So far 143 people whose organization already make use of controlled vocabularies answered the question “Which kind of controlled vocabulary do you use or plan to use in your applications?”.
The results so far show that lightweight models like taxonomies and thesauri are somewhat preferred over ontologies:

Taxonomies are the favorite, as 73.6% of participants use or plan to use them, followed by thesauri (62%) and ontologies (61.2%), while simple glossaries lag considerably behind with a usage of 31.4%.

This survey will close in about a week, so please take this chance to make your opinions on this topic count! You can find the questions here, it will take 5-10 minutes to answer them.

All participants will gain access to a report with the results within the following month. The most interesting results will be made public on this blog.

How to model divergent views on one concept?

Such a central model is not only much harder to manage, but there is also the general problem that differerent departments like marketing, quality assurance, R&D, etc. will have divergent views on the model and its concepts. These different perspectives on one and the same concept are hard to unify in a single model.

Think of a company that sells mobile phones and wants to create a model of its line of products. It wants to utilize this model in the context of its online shop as well as in the context of its user support forum. While the structure of the model (i.e. the relationships between the products) might be very similar or the same in both contexts, there will be differences in which properties of the products are actually relevant in the respective contexts.

In the model of the marketing department there might be a concept for a “Phantastax StamiMaxx” cell phone with a definiton “The StamiMaxx has a powerful battery and is great for professionals who travel a lot”. They might relate it to manufacturer “ACME Corporation” and to several concepts representing different features like “Android OS”, “Multi-touch touchscreen”, etc.
The very same phone has different properties that are interesting from the Quality Assurance departement’s perspective. They might call it by a more specific name like “Phantastax i3000 StamiMaxx S”, have a different definition for it like “3G cell phone implementing the new WTF3000 protocol, …” and relate it to concepts representing known problems and their solutions.

Now they face the task to integrate these different models, as it is not desirable to use a bunch of isolated models within one company.

Support of collaborative work on distributed models

To support this kind of collaborative work on distributed knowledge models, we would like to link the concepts of the models, just as is we link documents in the World Wide Web. Fortunately the Simple Knowledge Organisation System (SKOS) offers mapping properties that can be used to define relationships between concepts from different knowledge models.

E.g. when we want to say that concept “Phantastax StamiMaxx” in the product line thesaurus refers to the same real world entity as concept “Phantastax i3000 StamiMaxx S” in the Quality Assurance thesaurus, then we can use skos:exactMatch to express that. If we want to express that the concepts are merly similar, skos:closeMatch could be used.

The other SKOS mapping properties express a hierarchical (narrowMatch, broadMatch) or an associative (relatedMatch) mapping relation between concepts from different concept schemes. With those we can say that my Samsung Galaxy concept has a skos:broadMatch “Smartphone” in the product line vocabulary and a skos:relatedMatch “ACME Corporation” in a controlled vocabulary about Tech companies.

Modularisation of knowledge models

In this way SKOS thesaurus management systems like PoolParty make it possible to modularise knowledge models, represent concepts in their different contexts and consequently enable collaborative work on those models: The marketing guy can work on his model with the concept properties focused on sales without disrupting the work of the quality assurance expert on her own thesaurus. Later one or both of them can create the skos:exactMatch link between the concepts that are the same, like seen in the “Exact Matching Concepts” box in screenshot of PoolParty below.

Enrich your knowledge: Get connected with the LOD Cloud

Going a step further the models could be connected to external knowledge, e.g. a source from the Linked Open Data (LOD) Cloud. Once we establish links to LOD hubs like DBpedia, we can import additional information for their concepts or use it to establish whether similar concepts from different models really refer to the same real world resource.

KiWi, the Open Source development platform for building Semantic Social Media Applications, offers features required for Social Media applications such as versioning, (semantic) tagging, rich text editing, easy linking, rating and commenting, as well as advanced “smart” services such as recommendations, rule-based reasoning, information extraction, intelligent search and querying, a sophisticated social reputation system, vocabulary management, and rich visualisation.

