results according to user information needs. These systems do not consider the semantic relationships among the user query keywords, thus cannot interpret the query context accurately. Query Expansion technique enhances the performance of information retrieval process by expanding user query with relevant meaningful concepts. Ontology represents semantics of a domain, and emerges as an important resource for query expansion. In this paper, we propose a query expansion framework based on conceptual knowledge derived from ontology in the domain of computer science. The system prototype results show good average precision percentage of 89.2% over keyword based search.

Query Expansion Using Conceptual Knowledge in Computer Science

Expert System in identifying herbs, using Production Rules in the system will solve a problem of using conventional and manual procedures. By using physicals structure, defining herbs is tough. Identifying herbs by using collective intelligence from the public can lead to issues on trustworthiness. Rule based is derived from the rules for herbs expert knowledge domain. Thus, the system can learn from its experience by using factual knowledge based. The system has the ability to give the herb’s family name, scientific name and common name. Therefore, this system will help public to identify herbs easier. The result yields 75% of system outputs are properly matched to human knowledge. This system also can be implemented in others knowledge domain. In fact, the system can provide a list of herb benefits focusing herbs in Malaysia.

i-Herbs: An Expert System for Malaysian Herbs Identification Using Production Rules Approach

With the increased interest into data mining as an important tool for data processing and analysis, the researchers are concerned into data mining for real decision making, data mining helps in the organizational decision making, inaccurate information can mislead decisionmakers and cause costly errors. With more data collected for analytical purposes. Techniques data mining through Weka Explorer interface and experimental environment interface into determining the prediction and accuracy using different algorithm ratings to know the performance of best. Study confirm is to categorize data and help users mining useful data and easily identify an appropriate algorithm for an accurate predictive model, to access the best-performing algorithms, minimize errors and minimum time to build models through the Explorer interface and Experimental Environment Interface to get accurate.

Roslina Abd Hamid

Papers

A Methodology for Engineering Domain Ontology using Entity Relationship Model

Query Expansion Using Conceptual Knowledge in Computer Science

i-Herbs: An Expert System for Malaysian Herbs Identification Using Production Rules Approach

Comparison of Statistic Prediction Results in Weka Explorer Interface and Experimenter Environment Interface on Dataset