1. Dutch Semantic Web Get-together
Vrije Universiteit Amsterdam, March 16th
Organization: Antoine Isaac, Eyal Oren
Sponsor: the Network Institute

2. Agenda 09:30-09:45: welcome 09:45-10:30: big talk (Ivan Herman)
10:30-10:45: coffee + koekjes
10:30-11:30: speeddating
11:30-12:30: lightning talks + discussion
12:30-13:15: lunch
13:15-14:15: lightning talks + discussion
14:15-14:30: coffee
14:30-15:30: lightning talks + discussion
15:30-16:30: speeddating and drinks

3. Lightening talks - 1 Marshall Hoekstra Deursen Top Ossenbruggen Amin
Hildebrand
Wang Yiwen
Brugman

4. Scott Marshall

5.
Rinke Hoekstra (VU/UvA) and Saskia van de Ven (UvA)
Law and Semantic Web
Representation of regulations using Semantic Web languages
Legal reasoning using standard (DL) reasoners
Issue: “What if two sources say different things?”
Specificity (…)
Lex Superior
Temporal Validity (overwrite)
Lex Posterior
Applicability to old cases
Authority (implicit)
Jurisdiction
Location (…)
Scope (deeming provision)
Import (documents)
References to definitions, not documents

6. Semantic Web vs. Multimedia Annotation
Feature extraction
Find the best match
Feature DB
feature extraction results in low-level concepts
matching algorithms use a number of rules to propose a high-level concept
use SW technologies for this purpose
formally described feature DB
formally described rules
Metadata modeling

7. e-Science for Food Research
Jan Top
e-Science for Food Research
Semi-open innovation in food
RDF/OWL model of the scientific workflow
research question, preparation, experiment, data analysis, reporting, …
Food Thesaurus
Web application Tiffany – Sesame plus .NET
Openness or ‘stimulated-disclosure’?
Flexibility of the model, but how flexible is the user?
The new Luxaflex® powerpoint template

8. Jacco van Ossenbruggen
Who are the users? Why would they use the cloud? What tasks can be supported? How will the semantics help?
Jacco van Ossenbruggen

9. Alia Amin Comparison Search
Who : CH conservators, researchers, students
Why: Important Information Gathering task (JCDL’08)
What: Compare sets
using multiple thesauri, heterogeneous dataset
alignment between properties and values
How: semantic search & visualization

10. Michiel Hildebrand Subject Annotation
* thesauri may partly overlap * vocabulary alignment to avoid duplicates in the interface * distinguish ambiguous terms * present extra information (SKOS plus) * search functionality beyond SPARQL * prefix search * filtering of results * multiple keywords (syntactic and semantic relation) * merge equivalent results * support (hierarchical) navigation * visualization and organization differs per thesaurus (per type of resource)
Who: Professional annotators Why: Subject matter annotation of prints What: Search in multiple thesauri for annotation terms How: Autocompletion on who/what/where/when
Michiel Hildebrand 10

11. Patterns of Semantic Relations in Content-based Recommender Systems
Accuracy
Frequency
Serendipity
teachOf/
studentOf
Yiwen Wang, CHIP Project /03/2009

12. Hennie Brugman catalog vocabulary texts Semantic annotations
annotation service (GATE-Apolda)
annotations
ranking service
term suggestions
annotation repository
thesaurus (skos)
thesaurus
conversion enrichment
catalog
vocabulary
texts
5 (types of) repositories
2 services (ranking, annotation)
All hidden between clearly defined interfaces (for reusability, general applicability)
Dashed lines: services use repositories
Examples:
Ranking algorithm uses ‘semantic annotations’ and ‘GTAA vocabulary’
Annotation uses vocabulary and generates semantic annotations
Semantic
annotations
Annotation
(based on GATE)
Recommen-
dation &
Ranking
video

