Ontology based semantic search engine Design approach

Design of a semantic search engine based on the Ontology is one of the search engine design approach. this system framework is divided into four modules, and the overall framework is as follows:
1) Ontology processing module: mainly establish the ontology, encapsulate some operations on ontology, and offer interfaces of ontology operation to each part of the system.
2)Website collection module: collect website information in terms of the concrete crawling strategy, and store them in data base for subsequent operations.
3)Pre-processing module: analyze collected websites, mark the ontology of each website, and establish the inverted index on the marked metadata.
4)Query processing module: mainly provide users with interfaces for them to carry out queries and return results.

PageRank Algorithm

PageRank is a link analysis algorithm that assigns a numerical weighting to each element of a hyperlink set of documents, such as the WWW, with the purpose of "measuring" its relative importance within the set. The Algorithm may be applied to any collection of entities with reciprocal quotations and references. The numerical weight that it assigns to any given element E is also called the PageRank of E and denoted by PR(E). The name PageRank is a trademork of Google. This is the basic search algorithm which google use for search.

Conferences and workshps

As Semantic web is new domain of research there are lot of research work and conferences are going on now days. Most of AI and Knowledge base conferences consider semantic web as a topic of research paper presentation and publications. the further detail about the conferences can be gathered from IEEE, ACM and Semantic web conferences.
Latest confereces is: IEEE Conference on AI at Bangladesh. Fore more conference details visit
1. www.allconferences.com
2. www.conferencealert.com

Web Ontology Language (OWL)

The Web Ontology Language (OWL) is a family of knowledge representation languages for authoring ontologies, and is endorsed by the World Wide Web Consortium. This family of languages is based on two (largely, but not entirely, compatible) semantics: OWL DL and OWL Lite semantics are based on Description Logics, which have attractive and well understood computational properties, while OWL Full uses a novel semantic model intended to provide compatibility with RDF Schema.
The W3C-endorsed OWL specification includes the definition of three variants of OWL,
with different levels of expressiveness.
1. OWL Lite was originally intended to support those users primarily needing a classification
hierarchy and simple constraints. Development of OWL Lite tools has thus proven almost
as difficult as development of tools for OWL DL, and OWL Lite is not widely used.
2. OWL DL was designed to provide the maximum expressiveness possible while retaining
computational completeness, decidability, and the availability of practical reasoning
algorithms. OWL DL includes all OWL language constructs, but they can be used only under
certain restrictions.
3. OWL Full is based on a different semantics from OWL Lite or OWL DL, and was designed
to preserve some compatibility with RDF Schema.

Protege

Protege is the latest tool in an established line of tools developed at Stanford University for knowledge acquisition. Protege has thousands of users all over the world who use the system for projects ranging from modeling cancer-protocol guidelines to modeling nuclearpower stations. Protege is freely available for download under the Mozilla open-source license. One of the major advantages of the Protege architecture is that the system is constructed in an open, modular fashion. Its component-based architecture enables system builders to add new functionality by creating appropriate plug-in. The Protege Plug-in Library 3 contains contributions from developers all over the world.

XML in Semantic web

Extensible Markup Language (XML) is a subset of SGML (the Standard Generalized Markup Language), i.e. it is totally compatible with SGML. But it is simple and flexible. It’s original aim to tackle the problems of large-scale electronic publishing. However, it is also very important in data exchange on the Web. Despite its name, XML is not a markup language but a set of rules to build markup languages.

Semantic web development challenges

The main issues for the semantic web are:
- A well defined Ontology design approach.
-Semantic data representaion.
-Efficient search technique

Is Google Semantic Search engine.

Semantic is said to be a technology that uses machine understandability to find relevant result on vast data. We technology bloggers routinely drum up articles about it and sell it to you, the adoring masses, as a product that will change your web experience forever. Problem is, we often forget to tell you exactly what semantics and syntactic web.
Truth is that Google is not a semantic search engine, google basically work on the Algorithm introduced by larry page.

SHOE: Sematic Search Engine

SHOE is one of the semantic search engine designed with the concept of semantic search Engine and semantic web. SHOE is one of the university project.
For more detail visit: http://www.cs.umd.edu/projects/plus/SHOE

Semantic web as a research field

As Semantic web is new topic in the web technology and there are lot of aspects to explore. semantic web comes as a challenging topic for the research students of Phd, Masters and Graduate schools. the main Area which comes are:
1. Design of search engine.
2. Ontology design and uses.
3. Search engine optimization.
4. RDF/XML in semantic web.
5. Knowledge management.
6. Annotation, Indexing and search techniques.
These are some of the challenging fields in the semantic web where some of the work has been done already and a lot to be done.

Web Architecture

The current web architecture is two-tiered and characterized by a web client that displays information content and a web server that transfers information to the client. This architecture depends on three key standards: HTML for encoding document content, URLs for naming remote information objects in a global namespace, and HTTP for staging the transfer.

Swoogle - a semantic search engine

Swoogle is one of the semantic search engine that have many issues related to insufficient data and advantages over the current search technique. Swoogle allow users to specify an arbitrary search string to find relevant semantic web documents. In stead of build a full-text index, Swoogle parses a semantic web document and only indexes the part contributing most of the semantics of the document, i.e., the document's URL, the terms being used and/or being defined by the document, explicit description about the the document, and the namespaces used by the document.
For further detail about swoogle semantic search engine visit: http://swoogle.umbc.edu

Ontology Design

In the Initial phase of ontology as a concept. it was designed in an Ad-hoc manner. latter some of the approaches has been presented. these approaches may have different variants based on the requirement and domain for which we are going to present ontology.

RDF(Resource Description Framework)

RDF is a language defined by W3C for the knowledge representation in semantic web. Fore more information about RDF/XML knowledge representation and recent development visit. www.w3c.org/RDF

Ontology

Easiest and Toughest way to define ontology is "Ontology is specialization of conceptualization".
This definition defines the whole world of ontology.

Data and Knowledge

Data: Data is raw. it simply exists and has no significance beyond its existence. it can exist in any form, usable or not. it does not have meaning of itself. in computer parlance, a spreadsheet generally starts out by holding data.
Knowledge: Knowledge is the appropriate collection of information, such that it's intent is to be useful. knowledge is a deterministic process. in the computer parlance, most of the applications we use exercise some type of shared knowledge.

Semantic web: Defination

Tim Berners-Lee defines the Semantic Web as “a web of data that can be processed directly and indirectly by machines.” In the Semantic Web data itself becomes part of the Web and is able to be processed independently of application, platform, or domain. We can use computers to search for these documents, but they still have to be read and interpreted by humans before any useful information can be extrapolated. Computers can present you with information but can’t understand what the information is well enough to display the data that is most relevant in a given circumstance. The Semantic Web, on the other hand, is about having data as well as documents on the Web so that machines can process, transform, assemble, and even act on the data in useful ways.