Summary
In this interview from QCon San Francisco 2008, Stu Charlton discusses what the Semantic Web is, RDFa, large-scale adoption of Semantic Web, the SPARQL query language, implementing Semantic Web capabilities in an application, triple stores, and performing a Semantic Web query.

Bio
Stuart Charlton is the Chief Software Architect for Elastra, a provider of Cloud Computing software infrastructure. Stuart specializes in the areas of systems architecture, RESTful web architecture, data warehousing, and is an avid student of lean & agile approaches to business processes and product development.

About the conference
QCon is a conference that is organized by the community, for the community.The result is a high quality conference experience where a tremendous amount of attention and investment has gone into having the best content on the most important topics presented by the leaders in our community. QCon is designed with the technical depth and enterprise focus of interest to technical team leads, architects, and project managers.

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The Semantic Web presents the vision of a distributed, dynamically growing knowledge base founded on formal logic. Common users, however, seem to have problems even with the simplest Boolean expression. So how can we help users to query a web of logic that they do not seem to understand? One frequently proposed solution to address this problem is the use of natural language (NL) for knowledge specification and querying. We propose to regard formal query languages and NL as two extremes of a continuum, where semistructured languages lie somewhere in the middle.

To evaluate what degree of structuredness casual users prefer, we introduce four query interfaces, each at a different point in the continuum, and evaluate the users' preference and their query performance in a study with 48 subjects. The results of the study reveal that while the users dislike the constraints of a fully structured formal query language they also seem at a loss with the freedom of a full NLP approach. This suggests that restricted query languages will be preferred by casual users because of their guidance effect, mirroring findings from social science theory on human activity in general.

Speaker: Prof. Bernstein
Abraham Bernstein is a full Professor at the Department of Information Technology (Institut für Informatik) of the University of Zurich. He conducts research on various aspects of supporting dynamic (intra- and inter-) organizational processes. His work draws from both social science (organizational psychology/sociology) and technical (computer science, artificial intelligence) foundations.

Before coming to Zurich he was an Assistant Professor, at the Information Systems Department in New York University's Stern School of Business, and received a Ph.D. at MIT's Sloan School of Management, where he worked with Prof. Thomas W. Malone at the Center for Coordination Science.

Google Tech Talks
June, 26 2008

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Berners-Lee's vision of the Semantic Web is hindered by a chicken-and-egg problem, which can be best solved by a bootstrapping method: creating enough structured data to motivate the development of applications. We believe that autonomously `Semantifying Wikipedia' is the best way to bootstrap. We choose Wikipedia as an initial data source, because it is comprehensive, high-quality, modestly sized, and contains enough manually-derived structure to bootstrap an autonomous, self-supervised process. In this talk I will present our success to date in this endeavor:

A novel approach for self-supervised learning of CRF information extractors

Automatic construction of a comprehensive ontology via statistical-relational learning

Vast improvements in extraction recall through shrinkage over this ontology and retraining

The stimulation of a virtuous feedback cycle between communal content creation and information extraction

We aim to construct a knowledge base of outstanding size to support inference, automatic question answering, faceted browsing, and potentially to bootstrap the Semantic Web.

Speaker: Daniel S. Weld
Daniel S. Weld is Thomas J. Cable / WRF Professor of Computer Science and Engineering at the University of Washington. After formative education at Phillips Academy, he received bachelor's degrees in both Computer Science and Biochemistry at Yale University in 1982. He landed a Ph.D. from the MIT Artificial Intelligence Lab in 1988, received a Presidential Young Investigator's award in 1989, an Office of Naval Research Young Investigator's award in 1990, was named AAAI Fellow in 1999 and deemed ACM Fellow in 2005. Dan is an area editor for the Journal of the ACM, on the editorial board of Artificial Intelligence, was a founding editor and member of the advisory board for the Journal of AI Research, was guest editor for Computational Intelligence and Artificial Intelligence, edited the AAAI report on the Role of Intelligent Systems in the National Information Infrastructure, and was Program Chair for AAAI-96. Dan has published two books and scads of technical papers.

Dan is an active entrepreneur with several patents and technology licenses. In May 1996, he co-founded Netbot Incorporated, creator of Jango Shopping Search and later acquired by Excite. In October 1998, Dan co-founded AdRelevance, a revolutionary monitoring service for internet advertising which was acquired by Media Metrix and subsequently by Nielsen NetRatings. In June 1999, Dan co-founded data integration company Nimble Technology which was acquired by the Actuate Corporation. In January 2001, Dan joined the Madrona Venture Group as a Venture Partner and member of the Technical Advisory Board.

