IINSEMTIVES

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INSEMTIVESIncentives for Semantics

www.insemtives.euhttp://blog.insemtives.eu

Agenda

1. Idea2. What3. Why 4. How5. Expected outcomes6. Potential impact7. Key facts

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Idea

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Realizing the Semantic Web by encouraging millions of end-users to

create semantic content.

What• Bridging the gap between human and computational

intelligence in semantic content authoring.

• Methodologies, methods, tools for the large-scale creation of semantics– Driven by ideas from incentive theory and participatory

design.– Optimally combine human input and automatic

techniques. • Wide range of content types (text, multimedia, Web

services).• Case studies addressing the most important issues of

semantic content creation projects.04/08/2023 www.insemtives.eu 4

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Why

• More and more information is available on the Web. The information overflow is un-manageable.

• Semantic technologies help to make sense of this huge amount of information.

• BUT: Many tasks related to semantic content creation are human-driven and can not be carried out automatically.

• Limited involvement of users in the Semantic Web.

• Incentive structures are not in place for semantic content authoring.

The Web 2.0 experience• Altruism• Reciprocity (tags: Organisation, Reuse)• Reputation• Competition • Belonging to a community, a common goal• Autonomy, freedom• Attracting attention• Self Portaits (Facebook)• Social Component

(Kuznetsov, 2004; Marlow et al, 2006)The impact of incentives can be exemplified on

popular Web 2.0 platforms

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How

How (cont‘d)

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WPs 5-7

WP4

WP3

WPs 1-2

WP 8 Dissemination & Exploitation

WP 9 Management

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Expected outcomes

• A unified methodology for authoring semantic data.

• Incentive mechanisms for semantic content creation.

• Design guidelines for tools.

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Expected outcomes (cont)

• Models and methods for the creation of lightweight, structured knowledge.– Bootstraping through the

extraction of contextual knowledge.

– Converge of semantics.– Linking semantic content.– Semantic search.

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Expected outcomes (cont)

• A semantic content management platform for the storage and retrieval of user-generated content, including methods for supporting the lifecycle of this content.

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Expected outcomes (cont)

• A toolkit implementing guidelines and incentive mechanisms for ontology development and annotation of different types of media. – Generic games toolkit and

games.– Semi-automatic annotation

tools.– Bootstrapping tools.– Search and navigation tools.

Expected outcomes: an example

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Expected outcomes (cont)

• 3 case studies for evaluation of INSEMTIVES technology in real-world settings.– Different types of

communities of users.– Different types of

information items.– Different types of

semantic content.

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OKEnterprise• In corporate

environments, important information is often lost.

• Okenterprise is a social network for corporate knowledge management in Telefonica.

• We will apply INSEMTIVES technology to this network in order to generate and share new knowledge among co-workers.

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Virtual worlds

• Media producers and companies face the lack of reliable metadata for the huge collections of assets they produce.

• In this case study, we will apply incentive methods to the virtual world “Tiny planets” to semi-automate the creation of descriptive metadata.

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Web service annotation

• The lack of rich descriptions beyond their current syntactical interface hampers the automatic retrieval of Web services on the Internet.

• The case study will apply INSEMTIVES technology to facilitate user-provided annotation of Web services.

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Potential impact

• Massive amounts of useful semantic content which can enable the uptake of semantic technology through the development of application producing real added value for the Semantic Web and for industrial adopters.– Production of digital resources easier and more cost-effective– Enhanced search of digital resources

• Case studies solving real world problems– PGP: multimedia annotation – Seekda: annotation of Web services– Telefonica: semantically enhanced corporate knowledge management

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Potential impact: the same example

• 50 users playing games 24/7 for 6 months.

• 216,000 hours (50 * 24 * 180), hourly rates of 10€ 2 million € investment.

• 4 modeling decisions per 2 minutes game 12 milion modeling decisions in 6 months (216,000 * 60 = 12,960,000).

Key facts

• 2009 – 2012• Budget: 4.3 MEuro• 7 Partners

– STI Innsbruck, University Innsbruck, Austria

– University of Trento, Italy– Peppersghost Productions, UK– Telefonica, Spain– Seekda, Austria– OntoText, Bulgaria– University of Siegen, Germany

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INSEMTIVESIncentives for Semantics

www.insemtives.euhttp://blog.insemtives.eu