A short Tutorial on Semantic Web
coauthor: Andreas Hotho, Department of Electrical Engineering and Computer Sciences, University of Kassel
Description
The availability of electronically stored information increased
drastically through the development of the World Wide Web.
Currently the WWW contains more than a billion documents, but support for accessing and precessing information is limited. Most information is only presentable but not understandable by computers. Tim Berners-Lee envisioned the Semantic Web that aims at providing automated access to information due to machine-processable semantics of data. Ontologies formalize a shared understanding of a domain and therefore play a crucial role for communication among human beings and software agents. We will present the underlying ideas of the Semantic Web and will shortly introduce ontologies as the backbone of the Semantic Web. Further we will show how much effort is necessary to setup the Semantic Web and how tools can support this process. Additionally Web and Data Mining techniques can be used to bootstrap the Semantic Web. The idea of Semantic Web Mining is to improve the results of Web Mining by exploiting the new semantic structures in the web.
| Slides | |
| 0:05 | A Short Semantic Web Tutorial |
| 2:25 | Karlsruhe: Location for Semantic Technologies |
| 4:12 | KAON |
| 5:11 | slide4 |
| 6:05 | Semantic Web |
| 8:21 | Machine accessible meaning (What it’s like to be a machine) |
| 9:04 | Semantic Web Layers (T. Berners-Lee et al.) |
| 9:43 | XML: |
| 11:15 | XML: Document = labelled tree |
| 12:33 | XML: limitations for semantic markup |
| 13:51 | XML machine accessible meaning |
| 14:25 | The semantic pyramid again |
| 15:19 | RDF for semantic annotation |
| 17:10 | What does RDF Schema add? |
| 19:25 | RDF Schema syntax in XML |
| 20:57 | Conclusions about RDF(S) |
| 21:25 | Last but not least ... |
| 22:57 | Ontology |
| 25:22 | Communication Principle |
| 27:19 | Views on Ontologies |
| 28:52 | Menu |
| 29:43 | Menu |
| 30:33 | Menu |
| 32:44 | Ontology (in our sense) |
| 37:44 | Ontology & Metadata |
| 39:01 | Example: OntoWeb.org |
| 42:22 | slide27 |
| 42:33 | OTK Methodology: Knowledge Meta Process |
| 44:06 | But ... |
| 44:47 | Why only semi-automatically? |
| 46:29 | Where to start? |
| 48:45 | Extracting Semantics from the Web |
| 49:09 | Ontology Learning |
| 50:02 | Example |
| 56:21 | Example |
| 59:53 | Crawling the (semantic) web for filling the ontology |
| 61:02 | Example |
| 67:39 | Semantic Web Usage Mining |
| 68:59 | Text Document Clustering of Crawled Documents |
| 71:14 | slide44 |
| 71:28 | Our Vision |
| 74:12 | slide46 |
| 75:39 | Acknowledgements |
| 76:50 | Selected Literature |
| 78:42 | Selected Literature |
| 80:24 | Selected Literature |
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