Wednesday Apr 06, 2011

How to apply Semantic Web in Enterprises


Over the last three years, Oracle/Sun participated in a research project called Kiwi which ended in March 2011.
KiWi is an open-source development platform for building metadata-driven Semantic Social Media and Social Networking application and is part funded by the European Union under the European Union 7th Framework Programme.

We already had extensive experience on how to implement large scale Enterprise communities trough the implementation of our global social community framework called SunSpace and Community Equity which was used by over 30'000 Sun employees.

Oracle's (Sun) role in the Kiwi project was to validate if and how Social Semantic technologies can be used in large enterprises.


I would  like to thank Josef Holy and Jiri Kopsa for their excellent technical contribution to this project. A large portion of the content of this blog post(s) are excerpts from Kiwi project publications written by Josef, Jiri and myself.

Use Case: Enterprise Metadata Management

Our main goal for the Enterprise Metadata Management was to design, develop and deploy technologies and practices suitable for managing user-defined folksonomies and controlled vocabularies together.

Folksonomies and Taxonomies in the Enterprise

A folksonomy is a system of classification derived from the practice and method of collaboratively creating and managing tags to annotate and categorize content - simply put it, is a collection of tags created by users in the context of one or more content management systems. Folksonomies are usually associated with Web 2.0 services, which allow masses of users to create and annotate content (photos, videos, blog posts, etc.) freely, in an open manner.

In the enterprise environment, controlled vocabulary is a system of record for naming various things and concepts related to the company business - a typical example of it would be a 'Product vocabulary', containing a list of all official names for all products produced and sold by the company. Such vocabulary is usually defined centrally, in a top-down manner, by a responsible department or a by group of individuals, as opposed to folksonomies, which are defined by 'the wisdom of crowd' with nobody clearly responsible for their creation and maintenance.

If products in the Product vocabulary were put into appropriate categories, they would make up a simple example of 'Product taxonomy'. Compared to simple (flat) vocabularies, taxonomies represent richer structures, allowing hierarchical categorization of things contained inside them. When built, such hierarchies form structures of trees, each having a single root node representing the top-most (most general) concept in the hierarchy.

Managing folksonomies and taxonomies together means finding the right balance between the two worlds - openness and freedom on one side, with responsibility and control on the other.

Use Case implementation

We focused our evaluation on a typical Metadata lifecycle model which is composed of the following three states:


Apply Metadata
Regular user annotates some content item (document) with yet non-existing tag - such a tag is called a free tag, it has no codified meaning and thus belongs to the unstructured folksonomy. It can be freely reused by other users of the system. Reuse of tags is enforced by tag recommender UI, which recommends users with already existing tags.

Manage Metadata
User responsible for metadata management within the system evaluates newly created free tag and if appropriate, turns it into controlled concept which is described with richer information - it is assigned with various types of labels (f.e. synonyms in different languages) and it is put to the appropriate place within one or more taxonomy hierarchies.

Exploit Metadata
Now controlled tag becomes part of the controlled metadata space, which is used also for enhancing other content management system services, such as the Personalized Semantic Search.

Apply Metadata - Tag Recommender/Information Extraction

The goal of this use case was to allow users to tag both free and controlled tags within one UI while providing them with advanced tag recommending/suggesting functionality. KiWi platform was extended with a set of light-weight (JSON) web service endpoints, serving dedicated UI widget component which was designed and developed using standardized web technologies (HTML, CSS, JavaScript).
The Natural Language based text extraction Kiwi service was extend to recognize Oracle taxonomies (controlled tags). This allows invocation of the Information Extraction functionality right from the tagging UI. The invocation returns a list of free tags and controlled tags extracted from the document and let's the user to apply them.

Usability of the developed widget and tagging process was evaluated in an internal usability study with technical and non-technical users. The usability study covered also the information extraction functionality, which additionally went through separate internal evaluation, resulting in several requests for enhancements.

Key Findings

  • Usability study has shown, that the concept of free and controlled tags is understandable for new users. Users highly valued the implemented tag recommender UI, which allowed them to navigate through taxonomy hierarchies easily.
  • It is possible to implement more advanced taxonomy modeling, allowing for example the system-wide definition of business rules for required taxonomies and also support for the taxonomy prefixes.
  • Natural Language based Information Extraction combined with Semantic Taxonomies is very promising - specially the continuos improved tag suggestion results through the "self learning" capability of the system.