13. Lightening talks - 2 Brickley Omelayenko Cimiano Cornet Willems
Koenderink
Rijgersberg
Rutledge
Nederbragt
Bocconi

14. Dan Brickley

15. AnnoCultor porting collections and vocabularies to the Semantic Web
Borys Omelayenko
AnnoCultor porting collections and vocabularies to the Semantic Web
Museums: Various models
Louvre
Rijksmuseum
RKD
Tropenmuseum
Volkenkunde
etc.
e-culture: DC / SKOS
Work
Image
Concept
AnnoCultor
Converter in Java or XML*
No miracles,
program yourself
100s properties and concepts per institution
Structural conversion: from simple to very complex
Semantic enrichment: term lookup, disambiguation
Up to 80% terms found in vocabulary lookup
annocultor.sourceforge.net
CATCH day,

16. Philipp Cimiano Dutch SW Day @ VU, Amsterdam 16th March 2009
Web Information Systems (WIS) - EWI
TU Delft

17. Towards Linguistically Grounded Ontologies (joint work w. P
Towards Linguistically Grounded Ontologies (joint work w. P. Buitelaar, P. Haase and M. Sintek)
The Model
The Need
Ontologies do not need labels „per se“.
We need labels for:
human consumption
linking textual data to ontologies (ontology population)
generating NL descriptions from ontologies
etc. etc.
We need a general and principled model to associate
linguistic information to ontologies.
Related Work
Does SKOS do the job?
No, SKOS was defined for totally different purposes.
It provides a datamodel (highjacking RDF/OWL) to represent
classification schemas:
ex:animals rdf:type skos:Concept;
skos:prefLabel
skos:altLabel
skos:prefLabel
skos:altLabel
There are other models which are more in line with our work,
e.g. the LIR Model from UPM/Madrid.
Related Work
The Need
Requirements
capture morphological relations between terms, e.g., through inflection (animal,animals), separately from the domain ontology;
represent the morphological or syntactic decomposition of composite terms and the linking of the components to the ontology;
model complex linguistic patterns, such as subcategorization frames for specific verbs together with their mapping to arbitrary ontological structures;
specify the meaning of linguistic constructions with respect to an arbitrary (domain) ontology, and
clearly separate the linguistic and semantic (ontological) representation levels.
The Goal
The goal of this research is to yield a principled and generic model that
allows to declaratively specify a lexicon for an ontology.
The main goal is to avoid that all applications have to re-specify the
connection between language and an ontology in an „adhoc“ fashion
The vision is one where we can also publish lexica for ontologies
(in addition to the ontologies themselves) and people can search and
reuse these „ontology lexica“
The Model
Requirements
Future Work
Spread the model and make people use it (first version of an API is available)
Develop techniques that automatically instantiate the model
Investigate relation to other models (e.g. LIR)
The Goal
Future Work

18. Understanding & Evaluation
Ronald Cornet - Department of Medical Informatics Academic Medical Center – University of Amsterdam
Understanding & Evaluation
Implementation
GUI Design
Functionality
Classifications (rules)
Information models
Large-scale reasoning
Development
Formalization
Standardization
Architecture
SNOMED CT
Terminological
Systems
Auditing & Maintenance
(DL-based) Qual. Assurance
Collaborations VU
IHTSDO
NEN/CEN/ISO
Domains
Intensive Care
Anesthesiology
Nephrology

19. ERDSS Emerging Risks Holistic Ontology Forward chaining
Don Willems WUR/IM
Emerging Risks
Holistic
Ontology
Forward chaining
Risk assessment
Uncertainty

20. Nicole Koenderink, WUR -- IM
ROC
Interviews cost time for KE and DE → DE mature role
Difficult for DE to provide knowledge → prompting by tool
Models created from scratch → reuse existing sources
Task-specific knowledge required → monitor scope
Specify scope
Identify sources
ROC Tool
Proto-ontology
Extract triples
Sesame repository

