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Ontology - Tools (RDF, RDFS, Potege); Semantic  web, linked Data, Big Data, Data Mining, Data       Harvesting                                                                

 Ontology                                                                                               

 The word ontology comes from two Greek words "onto' - existence, or being real. "Logia - Science on Study / written or spoken. 

 • Ontology is a way of showing the properties of a subject area and how they are related, by defining a set of concepts and categories that represent the subject. 

 • Every academic disciplines on field created ontologies to limit complexity and organize data into information and knowledge. 

 • New ontologies improve problem solving within that domain. 

 • Translating research papes with in every field is a problem made easier when experts from different countries maintain a controlled vocabulary of jargon between each of their language. 

  Origin of ontology                                                                               

 The ontogy derives from the Greek onto (being) and logia (written on spoken). It is a branch of metaphysics, the study of first principles or the root of things 

 • The term is borrowed from philosophy, ontologies arise out of the branch of philosophy known as metaphysics, which deals with questions like "What exists?" and "what is the nature of reality?" 

 •The first occurrence in English of ontology as recorded by the OED (oxford English Dictionary, online Edition, 2008) 

  RDF (Resource Description Framework)                                         

 RDF is a standard model for data interchange on the web. RDF has features that facilitate data merging even if the underlying schemas differ and it specifically supports the evolution of schemas over time without requiring all the data consumers to be changed. RDF extends the linking structure of the web to use URIs to name the relationship between things as well as the two ends of the link (this is usually referred to as a triple). Using this simple model it allows structured and semi-structured data to be mixed, exposed, and shared across different applications. This linking structure forms a directed, labeled graph, where the edges represent the named link between two resources, represented by the graph nodes. This graph view is the easiest possible mental model for RDF and is often used in easy-to-undersland visual explanations. 

  Sementic Web                                                                                      

 With this vision of the web as semantic network much more automated services based on machine processable semantics of data are provided, and metadata are used to explicit represent the semantics of data accompanied with domain theories (e.g. ontologies). This provides a qualitatively new level of service concerning the "knowledge web". 

 The concept of the "Semantic Web" given by Tim Berners Lee outlined the purpose of using machine processable semantics of data and started a new evolution of the world wide web. This approach gives the possibility to sort and structure information in order to access and retrieve, content precisely. 

 A combination of semantical and statistical techniques makes is possible to retrieve more precisely documents that are linguistically  related to the information searched. This is provided by using ontologies in order to disambiguate the user query. However, one major problem we have to deal with is the fact that only a few web pages provide semantic annotations - and this will be a fact at least for the near future. Therefore, we decided not to rely on the annotation of documents, but to use different external resources to assign meaning to documents in relation to a given query in onder to disambiguate their content as a user does when he is searching for information, currently, people have to navigate among a lot of documents to discover the relevant one, because currently retrieval systems do not provides such semantic information on relations. 

 An ontology is a formal, semantical specification of a conceptualization of a domain of interest. Ontologies are used to describe the semantics of information exchange. In natural language texts certain terms have different meanings that are not explicitly defined. Humans are able to disambiguate them by its context. However,  current machine di sambiguating approaches frequently fail due to missing commousense knowledge or appropriate ontology models. An important role for the disambiguation of the word content is the domain in which a word occurs.

  Linked Data                                                                                        

• Linked data is structured data which is interlinked with other data so it becomes more useful through semantic queries. It builds upon standard web technologies such as HTTP, RDF and URIs, but rather than using them to serve web pages only for human readers, it extends them to share information in a way that can be read automatically by computers. 

 • Tim Berners-Lee, directors of the world wide web consortium (w3c) coined the term in a 2006. 

 Big data                                                                                                

 Big Data is a field that treats of ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data - processing application software. Data with many cases (raws) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and date source. Big data was originally associated with three key concepts : volume, variety, and velocity. Other concepts later attributed with big data are veracity (i.e. how much noise is in the data) and value. 

  Data Mining                                                                                        

 Data Mining refers to extracting knowledge  from large amount of data. 

It is the process of  discovering or mining knowledge from a large amount of data. Another term for Data Mining knowledge Discovery from data. 

 Attempts to extract hidden patterns and trends from large databases. 

 Also support automatic exploration of data. 

 Data Mining term was introduced in 1990. 

 Defination of Data Mining 

 i. Finding hidden into the database. 

 ii. Called as exploratory data analysis data driven and deductive learning. 

 iii. Extracting meaningful information database. 

 It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics and database systems. 

 Need of Data Mining  

i. Today big data or huge data so manual analysis is not possible so need automatic analysis.

  Data Harvesting                                                                                  

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