Manage Metadata - Concept Model Management

KiWi platform was deployed and integrated together with PoolParty, a commercial thesaurus management product,  using a set of Linked Data interfaces.
Both systems were filled with data from the internal legacy systems. 19 Sun and Oracle taxonomies with almost 6000 concepts in total were created.

The whole solution was evaluated by dedicated expert(s) contextually, during creation of the above mentioned taxonomies. The solution was also evaluated in the set of internal evaluation sessions with subject matter experts from various departments.

Key Findings

  • The envisioned goal to implement, deploy and test solution for merging bottom-up and top-down metadata management practices was successfully met using KiWi platform and PoolParty taxonomy management tool
  • Resulting metadata structures (hierarchies) can be used to provide enhanced metadata suggestions to the system users
  • Allowing users to navigate through individual taxonomies and to apply concepts from these taxonomies along with folksonomy tags helps to improve structure and consistency of metadata in the enterprise content management systems
  • Essential factors for implementing effective open metadata governance models within large enterprises are:
    • Management support- successful implementation of metadata governance requires substantial change in various content management processes within organization. These changes are impossible without clear leadership and guidance provided by responsible organization leaders.
    • The involvement of appropriate subject matter experts - in order to achieve one its main goals - proper structuring of organizational knowledge models - the direct (community) involvement of appropriate subject matter experts is needed.
    • Measuring quality and relevance within open collaborative systems - in collaboration systems with low barrier for participation (e.g. wikis) it is important to have the ability to measure the quality and relevance. For that reason, the Community Equity system was successfully integrated with the KiWi platform.
  • Although difficult to calculate precisely, the cost of newly created controlled tag (taxonomy concept) can be measured based on the time needed for:
    • resolving the free tag meaning, which is often accompanied by costs of communication with tag author or with one or more subject matter experts.
    • placing the concept into the appropriate taxonomy.

Exploit Metadata - Search/Browse Use Case Summary

The goal was to verify the usability and accuracy of the search results. Subject matter experts performed a set of search queries and compared the results against the internal search.

Key Findings

  • Taxonomy based synonym matching is efficient and improves the search results
  • Personalized search based on Social Analytics algorithms looks very promising
  • The faceted search functionality is highly configurable using RDF facets and superior to the existing search functionality


The implementation of the Oracle/Sun use cases in the KiWi/PoolParty system has been very useful. The development of the Metadata Management Process and its application in the environment of controlled taxonomies and folksonomies was significant. We learned how to optimize our metadata management processes, how to technically implement such service and how to improve the user interaction. The application of natural language processing combined with semantic technologies has also improved the quality of metadata. Since people naturally use different labels for same things, it is essential to relate multiple synonyms (implemented as alternative or hidden labels) to taxonomy concepts.

We have also explored the requirements of the system to the organizational structure, resourcing and processes and proved viability of the system in the existing enterprise. Furthermore, we have concluded that the system is sufficiently extensible by implementing extensions in the Oracle use cases. Specifically, customized tagging user interface realizing the custom concept of taxonomy prefixes has been integrated.

Tuesday Jan 25, 2011

Enterprise 2.0 Use Cases for Semantic Web

Last Friday I head the pleasure to present at the 8th Berlin Semantic Web Meetup about our work around Semantic Web and the Kiwi project - an EU founded research project for Social Semantic Web applications. Here is the presentation which explains the real live Enterprise 2.0 use cases Sun Microsystems (Oracle) is using to validate the "Power of the Social Semantic Web Platform called Kiwi. Enjoy
Enterprise 2.0 Use Cases for Semantic Web/Kiwi
View more presentations from Peter Reiser.

Sunday Dec 19, 2010

Enterprise 2.0 Use Cases for Semantic Web

How can an enterprise improve the efficiency and effectiveness of their Knowledge and Community model leveraging semantic technologies and social networking dynamics ?

This was the main question why Sun (Oracle) joint the Kiwi project in 2008.

KiWi is an open-source development platform for building metadata-driven Semantic Social Media and Social Networking application for the Internet and for the Enterprise and is part funded by the European Union under the European Union 7th Framework Programme.

