What is Semantic web technology?
The Semantic Web is a mesh of information linked up in such a way as to be easily processable by machines, on a global scale. You can think of it as being an efficient way of representing data on the World Wide Web, or as a globally linked database. To explain it in other level tell any New Yorker you had a sandwich made with rye
bread, corned beef, Swiss cheese, sauerkraut, and Russian dressing but can't remember what it's called. He'll tell you it's a Reuben. But just throwing a few ingredients into a search engine may not get you such a quick or even correct response. This is where the semantic Web comes in. It's more about Web services, where machines can work together to perform inferences in the way people do. The idea behind the semantic Web is to try to turn information on the Web into something with a much more clearly defined meaning. The semantic Web isn't about artificial intelligence, with computers learning how to understand human language. We're talking about a concept whereby computers will have enough semantics to allow them to solve well-defined problems through the sequential processing of operations. It may be that a software agent doesn't even come close to the conclusions that a human is capable of. But it may contribute to building a better and more useful Web than the one we have today.[1]
The Semantic Web is a mesh of information linked up in such a way as to be easily processable by machines, on a global scale. You can think of it as being an efficient way of representing data on the World Wide Web, or as a globally linked database. To explain it in other level tell any New Yorker you had a sandwich made with rye
bread, corned beef, Swiss cheese, sauerkraut, and Russian dressing but can't remember what it's called. He'll tell you it's a Reuben. But just throwing a few ingredients into a search engine may not get you such a quick or even correct response. This is where the semantic Web comes in. It's more about Web services, where machines can work together to perform inferences in the way people do. The idea behind the semantic Web is to try to turn information on the Web into something with a much more clearly defined meaning. The semantic Web isn't about artificial intelligence, with computers learning how to understand human language. We're talking about a concept whereby computers will have enough semantics to allow them to solve well-defined problems through the sequential processing of operations. It may be that a software agent doesn't even come close to the conclusions that a human is capable of. But it may contribute to building a better and more useful Web than the one we have today.[1] How is Semantic technology used in social network analysis?
Semantic web frameworks provide a graph model called resource description framework (RDF1), a query language (SPARQL1) and schema definition frameworks (RDFS1 and Web Ontology language OWL1) to represent and exchange knowledge online. These frameworks provide a whole new Resource description framework (RFD): A special meta-data code is added to a page that describes the relation between the data, these are called triples. Further ontologies are build upon a shared understanding within a community trying to find out what entities and what types of entities exist. This understanding represents an agreement of members of the community over the concepts and relationships that are present in a domain and a way of capturing social networks in much richer structures than raw graphs. Several ontologies are used to represent social networks, where Friend of a Friend (FOAF) is used for describing people profiles, their relationships and their activities online. The properties of the RELATIONSHIP ontology specialize the “knows” property of FOAF to type relationships in a social network more precisely (familial, friendship or professional relationships). The primitives of the SIOC (Semantically linked online communities) ontology extend FOAF in order to model more precisely online activities (e.g. posts in forums, blogs, etc). RDF based descriptions of social data provide a rich typed graph and offer a much more powerful and significant way to represent online social networks than traditional models of Social Network Analysis (SNA). Ontologies, like SCOT (Social semantic cloud of tags) have been designed to capture and exploit such activities and in parallel researchers have attempted to bridge folksonomies and ontologies to leverage the semantics of tags. Once they are typed and structured, the relations between the tags and between the tags and the users also provide a new source of social networks.[2] . Semantic SNA ontology modelling concepts that are used in SNA is degree or centrality. The degree centrality of a resource is the sum of its adjacent edges, i.e. its degree which is further defined into subclasses of OutDegree (is the sum of outgoing edges) and InDegre, (the in degree and out degree is sum of ingoing edges). Also, Distance is to describe the maximal distance that was considered between two actors. Then BetweennessCentrality and ClosenessCentrality are subclasses representing the eponymous centralities. The Semantic Web is a useful platform for linking and for performing operations on diverse person- and object-related data gathered from heterogeneous social networking sites. In the other direction, object-centred networks can serve as rich data sources for Semantic Web applications. This linked data can provide an enhanced view of individual or community activity in localised or distributed object-centred social networks. In fact, since all this data is semantically interlinked using well-given semantics (e.g. using the FOAF and SIOC ontologies), in theory it makes no difference whether the content is distributed or localised. All of this data can be considered as a unique interlinked machine-understandable graph layer (with nodes as users and related data and arcs as relationships) over the existing Web of documents and hyperlinks.[4]