Purpose

Poolparty was conceived to facilitate various applications like

• Semantic search engines
• Recommender systems (similarity search)
• Corporate bookmarking
• Annotation- & tag recommender systems
• Autocomplete services and facetted browsing.

These use cases can be either achieved by using PoolParty stand-alone or by integrating it with existing Enterprise Search Engines and Document Management Systems or Enterprise Wikis.

Thesaurus Management

PoolParty is aiming to be easy to use for people without a strong Semantic Web background or special technical skills. The GUI is entirely web-based and utilizes AJAX so the user can e.g. quickly merge two concepts via drag & drop. An overview over the thesaurus can be gained with a tree or a graph view on the concepts.

PoolParty also helps to semi-automatically add concepts to a thesaurus as it can be used to analyse documents (e.g. web pages or PDF files) relevant to a thesaurus’ domain in order to glean candidate terms. This is done by the key-phrase extractor of KEA. The extracted terms can be selected by the user, thereby becoming “free concepts” which later can be integrated into the thesaurus, turning them into “approved concepts”.

Documents can be searched in various ways – either by keyword search in the full text, by searching for their tags or by semantic search and similarity search. The latter takes not only a concept’s preferred label into account, but also its synonyms and the labels of its related concepts are considered in the search. The user might manually remove query terms used in semantic search. Boost values for the various relations considered in semantic search may also be adjusted. In the same way the recommendation mechanism for document similarity calculation works.

PoolParty by default also publishes a Semantic Wiki version of its thesauri, which provides an alternative way to browse and edit concepts. Through this feature anyone can get read access to a thesaurus, and optionally also edit, add or delete labels of concepts. Search and autocomplete functions are available here as well. The Wiki’s XHTML source is also enriched with RDFa, thereby exposing all RDF metadata associated with a concept to be picked up by RDF search engines and crawlers. (See two examples: Cocktail thesaurus &  Standard Thesaurus for Economics)

PoolParty also supports the import of thesauri in SKOS (including several consistency checks) or Zthes format. Those functionalities can also be consumed as stand-alone web services via PoolParty SKOS Services. Additionaly, lists of concepts and their labels can also be imported via CSV files.

Linked (Open) Data

PoolParty not only publishes its thesauri as Linked Open Data (in addition to a SPARQL endpoint), but it also consumes LOD in order to expand thesauri with information from LOD sources.

Concepts in the thesaurus can be linked to e.g. DBpedia  via a service like Georgi Kobilarov‘s DBpedia lookup service, which takes the label of a concept and returns possible matching candidates. The system suggests relevant resources from DBpedia and the user can select the one that matches the concept from his thesaurus, thereby creating a skos:exactMatch relation between the concept URI in PoolParty and the DBpedia URI. The same approach can be used to link to other SKOS thesauri available as Linked Data.

Other triples can also be retrieved from the target data source, e.g. the DBpedia abstract can become a skos:definition and geographical coordinates can be imported and be used to display the location of a concept on the map, where appropriate. The DBpedia category information may also be used to retrieve additional concepts of that category as siblings of the concept in focus, in order to populate the thesaurus.

PoolParty is capable of importing a SKOS thesaurus from a Linked Data server, and may also receive updates to thesauri imported this way. This feature has been implemented in the course of the KiWi  project funded by the European Commission. KiWi also contains SKOS thesauri and exposes them as LOD. Both systems can read a thesaurus via the other’s LOD interfaces and may write it to their own store. This is facilitated by special Linked Data URIs that return e.g. all the top-concepts of a thesaurus, with pointers to the URIs of their narrower concepts, which allow other systems to retrieve a complete thesaurus through iterative dereferencing of concept URIs.

Additionally KiWi and PoolParty publish lists of concepts created, modified, merged or deleted within user specified time-frames. With this information the systems can learn about updates to one of their thesauri in an external system. They then can compare the versions of concepts in both stores and may write according updates to their own store.

