Sam Rizvi

Information Extraction Using Web Usage Mining, Web Scrapping and Semantic Annotation

Extracting useful information from the web is the most significant issue of concern for the realization of semantic web. This may be achieved by several ways among which Web Usage Mining, Web Scrapping and Semantic Annotation plays an important role. Web mining enables to find out the relevant results from the web and is used to extract meaningful information from the discovery patterns kept back in the servers. Web usage mining is a type of web mining which mines the information of access routes/manners of users visiting the web sites. Web scraping, another technique, is a process of extracting useful information from HTML pages which may be implemented using a scripting language known as Prolog Server Pages(PSP) based on Prolog. Third, Semantic annotation is a technique which makes it possible to add semantics and a formal structure to unstructured textual documents, an important aspect in semantic information extraction which may be performed by a tool known as KIM(Knowledge Information Management). In this paper, we revisit, explore and discuss some information extraction techniques on web like web usage mining, web scrapping and semantic annotation for a better or efficient information extraction on the web illustrated with examples.

Ontology Merging Using Prompt Plug-In of Protégé in Semantic Web

An integral component of the Semantic Web is the notion of an Ontology. Ontologies which are a means for conceptualizing and structuring knowledge play a key role in the realization of semantic web’s vision of incorporating the machineunderstandable data on the current human-readable web. Ontology Management involves various key issues like: Ontology creation or reuse, merging, matching or mapping etc. Due to the exceeding number and wide range of Ontologies day by day, there is a need of higher level of abstraction that enables information fusion across multiple Ontologies which is possible by merging various source Ontologies to form a new and larger Ontology which may replace the earlier ontologies. Ontologies that need to be merged may be similar in some aspects and different in others which is a major challenge for semantic web applications. Ontology merging refers to a process of taking two or more source ontologies and return a merged ontology based on the given source ontologies. There is a need to merge a set of all conceptual based information and physical merging at attribute level in all Ontologies without any redundancy and taking care of alignment and mapping factors or any other significant issues which may be time consuming or a challenging complex task. Various approaches, tools and techniques are being applied for this purpose. This paper highlights the merging process and it’s key issues and illustrates it with an example of merging two ontologies using prompt plug-in of protégé

Ontology and Web Usage Mining towards an Intelligent Web Focusing Web Logs

Today, Internet is a huge database which comprises of a large number of Web sites, search engines and other information. Due to the unstructured and semi structured data in the web pages, it is a challenging task for researchers to make a relevant and efficient search in warehouse of such type of database. Ontology may be a good mechanism for achieving this goal and Web Mining technique may be used to discover and extract meaningful or relevant information from the Web documents. In this paper, analysis of web usage mining has been made with the help of an example of sample data for which WebLog analyzer tool, “Web Log Expert” has been used and it has been appended with the development of an Ontology for an intelligent or efficient web and it’s relation with web usage mining. Finally, it also summarizes some other research challenges towards an intelligent machine and web environment.

An exhaustive study on data mining techniques in mining of Multimedia database

Multimedia database can be define as a collection of storage and retrieval systems, in which large amount of media objects are created, modified, searched and retrieved, where as Multimedia is the combination of text, image, graphics, animations, audio and video. The extension of database application to handle multimedia objects requires synchronization of multiple media data streams. Multimedia data mining refers to the extraction of implicit knowledge, data relationships, or other patterns which are not stored in multimedia files explicitly. The systems overall performance in retrieval can be increase by indexing and classification of multimedia data with efficient information fusion of the different modalities is mandatory. Apart from text retrieval, the current waves in web searching and multimedia data retrieval are the search for and delivery of 3D scenes, images, music and video. The content-based multimedia information retrieval provides new techniques and methods for searching various multimedia databases over the world.

Ontology Creation towards an Intelligent Web: Some Key Issues Revisited

44 Abstract —As we are aware that there is a need of extending the current web to an intelligent web which may result in meaningful or efficient retrieval of information on web. Sir Tim Berner’s Lee, the father of web, has proposed a layered architecture of such a web known as semantic web where Ontology layer is of prime significance. One of the primary goal of Semantic Web is to store data in distributed locations and to use ontologies to aggregate or use it. There is a need of global information sharing and establishment of an appropriate standard known as Ontology to define the conceptual level of a metalanguage,which is described as sharable conceptualization of a specific domain of interest in a machine-understandable format which is also the goal of semantic web. Now, Ontology has several issues among which Ontology creation is the first and the most fundamental and significant aspect. Ontology creation is abstract and has various key issues concerned. It may be created in several ways where creating an ontology using some ontology building tool/editor is one of the methodology. Protégé is one of the most widely used tool or editor for ontology creation. Sometimes, large team-engineered ontologies are not sufficient to illustrate semantic web’s full potential. There is a need of a specification for expressing personal and relationship information within the Semantic Web community. Using Semantic Web applications for social networks, automated aggregation of a user’s distributed social connections will give a better picture of their profile and improve the functioning of the applications. FOAF(Friend-Of-A-Friend) Ontology/vocabulary may be a good solution for it. There are millions of FOAF profiles online, hosted at a number of websites. The way it is used satisfies the goal of using an ontology to represent considerable amounts of distributed data in a standard form. In this paper, first, we revisit, discuss and analyse about Ontology creation and it’s various key aspects. Second , we illustrate an aspect of an Ontology creation using protégé 3.4 for the “University School of Information Technology(USIT)” of Indraprastha University, Delhi, India. Third, it also illustrates the query retrieval using query tab of protégé and TGviz tab for providing the route of the ontology with a graph to reach to any classes or subclasses. Finally,FOAF(Friend-ofA-Friend) Ontology has been revisited and highlighted illustrating a FOAF profile snippet generation using an online tool, Foaf-a-Matic.

Query Optimization: Issues and Challenges in Mining of Distributed Data

The technique of finding the optimal processing method to answer a query is called Query optimization, whereas a collection of various sites, distributed over a computer network is called Distributed Database. In Distributed Database, the site communicates with each other through networks. There are various issues arise during evaluation of query cost, among which the processing cost and a transmission cost are important. There are several algorithms developed to find the best possible solution for a particular query, but they all have their certain limitations. The optimizer is mainly concern on search space, search strategy, and the cost model. It primarily focuses on these three factors. The mining cost of a query depends on the order of evaluation of the operators, for the same query we can have different cost if the order is changed. Hence, to find the optimal cost for a particular query is emerging as an open challenge for many researchers. Therefore, the cost-based query optimization technique has emerged as an important concept for dealing with the query optimization. This paper explores the issues and challenges of query optimization in mining of distributed data.