21. Design and use of a quantitative research vocabulary for e-science
Hajo Rijgersberg
Design and use of a quantitative research vocabulary for e-science
Problem
Approach
Vocabulary
Web services and web apps
Evaluate use
Lessons learnt
Support simple, recurring actions
Focus on those who actually need support
Integrate in popular tools
Excel add-in
Design and use of a quantitative research vocabulary
Problem: A lot of data exists in computers. Reuse is a disaster.
Strategy:
Create vocabulary for quantitative research Door het maken van vocabulary stellen we ontwikkelaars in staat om hogere-niveau- en betrouwbaardere software te maken. OQR:
Scientific reasoning,
Quantities and related concepts
Mathematical concepts
Programming constructs
Computations
Web services and tools Web services omdat we gebruik willen maken van een centraal podium in plaats van lokale podia met alle risico’s van suboptimalisatie vandien.
Evaluate the use of these tools Hoe de tools het leven gemakkelijker maken zit hem vooral in “artificial stupidity”: de ondersteuning neemt de onderzoeker saai, recurring en foutgevoelig werk uit handen.
Lessons learnt:
Support simple, frequent actions
Focus on those who actually need support. Mensen die heel goed thuis zijn in een domein hebben geen hulp nodig, mensen die in meerdere domeinen bezig zijn of minder expertise hebben hebben het wel nodig.
Integrate services in popular tools such as Excel
Therefore we integrate our services in Excel. We develop subroutines that call the web services in an add-in.
Outlook: we will evaluate the use of this add-in with researchers.
General outlook : When more services will be developed and integrated in more tools, we can create a better and semantic computer support of quantitative research step by step.
Uitsmijter: above we focused on the improvement of creating new data rather than upgrading old data. That is another topic that we use our vocabulary and web services for and create a tool for.

22. Semantic Friendly Forms RDFS/OWL functionality in form-based wiki
Now
Semantic MediaWiki enables crowd semantics (and displays)
Semantic Forms facilitates crowd entry (at ~RDF level)
Semantic Friendly Forms: RDFS&OWL-based menus/autocompletion
Entry is quicker and with fewer errors (?)
Process RDFS&OWL for form-based input
Input form value selection
Property selection for class instance input & infobox
Domain, range, cardinality, restrictions, symmetry, …
Questions
Do RDFS&OWL-based menus accelerate crowd entry?
Can crowds engagingly and effectively design ontologies?
What is effective pattern and scenario for use?
JWS special issue on Interaction: deadline April 20th!
Lloyd Rutledge

23. Hans Nederbragt RNA infrastructure / Sterna project web interfaces web
API's
repository
connector
RNA toolset
repository
connector
rdf-store:
rdf-records
metadata
rdf-store:
rdf-records
metadata
rdf-store:
rdf-records
metadata
rdf-store:
reference
structures
data connector
data connector
conversion
collection:
records
unstruct files
collection:
records
unstruct files
legacy
reference
structures
local
applications
local
applications
content and
metadata
reference
structures

24. Sterna project / RNA infrastructure
Launched in 2008, the Sterna project is an eContentplus best practice network that aims to contribute to the further development of the European Digital Library initiative. Sterna’s participants, mostly European institutions that are concerned with collecting and managing content on biodiversity, wildlife and nature in general, join forces to explore new ways of providing their content to the public. The project was initiated by the Netherlands natural history museum Naturalis and major technical contributor Trezorix.
Sterna is short for Semantic web-based Thematic European Reference Network Application. Sterna is also the scientific name for the bird genus of terns. Not coincidentally, because birds are the central theme of Sterna with respect to the content that will be made accessible by project partners via the semantic information network, which is a genuine RNA environment. This content can be any type, from scientific articles and imagery to MP3 files of bird sounds, field recordings and artefacts with bird feathers in them.
Multimodeling: The RNA environment is very flexible with creation and use of different datamodels. Harmonisation of data modeling is focused on the use of common properties, rather than on trying to end up with one common data model.  
Heterogenuous reference structures: In the RNA environment reference structures can accommodate both reference items (skos concepts) and content items (xml and rdf structures). Content items can be based on different data models, they even can combine different data models.
Inferencing: In the RNA environment inferencing is used to create mappings based on schema's, rather than mapping the data itself. Also inferencing can be used on the heterogenuous reference structures to realise interesting modes of findability, but this raises some difficult questions as well.

