Chapter 8A Semantic Web Primer 1 Chapter 8 Conclusion and Outlook Grigoris Antoniou Frank van Harmelen

Chapter 8A Semantic Web Primer 2 How it All Fits Together Scenario: Bargaining among personal software agents Each party represented by a software agent They commit to shared understanding of terms: an ontology (e.g. in RDFS or OWL) Case facts, offers and decisions are represented as RDF statements

Chapter 8A Semantic Web Primer 3 How it All Fits Together (2) Information is exchanged in some XML- based language (or RDF-based language) Agent negotiation strategies are described in a logical language Agents decide about next course of action through inference, based on negotiation strategy, case facts and previous offers and counteroffers

Chapter 8A Semantic Web Primer 4 Web Ontology Language: Is Less More? In the beginning the focus was more on expressive power Simpler languages have advantages: – More efficient reasoning support – Easier to learn and apply – Easier for tool vendors to support OWL Lite is a step in this direction

Chapter 8A Semantic Web Primer 5 Rules and Ontologies Rules are orthogonal to description logics One could try to combine them – Computational problems Rule-based languages as alternatives to OWL? Rule-based systems on top of ontology languages

Chapter 8A Semantic Web Primer 6 Will the Semantic Web Succeed? Key Questions Where will the ontologies come from? Where will the semantic markup come from? Where will the tools come from? How should one deal with a multitude of ontologies? Where can we expect the first success stories?

Chapter 8A Semantic Web Primer 7 Where Will the Ontologies Come From? Some large ontologies are becoming de facto standards – WordNet – NCIBI’s cancer ontology Many small ontologies – are hand-created (e.g. RosettaNet) or – Created automatically through machine learning, natural language analysis and from legacy cources (e.g. data schemas)

Chapter 8A Semantic Web Primer 8 Where Will the Semantic Markup Come From? Clearly not by hand Tools for new information resources Natural language techniques, borrowing from legacy sources for old resources

Chapter 8A Semantic Web Primer 9 Where Will the Tools Come From? Large variety of tools already exists – Editors, storage, querying and inferencing, visualization, versioning Mostly developed in academic domain … but taken up in the commercial sector – Highly innovative startups

Chapter 8A Semantic Web Primer 10 How Should one Deal With a Multitude of Ontologies? A big research question, still open – A potential bottleneck Various approaches currently tested – Negotiation – Machine learning – Linguistic analysis

Chapter 8A Semantic Web Primer 11 Promising Areas for Initial Successes Knowledge Management – … because of central authority E-Science – Use ontologies, are informed and enthusiastic users of new technology E-Commerce probably later – Problems with privacy, security and trust