Google Tech Talks
November 11, 2008

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Lecture slides:

• Beyond string search: fast and accurate retrieval of entities and dependencies.
• Plan
• Document Understanding Cartoon
• Beyond strings, beyond bag of words
• Structure & Domain Knowledge
• Demos
• Plan
• Language Representation
• Semantic tagging (some examples)
• Statistical Methods for Semantic Tagging
• Example: WordNet Supersense labels
• Dependency Parsing
• ( Publicity Break )
• Noise & Redundancy
• Entity Graphs: (forget about documents!)
• Semantic Web & User annotations
• Web Search Engines
• Plan
• Data Structures
• Retrieval Approach
• Inverted indices + position encoding
• Small Adaprive Set Intersection (SASI)
• Data Structures
• Entity and Dependency Retrieval
• Plan
• Machine Learning Issues
• Domain Adaptation

Author: Hugo Zaragoza, Yahoo! Research, Yahoo!

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The authoring of tags - unlike the authoring of traditional metadata - is highly popular among users. This harbours unprecedented opportunities for organizing content. However, tags are still poorly understood. What do they ''mean'', in what senses are they similar to or different from metadata? Different tags support different communities, but how exactly do they reflect the plurality of opinions,what is the relation to individual differences in authoring and reading? In this paper, we offer a definition and empirical evidence for the claim that ''tags are not metadata, but just more content''. The analysis rests on a multi-annotator classification of a blog corpus using the WordNet domain labels system (WND), the development of a system of text-classification methods using WordNet and WND, and a quantitative and qualitative comparative analysis of these classifications. We argue that the notion of a ''gold standard'' may be meaningless in social media, and we outline possible consequences for labelling and search-engine development.

Author: Bettina Berendt, Humboldt Universität Zu Berlin

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Lecture slides:

• Semantic Annotation in the Alvis Project
• Alvis Project
• Limitation of the Keyword-Based Search
• Our Framework
• Annotation of Semantic Unit and Relation Requires Linguistic Processing
• Semantic Annotation with Linguistic Processing
• Semantic Abstraction
• Linguistic Analysis
• Specific Resources Are Needed
• Learning the Resources
• Named-entity Learning
• Terminology Acquisition by YaTea
• Examples of Term Tagging
• Semantic Type Tagging
• Semantic Type Learning by Asium
• Semantic Disambiguation with Syntactic Context
• Semantic Type Learning by Asium (a)
• Semantic Disambiguation with Syntactic Context (a)
• Tagging Semantic Relations
• Rules for Semantic Relation Annotation
• Rule Learning with Propal (ILP-based )
• Preliminary Results on Relation Learning
• Conclusion

Author: Adeline Nazarenko, Université Paris Nord

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Social bookmarking is a recent phenomenon which has the potential to give us a great deal of data about pages on the web. One major question is whether that data can be used to augment systems like web search. To answer this question, over the past year we have gathered what we believe to be the largest dataset from a social bookmarking site yet analyzed by academic researchers. Our dataset represents about forty million bookmarks from the social bookmarking site del.icio.us. We contribute a characterization of posts to del.icio.us: how many bookmarks exist (about 115 million), how fast is it growing, and how active are the URLs being posted about (quite active). We also contribute a characterization of tags used by bookmarkers. We found that certain tags tend to gravitate towards certain domains, and vice versa. We also found that tags occur in over 50 percent of the pages that they annotate, and in only 20 percent of cases do they not occur in the page text, backlink page text, or forward link page text of the pages they annotate. We conclude that social bookmarking can provide search data not currently provided by other sources, though it may currently lack the size and distribution of tags necessary to make a significant impact.

Author: Paul Heymann, Stanford University

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We see an evolutionary path to supporting semantic search over text facilitated by

1. extractors and annotators for ever-growing collections of entity and relation types and
2. search systems that exploit a smooth continuum between structured entities and relations on one hand and uninterpreted text on the other.

The extractors and annotators will be imperfect and incomplete.

Author: Soumen Chakrabarti, Indian Institute Of Technology

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Lecture slides:

• WP 2: Learning Web-service Domain Ontologies
• Outline of the Presentation
• Learning Web-service Ontologies
• Application Mining
• GATE Case Study
• Data Sources
• A Typical Java Class
• Creating a Document Network
• GATE Comment Reference Network
• Transforming Networks into Feature Vectors
• Combining Feature Vectors
• LATINO & OntoGen Demo
• OntoGen Demo
• Dassault Case Study: Inclusion Dependencies
• Conclusions and Future Work