This means each system decides autonomously which data it accepts and there is no risk of a system pushing data that might lead to inconsistencies into an external store. Data transfer and communication are achieved using REST/HTTP, no other protocols or middleware are necessary. Also no rights management for each external systems is needed, which otherwise would have to be configured separately for each source.

Technology

The software is written in Java and utilizes the SAIL API, so it can be used with various triple stores. The thesaurus management itself (viewing, creating and editing SKOS concepts and their relationships) can be done in an AJAX Frontend based on Yahoo User Interface (YUI). Editing of labels can alternatively be done in a Wiki style HTML frontend. For key-phrase extraction from documents PoolParty uses a modified version of the KEA 5 API, which is extended for the use of controlled vocabularies stored in a SAIL Repository (this module is available under GNU GPL). The analysed documents can be stored and indexed in Lucene/Solr or any other (enterprise) search system along with extracted and semantically related concepts.

In order to allow for a high level of interaction, the attendance is limited to ten participants and ample time for questions and discussion with our experts is designated. Each webinar works as a stand-alone module, so you can pick and choose some of them or book the whole series of 6 webinars.

We’ll kick off with a session about Semantic Wikis on Thursday 22nd of October. A German language version will be held at 9 a.m., alternatively you can atted an English version at 6 p.m. CET.

Each Thursday we cover a different topic such as Semantic Search, Corporate Thesaurus Management, Text Mining on the Corporate Semantic Web, Linking Open Data and Semantic Advertising.

In order to participate you only need broadband access to the internet, Windows or a Mac and a fairly up-to-date browser. For detailed system requirement see the webinar overview.

We hope to talk to you in one or more of these sessions!

My basic idea was very simple. When you read about the Semantic Web you are confronted right away with connections to creating knowledge and knowledge management. But in my understanding the Semantic Web is a technical thing and knowledge management is primarily a cultural and organisational thing. So the research questions for my thesis where:

• What relevance do knowledge management and semantic technologies have in the daily work of people working in knowledge intensive domains?
• Which possibilities lie in the adoption of knowledge management and semantic technologies?
• Are semantic technologies already fit for practical use?

The basis of the empirical part of my thesis are group discussions held in different organisations. As a result I developed starting points for an understanding of the topics “Knowledge Management” and “Semantic Web” and their relevance in organisations. The empirical results, in short, provide the following answers to the research questions:

• The “theoretical relevance” of both topics is high, the “practical relevance” on the other hand is rather low. Neither do structured concepts for knowledge management exist in the studied organisations, nor are there attempts at using semantic technologies
• Most of the participants have not heard of the “semantic web” prior to the discussions. After having been introduced to the topic, the relevance of the semantic web and of semantic technologies is rated high
• Possibilities are seen in a better management of information or knowledge in organisations and, especially for semantic technologies, in the improvement of search functionality’s and search results
• Semantic technologies are not yet seen as fit for practical use
• The connection between knowledge management and semantic web is taken as a fact without giving any justification for it.

In my conclusion I tried to match my results with the results of the Semantic Web Barometer 2009 and it was very interesting for me, that there were several similarities. I also found that talking to the people that have to work with technologies that are developed for them can be quite interesting and that group discussion are a great way to do that.

I wrote most parts of my diploma thesis in a wiki (and the rest is available as PDF) so you can find it on my wiki.

Your comments and annotations are very welcome!

Thanks for reading as far as this, Helmut

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Luckily, times have come, where semantic search techniques have found their way to enhance knowledge providing theme portals. Nearly once a week a new knowledge portal with built-in semantic search pops up. They deal with environmental issues, health care, economy etc. These sites are good examples how the vision of a knowledge web is fostered by semantic technologies. Such focused approaches are great showcases for “a” semantic web (even if they are not based on “the” RDF semantic web) in the next few months besides general knowledge portals like Wolfram Alpha.

But the potential of these semantic theme portals is often reduced essentially by their bad usability. You get lost in categories and flags – you get puzzled by pulldowns, mouseovers and embedded hierachies – it’s sometimes a mess out off 1001 functions. You need to understand the underpinning semantic concept to get oriented within these applications – and this is not the goal of the exercise. Search has to be easy.