Pros:
The use of Internet has grown 380% since the year 2000 and web data has been increasing every year which requires a need to interlink this data. Semantic technology helps to interlink this data and ease the analysis of data making it simpler and faster. Think about HTML documents; when people started weaving these together with hyperlinks we got a Web of documents. Now think about data. When people started weaving individual bits of data together with RDF triples (that expressed the relationship between these bits of data) we saw the emergence of a Web of data and Semantic web came into picture.[5]
Cons:
As the Semantic Web is a relatively new, dynamic field of investigation, it is difficult to precisely delineate the boundaries of this network. In fact, it is difficult to determine at what point a new research concept becomes a separate field of investigation. The other difficulty in using Semantic Web is that it is difficult to code and use of RFD for the continuously increasing data.[6]
Application of Semantic technology:
The Facebook application of social graph is one such example. The stated goal of the Open G
raph protocol was to enable publishers to "integrate [their] Web pages into the social graph." Essentially, each web page can now become an 'object' in Facebook's social graph (which is Facebook's term for how people connect to each other in its network). Other applications of Semantic technology are Google squared, BBC World Cup Website and GetGlue [7] and software such as UCINET and Pajek use semantic analytics tool. The Semantic technology would have advancement in future where the whole Web would be simpler for social network analysis similar to UCINET and easy to network with people of same interest like that through GetGlue. A simple example of a very small number of the resources and connections that might be found in a Star Wars ontology. You can figure these out on your own from watching the movies and surfing the Web, but a web must have a clear outline to make sense of it and to provide you with different links.
Semantic web frameworks provide a graph model called resource description framework (RDF1), a query language (SPARQL1) and schema definition frameworks (RDFS1 and Web Ontology language OWL1) to represent and exchange knowledge online. These frameworks provide a whole new Resource description framework (RFD): A special meta-data code is added to a page that describes the relation between the data, these are called triples. Further ontologies are build upon a shared understanding within a community trying to find out what entities and what types of entities exist. This understanding represents an agreement of members of the community over the concepts and relationships that are present in a domain and a way of capturing social networks in much richer structures than raw graphs. Several ontologies are used to represent social networks, where Friend of a Friend (FOAF) is used for describing people profiles, their relationships and their activities online. The properties of the RELATIONSHIP ontology specialize the “knows” property of FOAF to type relationships in a social network more precisely (familial, friendship or professional relationships). The primitives of the SIOC (Semantically linked online communities) ontology extend FOAF in order to model more precisely online activities (e.g. posts in forums, blogs, etc). RDF based descriptions of social data provide a rich typed graph and offer a much more powerful and significant way to represent online social networks than traditional models of Social Network Analysis (SNA). Ontologies, like SCOT (Social semantic cloud of tags) have been designed to capture and exploit such activities and in parallel researchers have attempted to bridge folksonomies and ontologies to leverage the semantics of tags. Once they are typed and structured, the relations between the tags and between the tags and the users also provide a new source of social networks.[2] . Semantic SNA ontology modelling concepts that are used in SNA is degree or centrality. The degree centrality of a resource is the sum of its adjacent edges, i.e. its degree which is further defined into subclasses of OutDegree (is the sum of outgoing edges) and InDegre, (the in degree and out degree is sum of ingoing edges). Also, Distance is to describe the maximal distance that was considered between two actors. Then BetweennessCentrality and ClosenessCentrality are subclasses representing the eponymous centralities. The Semantic Web is a useful platform for linking and for performing operations on diverse person- and object-related data gathered from heterogeneous social networking sites. In the other direction, object-centred networks can serve as rich data sources for Semantic Web applications. This linked data can provide an enhanced view of individual or community activity in localised or distributed object-centred social networks. In fact, since all this data is semantically interlinked using well-given semantics (e.g. using the FOAF and SIOC ontologies), in theory it makes no difference whether the content is distributed or localised. All of this data can be considered as a unique interlinked machine-understandable graph layer (with nodes as users and related data and arcs as relationships) over the existing Web of documents and hyperlinks.[4]