Author: Miha Gr?ar, Jozef Stefan Institute

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Currently, one of the most practical application spaces for Semantic Web technologies is creating an ontological layer over existing legacy databases. Such layering allows flexible applications to be built without the cost of restructuring large amounts of data while maintaining the performance advantages of a relational database. Whereas applications designed to directly query a database encode business logic in specific queries, ontological layer offers a flexible framework whereby dynamically generated queries are resilient to schema changes. This same approach can be used to query multiple decentralized databases from a seemingly centralized point of view, allowing access to multiple database schemas via a single interface. In an ontology, Semantic Web technologies such as RDFS, OWL and SWRL can be used to specify composition rules and abstractions, making it possible to answer complex questions without developing complex queries. TopQuadrant has applied this approach to develop and deploy a flexible faceted search system over a network of large, decentralized legacy databases. The system uses ontologies in two distinct ways: as an abstraction layer over an underlying relational data model; and as a search interface model driving the system itself. This model-based approach allows dynamic system configuration simply through changes to the model. The model controls what data can be searched, what facets can be used for building queries, and even how data should be displayed.

The system combines the structured power of ontologies with more conventional keyword-based search over a related unstructured document corpus. The resulting hybrid system provides capabilities beyond what is possible with either approach alone. This talk will describe the process used for developing the ontological layer; discuss challenges and technical solutions in integrating the databases and bringing together structured and unstructured search. We will also show the benefits of using ontology to specify the search interface and interaction.

Author: Ralph Hodgson, Top Quadrant

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Lecture slides:

• Ontologies, Semantic Web and Virtual Enterprises
• Agenda
• Agenda - Motivation
• Motivation
• Agenda - Ontologies
• Ontology
• Formality of Ontology
• Description Logics
• Agenda - Semantic Web
• Semantic Web
• RDF
• RDF Serialization
• RDFS
• OWL
• OWL as RDF graph
• OWL Reasoning Examples
• SPARQL
• Agenda - Selected Applications - Semantic Search
• Semantic Search
• Web versus Enterprise
• Semantic Search: Example
• Semantic Search
• Agenda - Selected Applications - Semantic Integration
• Semantic Integration
• Communication
• “Berlin” Ontology
• “Boeing” Ontology
• Translation
• Ontology Service
• Translation – Architecture
• Agenda - Selected Applications - Semantic Web Services
• Services
• Semantic Web Services
• OWL-S
• OWL-S Matchmaking
• Semantic Web Services
• Agenda - Summary
• Summary
• Some references and further reading
• Thanks for your attention

Author: Marek Obitko, Rockwell Automation

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Lecture slides:

• Improving the recruitment process through ontology-based querying
• Outline
• Motivation
• Project Context
• Today’s e-Recruitment
• Use Case-based Requirement Analysis
• Semantic Web-based solution (I)
• Semantic Web-based solution (II)
• Advantages of Semantic Web-based solution
• Semantic Web-based solution (III)
• Some (still) open issues
• How to relax the query
• How to specify these relaxations
• How to specify these relaxations
• Rewriting Rules (I)
• Replacing OWL queries
• Search tree
• Advantages and Disadvantages
• Proposal for Combination
• Conclusion

Author: Holger Wache, University Of Applied Sciences Northwestern Switzerland (Fhnw)

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This paper proposes a novel expert peering system for information exchange. Our objective is to develop a real-time search engine for an online community where users can query experts, who are simply other participating users knowledgeable in that area, for help on various topics.We consider a graph-based scheme consisting of an ontology tree where each node represents a (sub)topic. Consequently, the fields of expertise or profiles of the participating experts correspond to subtrees of this ontology. Since user queries can also be mapped to similar tree structures, assigning queries to relevant experts becomes a problem of graph matching. A serialization of the ontology tree allows us to use simple dot products on the ontology vector space effectively to address this problem. As a demonstrative example, we conduct extensive experiments with different parameterizations. We observe that our approach is efficient and yields promising results.

Author: Tansu Alpcan, Deutsche Telekom Laboratories

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Lecture slides:

• OntoWiki - A Tool for Social, Semantic Collaboration
• How do we get more Semantic to the Semantic Web?
• Adaptive vs. predictive
• Adopting Web 2.0 ideas
• The Wiki Way
• OntoWiki / Powl Architecture
• OntoWiki Application Domain
• Current Work & Future Plans

Author: Sören Auer, University Of Leipzig

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Lecture slides:

