Top PDF TermGenie – a web-application for pattern-based ontology class generation

TermGenie – a web-application for pattern-based ontology class generation

TermGenie – a web-application for pattern-based ontology class generation

Creating new classes in ontologies is a common task, one that is typically done by a developer using an Ontology Development Tool (ODT) such as OBO-Edit [1] or Pro- tégé [35]. These are both comprehensive, general purpose environments, and are not intended for use by annotators and biocurators without requisite training. In addition, both are desktop applications, requiring an installation on the user’s machine. The limitations of desktop ontology development software, especially for collaborative work, led to the creation of WebProtégé, a web-based ontology development tool [36]. All three applications are pow- erful tools with steep learning curves and are usually intended for knowledge/ontology engineers and ontology developers. They do not offer the separation of design and quick everyday use for non-experts. TermGenie is not intended to replace comprehensive ODTs; the pattern- based approach and ODTs complement each other in the ontology development workflow. In fact the comprehen- sive ODTs are required during the template design and testing.
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A Novel Approach to Analyse User Satisfaction Level on Web pages using Ontologies

A Novel Approach to Analyse User Satisfaction Level on Web pages using Ontologies

patterns of web site usage and the features of user’s behavior. The proposed method constructs sessions as a Directed Acyclic Graph which contains pages with calculated weights. This will help site administrators to find the interesting pages for users and to redesign their web pages. After Session Construction a web usage analysis is used for finding the correlation between consumer emotions and buying behaviors. A semantic web usage mining technique is proposed for finding web access patterns from the annotated web usage logs. It includes consumer emotions and behaviors via self-reporting and behavioral tracking. To signify the real-time temporal concepts and requested resource attributes of periodic pattern based web access activities fuzzy logic is used. The consumer emotions and behaviors are integrated into a Personal Web Usage Lattice which represents the web access activities. From this we create Personal Web usage Ontology which facilitates semantic web applications. But the limitation is less efficient in terms of accuracy and user satisfaction level. So, in this manuscript an innovative technique is introduced which is called Optimum Session Interval based Particle Swarm Optimization(OSIPSO). This technique is used to find the optimum session interval. Additionally, an associative classification is used to enhance the level of accuracy. Associative classification is a combination of associative rule mining and classification rule mining. An experimental result shows that the proposed work achieves high accuracy and highly efficient in terms of user satisfaction level.
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Comparative GO: a web application for comparative gene ontology and gene ontology-based gene selection in bacteria

Comparative GO: a web application for comparative gene ontology and gene ontology-based gene selection in bacteria

The primary means of classifying new functions for genes and proteins relies on Gene Ontology (GO), which defines genes/ proteins using a controlled vocabulary in terms of their Molecular Function, Biological Process and Cellular Component. The challenge is to present this information to researchers to compare and discover patterns in multiple datasets using visually comprehensible and user-friendly statistical reports. Importantly, while there are many GO resources available for eukaryotes, there are none suitable for simultaneous, graphical and statistical comparison between multiple datasets. In addition, none of them supports comprehensive resources for bacteria. By using Streptococcus pneumoniae as a model, we identified and collected GO resources including genes, proteins, taxonomy and GO relationships from NCBI, UniProt and GO organisations. Then, we designed database tables in PostgreSQL database server and developed a Java application to extract data from source files and loaded into database automatically. We developed a PHP web application based on Model-View-Control architecture, used a specific data structure as well as current and novel algorithms to estimate GO graphs parameters. We designed different navigation and visualization methods on the graphs and integrated these into graphical reports. This tool is particularly significant when comparing GO groups between multiple samples (including those of pathogenic bacteria) from different sources simultaneously. Comparing GO protein distribution among up- or down- regulated genes from different samples can improve understanding of biological pathways, and mechanism(s) of infection. It can also aid in the discovery of genes associated with specific function(s) for investigation as a novel vaccine or therapeutic targets.
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SemAware: An Ontology-Based Web Recommendation System