To show the potential of semantic technologies, we need good examples, which offer good usability. This is a call to everyone to provide such examples.

See my favorites:

• NextBio, a platform that enables life science researchers to search, discover, and share knowledge locked within public and proprietary data
• reegle, the Search Engine for Renewable Energy and Energy Efficiency
• CultureSampo, a Finnish cultural heritage platform for institutional organizations as well as private citizens

Markus Luczak-Rösch and his team from FU Berlin have published loomp, a WYSIWYG annotation tool especially designed for inhouse use. loomp is aiming at the Corporate Semantic Web market, providing a semantic application with low entry barriers and high usability designed for non-techies.

When asked about the concrete application area Markus says:

We have found various use cases especially in knowledge and content intense domains. The most interesting one is the journalists use case. Consider journalists which research and write articles and editors which revise and publish the work of journalists.

Journalists research specific topics on demand and access various information sources for this purpose, e.g. websites, books, related articles, and human informants. Only few journalists use digital devices for this task and even fewer apply information management systems. To transfer the finished article to the responsible editor at the publishing house the people use free text documents and email communication. Finally, an editor revises and releases the articles for his department. loomp can help journalists to manage their notes, interview logs, references, addresses, etc. loomp helps to link an article to its information sources.

Read the full interview here.

Sebastian Schaffert and his team demonstrated the latest version of this semantic based framework based on wiki principles and built on JBoss Seam.
You can take a look at the online showcase and download the one click installer of the pre-release.
Sebastian emphasised that KiWi will follow Linus Torvald’s maxim of releasing early and releasing often.
In June 2009 KiWi 1.0 should be ready, followed by 1.5 in December 2009, at which time Enabling Technologies and a first implementation of the uses cases will be included in the system.

After hearing talks about the KiWi User experience, data model and transaction management, we learned about the status of reasoning, querying, information extraction and personalisation of the Enabling Technologies groups (online slides forthcoming here).

Peter Reiser presented the Sun use case, in which the focus now is on realising an expert finder mechanism based on the “Community Equity” concept found in Sun Spaces (their highly popular, heavily customized version of Confluence).

In short Community Equity is a system for analysing the social activities in a community and measuring the value of the contributions to the community. Social activities are anything from creating content to simply viewing it. These activities are used to calculate the Community Equity (which is simply a number) of content, tags and people.
Consider this example for a content page: The more people view, download, reuse, comment on or rated the page positively, the higher the page’s Information Equity will be.
In turn the community members acquire Contribution Equity through the content items they create, i. e. the Information Equity of a content item “spills over” to its creator.
The same goes for Tag Equity: Each tag obtains the Equity from all the pages it is applied to. E.g. if there are 3 pages with the tag “JBoss” with Information equity of 10, 5 and 20, then the Tag Equity of JBoss is 35.
These things alone is very helpful for motivating people to contribute to the community and for judging the quality of content and ranking it accordingly.

On top of that, the Equity system allows for a expert finder system. People are related to all the tags that are used on the content items they created. Imagine a contributor has created several documents that were tagged with java and the sum of information equity of those pages is 550, then the person also has
That way a search for “Java” doesn’t only bring documents tagged with java, but also people with expertise in Java.
In KiWi this Community Equity system will be implemented and extended. For one, instead of flat tags KiWi will use concepts coming from SKOS thesauri, which will be managed using PoolParty.
These thesauri act as a shared knowledge model. In this way synonyms, parent/child concept relationships, etc. can be considered for Equity calculation, therby taking personalization, querying and expert finding to a whole new level.
Research will engage with questions like how should the Equity disperse through the graph: Imagine a community member with high Equity in “JBoss”. This means she probably has good expertise in Java too. As this subconcept relationship is expressed in the thesaurus, it is possible to transfer Equity from JBoss to Java, but one has to consider what percentage the equity will be transferred, if Equity only can only spread upwards from subconcept to parent concept or whether other kinds of relationships also warrant the transfer of some Equity.