• Learning to Reason: Knowledge Acquisition in Cyc
• Overview
• Expressivity
• The Cyc Knowledge Base
• Syntactic Power
• For Inference: Senses of ‘In’
• Senses of ‘In’
• Concepts are densely related
• in Cyc
• Power
• The Cyc Analytic Environment - 1
• The Cyc Analytic Environment - 2
• How is this done?
• The Cyc Analytic Environment - 3
• 45th’s Space Wing Hurricane Preparedness
• Performance: Subtheory: disjointWith
• Inference is Fast & Trainable
• You can get Cyc
• Cycorp Corporate Mission
• Manual Knowledge Entry
• Cycorp Corporate Mission
• Ambitious Approach: General Automated Interview - 1
• Ambitious Approach: General Automated Interview - 2
• Ambitious Approach: Analogical Reasoning
• Ambitious Approach: Using Background Knowledge
• Concept Refinement Interview
• Ambition is Good but...
• What can you do?
• Example: Getting Knowledge in Context
• Example: Use of Acquired Knowledge - 1
• Example: Use of Acquired Knowledge - 2
• Example: Use of Acquired Knowledge - 3
• Example: Use of Acquired Knowledge - 4
• Intelligent Search
• Content adaptation: heart valve repair
• Content adaptation: coronary artery
• Facts and Rules (from 1998, 2003)
• Some opportunities for ML
• Document Tagging
• Military Taxonomy
• Knowledge-based disambiguation - 1
• Disambiguation Rules: ‘Jet’
• Knowledge-based disambiguation - 2
• Knowledge Driven Disambiguation - 1
• Rule Induction
• The Induction Pipeline
• Example - 1
• Example - 2
• Early Results
• Sample Rules Produced
• Situation Recognition
• Example - 1
• Example - 2
• Example - 3
• Example - 4
• Other Approaches
• Previous Results on Whodunit Task
• Integrating Markov Logic
• Early ML Experiments
• Markov Logic Work
• Getting Ground Facts
• Second Life
• Overview
• Cyc

Author: Michael Witbrock, Cycorp Inc.

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Starting with an overview of the requirements for defining and using ontologies on the Web Sean will then outline the main concepts and issues associated with the Web ontology languaages, RDF, RDFS and OWL. By the end of this presentation attendees will have gained a basic understanding of the principles underlying OWL.

Author: Sean Bechhofer, University Of Manchester

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Lecture slides:

• Bootstrapping ontology evolution with multimedia information extraction
• The facts
• Objectives
• Approach
• The end user’s view pt 1
• The end user’s view pt 2
• The service provider’s view pt 1
• The service provider’s view pt 2
• Semantics extraction
• Multimedia semantic model
• Ontology evolution
• Open issues: semantics extraction
• Open issues: semantic model
• Open issues: ontology evolution
• Open issues: system integration
• BOEMIE workshop

Author: Georgios Paliouras, Ncsr "Demokritos"

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Using the insights gained in a decade of research Asun will describe the main principles and phases underlying the construction of ontologies, including acquisition, conceptualization, evaluation and integration. By the end of this presentation attendees will have an overview of the main steps involved in creating an industrial strength ontology.

Author: Asunción Gómez Pérez, Universidad Politecnica De Madrid

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The purpose of this tutorial is to help attendees gain sufficient experience of working with OWL and tools to allow them to fruitfully explore new ontologies that they may encounter. In other words, they should be able to do the equivalent of “view source” on an ontology. Also, they will get better fluency in the use and abuse of OWL by examining features, limitations, and workarounds in real contexts, as well as gaining an understanding of the impact of future extensions of OWL, in particular of rules and the proposed revision of the language called OWL 1.1.

Author: Uli Sattler, School Of Computer Science, The University Of Manchester, Uk

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This lecture will be given by Michael Witbrock, chief director of Cycorp (http://www.cyc.com) which is involved in the construction of the largest knowledge database and system for assumptions. On the lecture he will present us how to encode knowledge and how this knowledge is being used by computers with the intent to perform new unknown facts. He will also show some demos, which are pointing to the capabilities of Cyc which with its quality performs better results than any other known modern method which can be found on the web. We have to mention also that Cycorp has opened its new branch office in Slovenia.
On the seminar we will discuss the Cyc system which is said to be one of the most controversial experiments in computer and artificial intelligence history. The idea of Cyc has its beginnings in the 80s when the goal of a group of scientists from the University of Stanford was to build base of knowledge which would incorporate most of the knowledge we operate with in everyday life. On top of the knowledge base a mechanism of conclusion making would be used and would enable the use of encoded knowledge for the formulation of unknown knowledge. After 20 years of development the Cyc system contains a great quantity of common sense knowledge encoded in formal logic. The system was used for a series of difficult applications where a deeper view into the stored information was needed.

On the lecture we will be able to see how Cyc works in practice. Cyc and Jozef Stefan Institut, Slovenia have opened a new branch of Cyc to continue the development of the system. Michael Witborck is also the director of the Slovene affiliation.

Author: Michael Witbrock, Cycorp

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The purpose of this tutorial is to help attendees gain sufficient experience of working with OWL and tools to allow them to fruitfully explore new ontologies that they may encounter. In other words, they should be able to do the equivalent of “view source” on an ontology. Also, they will get better fluency in the use and abuse of OWL by examining features, limitations, and workarounds in real contexts, as well as gaining an understanding of the impact of future extensions of OWL, in particular of rules and the proposed revision of the language called OWL 1.1.