SemAware: An Ontology-Based Web Recommendation System

While most sequential pattern mining algorithms use the item support as the main inter- estingness measure for pruning out candidate sequences, the size of the potential frequent pattern is being ignored. It is believed that the size of a frequent pattern has an impor- tant effect on the decision maker and the generation of association rules more than mere frequency (i.e., support) of the pattern. For example, one can set the minimum support (min sup) at a certain value but get results of only frequent 1-sequences, which has no contribution to association rules and minimal effect on decision making, especially in ap- plications where frequent 1-sequences are not really interesting. As a matter of fact, we have noticed during our comparative experiments with sequential pattern mining algo- rithms, that in most cases a min sup more than 1% generates only frequent 1-sequences, if it does generate any. Geng and Hamilton [2006] have identified nine criteria that can be applied in three different ways to determine if a pattern is interesting or not (conciseness, coverage, reliability, peculiarity, diversity, novelty, surprisingness, utility, and actionabil- ity), of which we can list the size of the frequent pattern under utility and actionability, meaning that the pattern is of importance to the decision maker and it enables him/her to take further action. Aljandal [2009] discusses that itemset size is a propoerty which has not been involved directly in any interestingness measure, and he points out its importance in DNA replication applications. A direct application of frequent pattern size would be to use it as a limiting criteria during pattern mining and restrict the algorithm to find patterns of a certain minimum size. This can be applied in Pattern-Growth algorithms (like PrefixS- pan) by allowing a minimum size of the prefix/suffix from which growth will start. Some challenges still remain, like what is the maximum size allowed given the provided min sup value? What relation is there between min sup and the size of found frequent patterns? How will this affect time and space complexity of an algorithm? The development of a mining method to answer these questions and also relate frequent pattern size to the utility of the pattern is left for future work.
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Ontology Generation from Session Data for Web Personalization

Ontology Generation from Session Data for Web Personalization

The Limitation of our model is, to create User Interested Page Ontology for the new users in the website, because we will create UIPO from the web log information only. For the new users there is no web log data, for those cases it creates the UIPO based upon the user profile or creates the UIPO for the corresponding website without user’s interest. In future we will find solution for this problem. Ultimately the aim of our research is according to the discovered pattern to generate recommendations and improve the website Design. The results produced by our research can also provide guidelines for improving the design of web applications too. REFERENCES
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Design Based Assessment Data Processing System Web Using Framework Codeigniter

Design Based Assessment Data Processing System Web Using Framework Codeigniter

CodeIgniter's work structure starts from the browser that will interact via the controller. Then the controller will accept and reply to all requests from the browser. For data, the controller will ask for a model and for the UI or template controller it will ask to see. When the browser requests a web page, the router will find the controller that should handle the request. Later the controller will be used to access data and display models to display data. MVC is used to separate data access and business logic from data presentation and user interaction. Separation is done so that any changes in presentation logic or business logic do not affect each other very complex. The MVC separation solution is expected to increase application flexibility and reusability. MVC architecture separates applications into three parts, namely Model, View and Controller[1]:
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Online Full Text

Online Full Text

Basic gaming mechanics are incorporated to achieve this research objective. The incorporated basic gaming mechanics are; rewards, loss, and winnings. The lecturer asks a question to the class and a student voluntarily raised their hand as indication of willingness to answer the lecturer's question – the lecturer then affords the student with the opportunity to answer the question. If the student answers the question correctly, it is a win for the student. The lecturer then rewards the student with the appropriate point(s). However, if the students provide an incorrect answer to the question it is a loss for the student. Consequent to that other student are now allowed to raise their hands and attempt to gain points by providing the lecturer with a correct answer to the question put forth. Three layers have been designed to integrate the designed and developed for the construction of the gamified web- based system to increase student's participation in class. This section focuses on the design and development of the User Interface layer. Through the user Interface of the web- based system, a lecturer can be able to assign a point to a student and student's' can be able to see instant results of their points gained through participating.
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Web Based OOP Domain Ontology (WOOPDO)

Web Based OOP Domain Ontology (WOOPDO)

Various Definitions in knowledge engineering, natural languages processing and cooperative information systems, in addition to intelligent information integration, and knowledge management reports about the applications of ontologies in developing and using system in WWW.[8] In Computer Science Ontology is the formal characterization of a specific domain by defining the concepts of tags and their attributes and relationships between these concepts (relations). Concepts are usually organized according to a hierarchical relationship of globalization and localization between these concepts. In this hierarchical concept there is the offspring of the broader concept of it. For example: "student" is a "human" and "human" is an "Object" (the concept of "student" is the son of the concept "human). All concepts are implicitly members of the concept “Thing”.[10]
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OntoFox: web based support for ontology reuse