We already had extensive experience on how to implement large scale Enterprise communties trough the implementation of our global social community framework called SunSpace and Community Equity which was used by over 30'000 Sun employees.

Oracle's (Sun) role in the Kiwi project is to validate if and how Social Semantic technologies can be used in large enterprises.

We structured the validation around three main use cases

  1. Manage Metadata - How can we improve our efficiency in better managing corporate Ontologies/Categories and at the same time leverage the power of tagging/folksonomy?
  2. Apply Metadata - How can we improve the quality and quantity of metadata?
  3. Exploit Metadata - How can we improve the effectiveness of enterprise search and expertize discovery by leveraging semantic technologies and metadata?


and voila - we had the Enterprise 2.0 use cases for Kiwi :-) .

1 . Manage Metadata


  • Manage corporate taxonomies/categories and tags (folksonomy) using an Ontology Management System
  • Maintain Oracle+Sun taxonomies and relate them together where possible
  • Investigate Bottom-up (folksonomy) +Top-down (Enterprise Ontology management) governance models


2. Apply Metadata


  • Improve existing tag recommendations with semantic-based technologies
  • Recommend tags based on:
    • Analysis of the document
    • Relationships between tags
    • User's context (Personalization)
    • Tag Equity Value
  • Comply with Sun/Oracle annotation practices
    • Require usage of controlled taxonomies
    • Merge with free tags (concepts)

3. Exploit Metadata


  • Allow user to navigate through the content based on pre-defined taxonomies
  • Faceted search
  • Discover Experts based on their contributions, social value of the content and social networking context
  • Personalize search results and recommendations of content, communities, people and metadata/tags

In the next couple of weeks I will publish a series of blog post about the implementations and findings of the three use cases.

Wednesday Oct 06, 2010

Kiwi -Semantic Web Open Source Release 1.0

  • an open-source development platform for building Semantic Social Media applications
  • Part founded by the European Union FP 7 program.

  • Sun (Oracle) is participating in the project.

There will be a press conference followed

by a release party on
Thursday, October 14 ยท 6:30pm - 11:30pm at the Planetarium Vienna, (map )
  • Open Source Evangelist Ross Gardler (Vice President Community, Apache Software Foundation) - "Open Source Solutions a Source of Inspiration"
  • David Ayers (Free Software Foundation Europe) - "Free Software, why and how it matters"
  • Launch of the first Semantic Social Development Platform - KiWi
  • Awarding of "KiWi Snow Camp Tickets" to developers at the Party
  • Drinks and nibbles - music and visuals - nerds and other nice folk to meet

If you want to think, code, talk, drink and eat KiWi while staring at the stars then join the Kiwi team on this event .



cu you in Vienna :-)

Tuesday Mar 18, 2008

Semantic Web Mashup: Wikipedia-Open_Calais-Goggle-Amazon

Do you want to see the power of Semantic Web  mashup services  ?

Then check this out:

This is an experimental mashup service from the Semantic Web Company (Credit goes to Rene Kapusta)

What does it do ?

This prototype  shows how to use semantic based  term extraction and the Amazon API to search for relevant books for a specific topic.

How it works

  1. The search input  is send to
  2. The respective wikipedia page is sent to the Open Calais service to extract the terms
  3. The extracted terms are sent to google, and get enriched by related terms using the google labs service "google suggest".
  4. and last but not least the  terms are sent to the Amazon API and the relevant books from Amazon are displayed

Pretty cool !

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Wednesday Mar 12, 2008

EU Kiwi project Semantic Wiki kickoff meeting

I am  attending the EU Kiwi project kickoff meeting in Salzburg Austria

The project KIWI is concerned with knowledge management in Semantic Wikis and funded by the European Commission under the Project Number 211932 in the EU Seventh Framework Programme (FP7). KIWI’s objective is to investigate how knowledge management in highly dynamic environments can be supported using Semantic Wiki technologies, and how Semantic Wikis can be improved to satisfy the requirements of knowledge management. For this purpose, KIWI will

    \* implement an advanced knowledge management system based on the Semantic Wiki IkeWiki and extend it by improved, rule-based reasoning support, information extraction, personalisation, and advanced visualisations and editors
    \* verify the system on two use cases in the areas of project knowledge management and software knowledge management, with flexible workflow models and specific support for the respective application areas.

More information can be found here

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