Author: Bernardo Cuenca Grau, University Of Oxford

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The purpose of this tutorial is to help attendees gain sufficient experience of working with OWL and tools to allow them to fruitfully explore new ontologies that they may encounter. In other words, they should be able to do the equivalent of “view source” on an ontology. Also, they will get better fluency in the use and abuse of OWL by examining features, limitations, and workarounds in real contexts, as well as gaining an understanding of the impact of future extensions of OWL, in particular of rules and the proposed revision of the language called OWL 1.1.

Author: Bijan Parsia, School Of Computer Science, University Of Manchester

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Lecture slides:

• GRDF
• Development Scenario
• Application Snapshot
• Semantic GML
• GRDF Snapshot
• GRDF & GML Correspondence
• GRDF and OWA
• GRDF and SWS
• GRDF Alignment
• GRDF Adaptors
• Geo Data Rights Management
• GRDF Applications
• Thank you

Author: Ashrafaul Alam

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Lecture slides:

• Extending faceted navigation for RDF data
• Summary
• Outline
• Faceted browsing example: iTunes
• Faceted browsing: limitations
• Faceted browsing on the Semantic Web
• Selection operators
• Inverse operators
• Outline
• Interface construction algorithm
• Facet browsing: decision tree
• Facet ranking: optimising decision tree traversal
• Outline
• Prototype: choose FBI dataset
• Prototype: interested in terrorists
• Prototype: filter by facets
• Prototype: see matching results
• Evaluation
• Challenges
• Summary

Author: Eyal Oren, Network Institute, Vu University Amsterdam

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Lecture slides:

• Fresnel: A Browser-Independent Presentation Vocabulary for RDF
• Semantic Web Browsers and other RDF Visualization Tools
• Applications with different goals and approaches . . .
• . . . but faced with the same core issues
• Fresnel : a presentation vocabulary for RDF
• Outline
• Fresnel’s Foundational Concepts
• Core Lens Vocabulary - Content Selection and Ordering
• Code example
• Core Formatting Vocabulary
• Example
• Selector Languages
• Example : browsing Geonames + FOAF data
• Extension Vocabularies
• Example of extension vocabulary : Fresnel2D
• An open, community-based effort

Author: Emmanuel Pietriga, Inria

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The Semantic Web has reached a level of maturity that allows RDF and OWL to be adopted by large commercial software companies. Many of the products that are based on these standards promise the ability to provide more effective solutions to the increasing IT complexity that many industries are facing. This presentation will describe Oracle's interest in being an early adopter of Semantic Web technologies, as well as providing a technical overview of the RDF Data Model in the latest release of the Oracle Database. Experiences gained from implementing Semantic Web technologies will also be provided.

Author: Susie Stephens, Oracle

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Automated composition of Web services or the process of forming new value added Web services is one of the most promising challenges in the semantic Web service research area. Semantics is one of the key elements for the automated composition of Web services because such a process requires rich machine-understandable descriptions of services that can be shared. Semantics enables Web service to describe their capabilities and processes, nevertheless there is still some work to be done. Indeed Web services described at functional level need a formal context to perform the automated composition of Web services. The suggested model (i.e., Causal link matrix) is a necessary starting point to apply problem-solving techniques such as regression-based search for Web service composition. The model supports a semantic context in order to find a correct, complete, consistent and optimal plan as a solution. In this paper an innovative and formal model for an AI planning-oriented composition is presented.

Author: Freddy Lecue, France Telecom Research

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Lecture slides:

• ISENS: Information Integration via an End-to-End Distributed Semantic Web System
• Outline
• The Problem and the Opportunity
• Motivating Query Example
• Technical Challenges
• How are we addressing the challenges?
• ISENS Prototype Architecture
• We independently created two ontologies
• Relevant Statements
• Map Ontologies
• Limitations of OWL Maps
• Standard Information Integration Approaches
• Why the MiniCon Algorithm?
• MiniCon Algorithm Flow
• Generation of MiniCon Descriptions
• MiniCon Adaptation
• Sample of Subtask Execution Times
• Summary
• Future Development
• Acknowledgements

Author: Dimitre Dimitrov, Tech X Corporation

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Web is a highly distributed and dynamic environment that poses a number of challenges to designers and users of Web services and applications. In our research we promote a user centric approach to studying Web related issues and designing solutions that would improve the user's experience. We show how an exploratory study of users' behaviour led to new opportunities for modifying and improving Web publishing practices and browser features. We present the findings from quantitative and qualitative analyses of the users' logs and the impact they had on the prototype design. We point to the depth of problem understanding that has been achieved through the combined approach of statistical analysis of user logs and user interviews.