OntoFox: web based support for ontology reuse

Different OntoFox settings have been applied for gener- ating these 12 ontology subsets for VO imports (Table 2). In terms of superclass extraction, six were generated with the OntoFox setting "includeNoIntermediates", which is particularly useful when the intermediate superclasses do not generate much more information needed for the tar- get ontology. The setting "includeComputedIntermedi- ates" was used for extracting ontology terms from three external resources, including NCBITaxon, PATO, and PRO [32]. In the case of the NCBI taxonomy it reduces the number of imported classes without losing the infor- mation of the most recent ancestor superclasses (Figure 5). Finally, the setting "includeAllIntermediates" has been used for extraction from OBI, ro_proposed http:// purl.org/obo/owl/ro_proposed, and the Sequence Ontol- ogy (SO; http://www.sequenceontology.org/) (Table 2). These three external ontologies are closely related to VO, and we would like to use all original hierarchies for those terms imported to VO.
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An Ontology Framework based on Web Usage Mining

An Ontology Framework based on Web Usage Mining

Extracting Ontologies- In the ontology extraction phase of the ontology learning process, major parts, i.e. the complete ontology or large chunks reflecting a new subdomain of the ontology, are modeled with learning support exploiting various types of (Web) sources. Thereby, ontology learning techniques partially rely on given ontology parts. Thus, we here encounter an iterative model where previous revisions through the ontology learning cycle may propel subsequent ones and more sophisticated algorithms may work on structures proposed by more straightforward ones before. Describing this phase, we sketch some of the techniques and algorithms that have been embedded in our framework and implemented in our ontology learning environment .
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THE THROUGHPUT FOR MULTI HOP RELAY WIRELESS SENSOR NETWORKS BASED ON COOPERATIVE 
DIVERSITY

THE THROUGHPUT FOR MULTI HOP RELAY WIRELESS SENSOR NETWORKS BASED ON COOPERATIVE DIVERSITY

traditional development process. In the above section, the performance is compared and found that the code and unit test time could be minimized significantly for an application in the development phase. The proposed framework is well suited in two tier and three tier based applications where each tier, the proposed framework could be configured. The data security concerns are taken care of in this proposed approach as this framework does not require a persistent storage base e.g. file system, database etc, for caching the data objects. As we have seen in the section 3, there is no additional infrastructure required to establish this frame work. So this approach could be configured perfectly where the data server is located remote location and the application is very slow in its development phase, when it is expected to be developed in other geographical location.
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Frameworks for SQL Retrieval on Web Application Security

Frameworks for SQL Retrieval on Web Application Security

Therefore, the existing network security techniques are completely inadequate to defend the web application attacks. This paper proposes a P-SQLIAD (Pattern based SQL Injection Attack Detection for detecting and preventing SQL injection attack detection. The P-SQLIAD uses pattern based approach to detect illegal queries before they are executed on the database. Also, the detection time is faster than the existing web application security and reduces the positive false rates using pattern of attacks.

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Web-based Application for Visual Representation of Learners' Problem-Posing Learning Pattern

Web-based Application for Visual Representation of Learners' Problem-Posing Learning Pattern

General results of Monsakun’s performance has been researched before, such as an analysis regarding students’ performance in forming a story-based question on a discussion related to the number of questions formulated by the students [6]. Another research has also been done to analyze its learning effect in a pretest and posttest approach [10]. Further research has also been done to understand students’ learning and thinking process using Binomial test based on students’ first sentence choice while using the application [11], result shows that first sentence selection changes based on students’ experience. In term of formulation-of-problem process when using the application, result shows an improving effect on how the students pose the problem with higher validity [12] while also avoiding as many mistakes as possible [13]. Although extensive researches have been done to analyze the log data from Monsakun, a dedicated software to explore and monitor the learning activity has not been created yet. Furthermore, data visualization of such data also has not been explored extensively, albeit how data visualization has always been a strong approach to interpret such data in which the result can be used to improve cognition regarding the data [14]. The visualization approach can be combined with data mining, allowing a more thorough and effective exploration towards more obscure and hidden aspect of the data as well as its implication [15].
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Test Suite Generation of t- way CIT with          constraints for Web Based Application

Test Suite Generation of t- way CIT with constraints for Web Based Application

A desired requirement of combinatorial interaction testing strategy is the ability to deal with complex constraints [7,8]. Although the presence of constraints reduces the size of combinatorial test suites it also makes test generation more challenging. The general problem of finding minimal test suite that satisfies t-wise interaction coverage is NP complete. If constraints are added on the input domain finding a single test suite that satisfies t-wise interaction coverage is NP complete.