Pros:
The use of Internet has grown 380% since the year 2000 and web data has been increasing every year which requires a need to interlink this data. Semantic technology helps to interlink this data and ease the analysis of data making it simpler and faster. Think about HTML documents; when people started weaving these together with hyperlinks we got a Web of documents. Now think about data. When people started weaving individual bits of data together with RDF triples (that expressed the relationship between these bits of data) we saw the emergence of a Web of data and Semantic web came into picture.[5]
Cons:
As the Semantic Web is a relatively new, dynamic field of investigation, it is difficult to precisely delineate the boundaries of this network. In fact, it is difficult to determine at what point a new research concept becomes a separate field of investigation. The other difficulty in using Semantic Web is that it is difficult to code and use of RFD for the continuously increasing data.[6]
Application of Semantic technology:
The Facebook application of social graph is one such example. The stated goal of the Open G
raph protocol was to enable publishers to "integrate [their] Web pages into the social graph." Essentially, each web page can now become an 'object' in Facebook's social graph (which is Facebook's term for how people connect to each other in its network). Other applications of Semantic technology are Google squared, BBC World Cup Website and GetGlue [7] and software such as UCINET and Pajek use semantic analytics tool. The Semantic technology would have advancement in future where the whole Web would be simpler for social network analysis similar to UCINET and easy to network with people of same interest like that through GetGlue. A simple example of a very small number of the resources and connections that might be found in a Star Wars ontology. You can figure these out on your own from watching the movies and surfing the Web, but a web must have a clear outline to make sense of it and to provide you with different links.Conclusion:
The future of search almost certainly involves social networks, social graphs, or social filtering in some capacity. Companies will live or die by whether they get the "social" part right: creating the right level of intimacy, trust, reliability, social connectedness, and accuracy in their results listings. Semantic technology is certainly crucial for dealing with the arising heterogeneity. [8]
The future of search almost certainly involves social networks, social graphs, or social filtering in some capacity. Companies will live or die by whether they get the "social" part right: creating the right level of intimacy, trust, reliability, social connectedness, and accuracy in their results listings. Semantic technology is certainly crucial for dealing with the arising heterogeneity. [8]
Sources:
1. http://www.clickz.com/clickz/column/1693064/what-does-semantic-
2. journal.webscience.org/141/2/websci09_submission_43.pdf
3. dare.ubvu.vu.nl/bitstream/1871/13263/5/7915.pdf
4. www.johnbreslin.org/files/publications/20080910_rwss2008.pdf
5. http://tomheath.com/blog/2009/03/linked-data-web-of-data-semantic-web-wtf/
6. http://www.springerlink.com/content/86n7474l62485n11/
7. http://www.readwriteweb.com/archives/top_10_semantic_web_products_of_2010p2.php
8. http://www.springerlink.com/content/86n7474l62485n11/
9. http://computer.howstuffworks.com/semantic-web4.htm
Other references:
10. http://semtech2010.semanticuniverse.com/
1. http://www.clickz.com/clickz/column/1693064/what-does-semantic-
2. journal.webscience.org/141/2/websci09_submission_43.pdf
3. dare.ubvu.vu.nl/bitstream/1871/13263/5/7915.pdf
4. www.johnbreslin.org/files/publications/20080910_rwss2008.pdf
5. http://tomheath.com/blog/2009/03/linked-data-web-of-data-semantic-web-wtf/
6. http://www.springerlink.com/content/86n7474l62485n11/
7. http://www.readwriteweb.com/archives/top_10_semantic_web_products_of_2010p2.php
8. http://www.springerlink.com/content/86n7474l62485n11/
9. http://computer.howstuffworks.com/semantic-web4.htm
Other references:
10. http://semtech2010.semanticuniverse.com/
2 comments:
Web 3.0, here we come. You have done a very good job researching the Semantic web articles out there. I'm glad you sourced them all, because you have quite liberally taken chunks of what other people say and pasted them in. The part on the applications of semantic technology seems to be yours, and it's interesting! I wish you had written your own conclusion, though.
Thank you Dr. Tunnard for the feedback.
The sessions which made us familar with the applications of updated technologies inspired me to write this post.
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