Author: Natasa Milic Frayling, Microsoft Research, Cambridge

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Lecture slides:

• Human Language Technology for the Semantic Web
• Are you wasting your time?
• Structure of the Tutorial
• The Knowledge Economy and Human Language
• HLT & Knowledge: Closing the Language Loop
• Background and Examples (1)
• Background and Examples (2)
• Structure of the Tutorial
• Information Extraction (1)
• Information Extraction (2)
• MUC-7 tasks
• An Example
• Performance levels
• What are Named Entities?
• What are NOT NEs (MUC-7)
• Basic Problems in NE
• More complex problems in NE
• Structure of the Tutorial
• Corpora and System Development
• Some NE Annotated Corpora
• The MUC-7 corpus
• The MUC-7 Corpus (2)
• ACE – Towards Semantic Tagging of Entities
• ACE Example

Author: Hamish Cunningham, University Of Sheffield

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The presentation presents the status of the Semantic Web from W3C's perspective, referring to the finished and active works in terms of W3C groups, view of available tools, and ideas floating around for possible future work. References and examples of real Semantic Web applications will also show the widening adoption of the technology by the industrial community.

Author: Ivan Herman, World Wide Web Consortium

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We present a baseline system for modeling textual entailment that combines deep syntactic analysis with structured lexical meaning descriptions in the FrameNet paradigm. Textual entailment is approximated by degrees of structural and semantic overlap of text and hypothesis, which we measure in a match graph. The encoded measures of similarity are processed in a machine learning setting.

Author: Aljoscha Burchardt, Saarland University

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Lecture slides:

• Metadata Extraction: Human Language Technology and the Semantic Web http://gate.ac.uk/ http://nlp.shef.ac.uk/ Hamish Cunningham Kalina Bontcheva Valentin Tablan Diana Maynard SEKT meeting,
• The Knowledge Economy and Human Language
• HLT and Knowledge: Closing the Language Loop
• Structure of the Tutorial
• Information Extraction
• MUC-7 tasks
• An Example
• Performance levels
• What are Named Entities?
• What are Named Entities (2)
• Basic Problems in NE
• More complex problems in NE
• Corpora and System Development
• Some NE Annotated Corpora
• The MUC-7 corpus
• The MUC-7 Corpus (2)
• ACE – Towards Semantic Tagging of Entities
• ACE Entities
• ACE Example
• Annotation Tools: Alembic, GATE, ...
• Performance Evaluation
• The Evaluation Metric (2)
• The GATE Evaluation Tool
• Corpus-level Regression Testing
• Regression Testing (2)
• Challenge: Evaluating Richer NE Tagging
• SW IE Evaluation tasks
• Structure of the Tutorial
• Two kinds of IE approaches

Author: Hamish Cunningham, University Of Sheffield

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Lecture slides:

• EKOSS a knowledge-user centered approach to Knowledge Sharing, Discovery and Integration on the Semantic Web
• Need for scientific knowledge
• Example of Scientific Knowledge Needs
• Presentation Outline
• People in society want to access university scientific knowledge...
• People in society want to access university scientific knowledge...
• In the past...
• In the past...
• In the Past...
• However, writing a paper today is like putting a message in a bottle
• However, writing a paper today is like putting a message in a bottle
• Attach a transmitter to the bottle
• Attach a transmitter to the bottle
• Research Approach
• Looking for a \\\"Blue Car with red Doors\\\"
• Simple word-matching
• Thesaurues-based search
• Semantic Matching
• Semantic Matching
• Presentation Outline
• The EKOSS System
• What kind of semantic description?
• Description Logics Ontologies as Simplification of Natural Language
• Description Logics Ontologies as Problems with Implementation
• The SCINTENG Knowledge Model
• Developing a Solid Oxide Fuel Cell
• Sintering Reaction Model
• Insulation Layer Model
• SOFC design specification
• Reasoning in EKOSS
• Matching results - sintering model
• Matching results - insulation model

Author: Steven Kraines, University Of Tokyo

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In this talk I will present an overview of Human Language Technology (HLT) and its use in Semantic Web development. HLT is concerned with automatic linguistic processing towards the semantic analysis and extraction of information from textual data. In the context of the Semantic Web the use of HLT is in knowledge markup of web documents for ontology population and text mining for ontology evolution (extension and modification of ontology models). The talk will include examples of both as currently developed in the context of the SmartWeb project on "Mobile Broadband Access to the Semantic Web" - http://www.smartweb-projekt.de/

Author: Paul Buitelaar, Dfki Gmb H, Uage Technology Department

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In this paper we claim that learning in a first order logic framework is crucial for the future of user modeling applications in the context of the Semantic Web (SW in the remaining of the paper). We first present some works that have currently been done for designing first order logic based languages, for reasoning in the SW. In the context of user modeling in the SW, it would then be relevant to use such languages to model user’s behaviors and preferences. We show that discovering knowledge in the context of such languages could be done using multi-relational data mining that has already provided efficient prototypes. Nevertheless, some work remains to be done in order to use them in that context and we give some directions for that purpose.