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SURVEY ON TEXT CLASSIFICATION TECHNIQUES/ALGORITHMS

SURVEY ON TEXT CLASSIFICATION TECHNIQUES/ALGORITHMS

In data mining, Machine learning is often used for Prediction or Classification. Prediction means extracting information from data and using it to predict future trends and behavior patterns. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting it to predict future outcomes. Classification involves finding rule that partition the data into disjoint groups. The input for the classification is the training data set, whose class labels are already known. Classifications analyze the training data set and construct a model based on the class label. The goal of classification is to build a set of models that can correctly predict the class of the different objects.
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Building a family ontology to meet consistency criteria

Building a family ontology to meet consistency criteria

Semantic Web Reasoning on Semantic Web Description Logic Rules 2.2.3.1 SWRL Rules Ontology Ontology Languages Ontology Evaluation Criteria 2.3.2.1 Consistency 2.3.2.2 Completeness Ontolo[r]

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Ontology-based specification and generation of search queries for post-market surveillance

Ontology-based specification and generation of search queries for post-market surveillance

AU developed the search ontology method for modelling and generation of search queries, designed and implemented SON and SONG. SK made substantial contributions to the design and implementation of SON and SONG, analyzed the recent related works. HH was responsible for project management, conception and semantic foundation of developed ontologies. TW, SM, KG, SR, MOS, acting as domain experts, set up the content design of the SON, created the evaluation plan and conducted the evaluation. The main contribution of WB was the designing and implementing the Corpus Builder with the Prospector component including the integration of the ontology to control developed components. CG and PB created an Elasticsearch plugin providing advanced functionalities such as additional search modes and NEAR operators. RS, CB, MK, KP and WL implemented a risk classification on the basis of the FDA coding system and performed the evaluation of the system from the regulatory perspective of the German Federal Institute for Drugs and Medical Devices (BfArM). JUM and MW contributed to the design of the overall system. All authors read and approved the final manuscript.
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Ontology Based Searching Techniques in Web Environment

Ontology Based Searching Techniques in Web Environment

vi. Rules: Rules Interchange Framework (RIF) is added in the third version of Semantic architecture’s layer. RIF is made as a draft specification for a rule language under RDF schema and ontology as its base. Semantic Web Rule Language (SWRL) is initiated by W3C as a Rule language is required above Ontology.

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A Comparative Study on Web Crawling for
          searching Hidden Web

A Comparative Study on Web Crawling for searching Hidden Web

Figure 2 show the architecture of a traditional web crawler. The working of this web crawler starts with the Frontier module where this module gets as a input URLs from a set of seed URLs. The seed URLs are passed to the next module which is fetcher. The fetcher module retrieves the contents of the web pages which is associated with the unique URLs form the www. These contents are passed to the URL Extractor. The URL extractor parses the HTML pages and extracts new links from them. New HTML links are passed to the HTML-page filler module and the database. Database interacts with the data files and stores the new links. HTML-page filler filters URL that is not interesting to the web crawler. After that the URL passed to the URL-seen module. The working of this module is to finds the new URLs that are not retrieved and passed them to the Fetcher for retrieval. This looping is continues until all the reachable related links are visited.
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ONTOLOGY BASED ADAPTIVE WEB SEARCHING

ONTOLOGY BASED ADAPTIVE WEB SEARCHING

Contextual ontology helps in the knowledge-full indexing of documents, providing semantic structure to the document. The advantage of such model is sharing common understanding of the structure of context information among users, devices and services to enable semantic interoperability. It also enables reuse of domain knowledge, i.e., building a large ontology by integrating several ontologies describing portions of the larger domain. Also, it enables formal analysis of domain knowledge, for example, context reasoning becomes possible by clearly defining context ontology.
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