25. Entity-based data integration
Stefano Bocconi
Entity-based data integration
The concept of identity for entities: is identity between two entities a matter of context?
Entity-based data integration, i.e. in how to integrate different knowledge sources about the same entity. Need for:
handling inconsistencies?
a quality mechanism to discard less trustworthy information in case of conflicts?
Identifier lifecycle: guarantee persistency of identifiers (e.g. duplicate cases)
The domain is scientific publishing (particularly in Biology) and news publishing (event detection).

26. Lightening talks - 3 Jellema Wang, Shenghui Tordai Groenouwe Groth
Siebes
Brussee
Hollink
Oren
Jijkoun
Schreiber

27. STITCH @ CATCH SemanTic Interoperability To Cultural Heritage
Thesaurus alignment techniques (lexical, structural, extensional and using background knowledge)‏
Alignment deployment and evaluation in real-world scenarios (book reindexing and search, thesaurus merging and collection navigation, etc)
Challenges
Heterogeneity
Scalability
Multilingualism
Shenghui Wang

28. Towards a Methodology for Vocabulary Alignment
E-Culture project: Semantic search engine for CH collections and vocabularies
We do not want to create new techniques.
We want to use existing techniques and their combinations.
Select multiple alignment
techniques
Combine for higher recall
Evaluate
Apply disambiguation techniques to improve precision
Anna Tordai

29. Game “SWiFT”: Semantic Web in Fast Translation
Chide Groenouwe, Jan Top, Mark van Assem
Goal: Translate all information in high quality SW representations.
Problem: Not enough knowledge engineers, A.I. is too “stupid”.
Towards Solution: Fostering capability in information creators.
Means: multi-player online game SWiFT?
Case studies: TIFN scientific collaboration (Jan Top et al), wikipedia translation.
Background information: Towards a Constitution Based Game for Fostering Fluency in “Semantic Web Writing”
Sign up to play!

30. Which ones should I trust?
Paul Groth
From pipes.deri.org
From Chris Bizer
Who’s responsible?
How were they produced?
Triples
Which ones should I trust?

31. Ronald Siebes

32. The web is not about anglebrackets
RDF’s layering on top of XML is the single largest obstacle for the adoption of Semantic Web technology.
Mismatch in datatypes:Trees vs. Graphs
Try find a tutorial RDF/XML example which is not a tree
It stimulates bad practices: e.g. URI’s
far to hard to read for human beings, NEED tools
XML causes real practical problems
No unique way to represent an RDF graph as an XML tree
If you need an XML parser anyway, RDF becomes extra burden
Parsing is seriously inefficient or (worse) chokes you XML parser
XML makes RDF unnecessary hard to understand
Obscures triple model
Where does XML stop and RDF begin
Better alternatives exist: please make turtle the official preferred RDF serialisation.
Rogier Brussee

33. MuNCH User Behavior In Audiovisual Archive
Does the thesaurus represent the user queries?
Popular programs over time.
Can we use queries to automatically annotate shots?
Multimedia
Analysis
Semantic
Technologies
Language
Technology
Enrich thesaurus structure
sidewalk – pavement
sand – concrete
pearls – juwelery
fjords – seas
barbecues – picknicks
queens – aristocrats
acupuncture – negotiation 33

34. Large-scale distributed RDF(S) reasoning http://larkc.eu/marvin
Eyal Oren
Large-scale distributed RDF(S) reasoning

35. Web service for text information processing:
Extraction (terms, names, reported speech,…)
Cross-document name normalization and linking
Analysis (compare, track dynamic changes)
Protocols and standards
REST (HTTP POST/GET) + XML
SOAP
RDF/XML (on demand)
Valentin Jijkoun

36. Basic application loop:
Upload your documents (text, html, pdf,…)
Specify processing type
Access the results of processing
Example: what themes played in Dutch news in the past month around Ikea?
Questions/contacts UvA:
Maarten de Rijke
Valentin Jijkoun

37. Guus Schreiber