Author: François Jacquenet, University Of Saint Etienne

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We examine research issues that arise when most information items in an enterprise can be linked to each other via short paths, implicit or explicit. In such high-recall settings, the treatment of metadata management, indexing and ranking needs new attention. Additional issues arise as to the best way to handle updates to the connections, whether on or off the transaction path. Even traditional techniques, such as classification and clustering of documents, which stand to benefit from the extra information provided by the so-called network of meaning, need to be reexamined for how best to exploit the extra information. The talk ends with an examination of some promising avenues for using high recall as a driver for the next wave of business process automation

Author: Pankaj Mehra, Hp Labs, Palo Alto

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Don’t search for keywords! Search for ideas! Our system extracts subject-verb-object triples from unstructured text, groups them into hierarchies, and allows iterative refinement to findexactly what you want.

Author: Vladimir Zelevinsky, Endeca Technologies Coauthor: Robin Stewart, MIT

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The proposed tutorial presents the Web Service Execution Environment WSMX and the Internet Reasoning Server IRS as an integrated environment for development and execution of Semantic Web Services on basis of the Web Service Modeling Ontology WSMO.

Author: Mick Kerrigan, Digital Enterprise Research Institute (Deri), University Of Innsbruck

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Lecture slides:

• Building semantic applications
• Introductions: myself, and SEKT
• Introductions: myself, and SEKT cont.
• Motivation
• The need for semantics
• Higher precision, greater recall
• Precision in searching
• Interests and context
• Too much relevant information
• In the right form
• Visualisation knowledge
• The goal
• Acquiring and using semantics
• Context
• Ontology modelling
• Metadata
• Accessing a knowledgebase
• The knowledgebase
• Ontology-based information extraction
• Information integration
• Mapping ontologies
• Applications … in SEKT
• Intelligent content management
• Improving and extending
• SEKT architecture
• Knowledge management
• Improving knowledge sharing
• Intelligent decision support
• Semantic distance
• Better decisions
• OPLK classes identified
• SEKT applications – intelligent decision support
• Using factorial analysis
• Ontological subdomains
• Architecture of Iuriservice
• Ontology engineering in SEKT
• Person class
• Property and class hierarchies
• Profiles in the Digital Library
• Topics
• Classes as property values
• Diligent
• Distributed and loosely controlled
• Diligent Wiki
• More applications
• Knowledge discovery
• Business intelligence
• The semantic desktop
• Extensible and interoperable
• Keeping the context
• Communication
• Collaboration
• Semantic Grid
• Grid services and resources
• State-of-the-art
• Challenges
• Bibliography - 1
• Bibliography - 2
• Bibliography - 3
• Bibliography - 4

Author: Paul Warren, British Telecommunications Plc.

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Vulcan's Project Halo is an ambitious, multiyear research program to develop a detailed scientific knowledge base that can answer AP-level questions and provide explanations in a user-appropriate manner. It is one of the largest AI research programs in the US today. Halo's current focus is building AI tools that allow graduate students in chemistry, biology, and physics to author scientific knowledge adequate to answer sophisticated natural-language questions without relying on trained knowledge engineers. Halo researchers have been working to link Semantic Web technology with the other knowledge representations in the system. This talk will lay out Halo's technologies and results to date, and describe the technical and UI issues we have faced in getting users to author scientific conceptual knowledge.

Author: Mark Greaves, Vulcan Inc.

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In addition to its technical implications, the semantic web vision gives rise to some challenges concerning usability and interface design. What difficulties can arise when persons with little or no relevant training try to

• formulate knowledge (e.g., with ontology editors or annotation tools) in such a way that it can be exploited by semantic web technologies;
• leverage semantic information while querying or browsing?

What strategies have been applied in an effort to overcome these difficulties, and what are the main open issues that remain? This talk will address these questions, referring to examples and results from a variety of research efforts, including the project SemIPort, which concerns semantic methods and tools for information portals, and Halo 2, in which tools have been developed and evaluated that enable scientists to formalize and query college-level scientific knowledge.

Author: Anthony Jameson, Dfki And International University In Germany

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Lecture slides:

• overview
• more semantic web browsers...
• traditional faceted browsing
• /facet use case: browsing cultural heritage
• Semantic web faceted browsing
• Browsing heterogeneous RDF-- 4 contributions
• Multiple resource types
• Browsing heterogeneous RDF-- 4 contributions
• dynamic facts
• Browsing heterogeneous RDF-- 4 contributions
• autocompletion search
• Browsing heterogeneous RDF-- 4 contributions
• cross relations
• pros and cons
• more from us...
• acknowledgements

Author: Michiel Hildebrand, Centrum Voor Wiskunde En Informatica Cwi

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In the recent years semantic technologies have demonstrated their usefulness and applicability in a variety of domains, the Semantic Web being the most prominent one. The Semantic Web has started to move from academic research to deployed business-critical and scientific applications, with support from recommendations (standards) developed under W3C governance and a growing list of commercial technologies and products is being developed. These developments seem to be early but firm steps in establishing semantics as a core column of computer science and application development. The outreach of this development can only be assessed to limited degree at the moment, but most likely will affect key aspects of society and the way we communicate.

This high potential was recognized early by funding agencies all over the world. However, after the first strong funding in US by DARPA, subsequent research funding seems to be limited. Europe seems to have seem more substantial and sustained funding, at least during last few years. Now may be a good time to assess what has been achieved so far and how funding agencies see future research directions, funding opportunities and funding environments, i.e., what are the planned strategies and instruments of funding agencies to maximize the impact of future research in semantics. We consider it specifically interesting to the research community to hear the opinions and plans of the major funding bodies around the world and to learn about their view on future issues/requirements/applications/challenges related to semantics and Semantic Web-- and by extension their opinion on the needs of industry, government and education for research in the Semantic Web and related areas.

Coauthor: Frank Olken, National Science Foundation Coauthor: Isidro Laso Ballesteros, European Commission Coauthor: Mark Greaves, Vulcan Inc. Coauthor: Todd Hughes, Darpa Coauthor: James Milligan, Air Force Research Laboratory, Usa Coauthor: Amit Sheth, University Of Georgia

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Lecture slides:

• Know how to Know-How
• Software AG’s Information Integrator
• Kinds of Integration Problems
• Onology
• Mapping an Ontology with Data
• Concept
• Information Integrator Studio / Semantic Server
• Concept
• Studio
• Datasource mapping Rules
• Concept
• Studio
• Mapping rules
• Concept
• Studio
• Runtime Performance

Author: Jurgen Angele, Ontoprise Gmb H

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Lecture slides:

• Semantic Structure in Structured Document Retrieval
• Objectives:
• Motivation
• Hypothesis
• Reuters - towards a meaningful structure
• Example
• Reuters XML DC
• Experimental Setup
• Results - CO
• Results - CAS
• Concluding Remarks

Author: Roelof Van Zwol, Yahoo! Research Barcelona

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The virtual state is a metaphor meant to draw attention to the structures and processes of the state that are becoming increasingly aligned with the structures and processes of the semantic web. Semantic Web researchers understand the potential for information sharing, enhanced search, improved collaboration, innovation, and other direct implications of contemporary informatics. Yet many of the broader democratic and governmental implications of increasingly networked governance remain elusive, even in the world of public policy and politics.

Author: Jane Fountain, University Of Massachusetts Amherst

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The main goal of this tutorial is to provide an extensive survey of the past and current work in the area of context related topics. This includes analysis of the past work: (1) defining the notion of “context”, (2) present logic-based formalisms for dealing with contexts, (3) present probabilistic/fuzzy approaches to model context, (4) demonstrate “modelling the context” and “reasoning with contexts” in real-life applications. In addition, the presented work we will provide a synthesis of the past work in the light of a unified categorization of context-related approaches along several dimensions which appear as relevant from theoretical and practical point of view.

Author: Igor Mozeti?, Jozef Stefan Institute

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Lecture slides:

• Automatic Annotation of Web Services Based on Workflow Definitions
• Web Service Annotations
• Approach
• Outline
• Ontologies
• Deriving annotations for inputs
• Deriving annotations for outputs
• Use of derived annotations: facilitating the annotator task
• Use of derived annotations: parameter compatibility
• ?
• Application to bioinformatics web services
• Sources of Annotation conflicts
• Example of a workflow with a mismatch
• Resolution of Annotation conflicts
• Conclusions
• Acknowledgments

Author: Khalid Belhajjame, University Of Manchester

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The main goal of this tutorial is to provide an extensive survey of the past and current work in the area of context related topics. This includes analysis of the past work:

1. defining the notion of “context”,
2. present logic-based formalisms for dealing with contexts,
3. present probabilistic/fuzzy approaches to model context,
4. demonstrate “modelling the context” and “reasoning with contexts” in real-life applications.

In addition, the presented work we will provide a synthesis of the past work in the light of a unified categorization of context-related approaches along several dimensions which appear as relevant from theoretical and practical point of view.

Author: Marko Grobelnik, Department For Knowledge Technologies, Jožef Stefan Institute

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