“KWARC was!”

• Tagging is used to structure content (e.g. to generate personalized sequences of lecture material)
• Social tagging = collaborative structuring of content
• Tags attach specific information to an object
• Tags are usually keywords
• Tagging creates a common vocabulary, in social tagging this is also referred to as folksonomy
• My suggestions: Towards a more sophisticated (semantic) tagging: i.e. tags are semantic concepts, such as mathematical symbols (e.g. represented in OpenMath or (content) MathML) with commonly agreed on or private definitions, which are stored in Content Dictionaries
• But: Maybe this conflicts with the definitions in the inclusive tagging paper (WSKS) as the authors distinguish between different levels of annotation (from tag to formal metadata). But considering the very general definition of “tagging is attaching specific information to an object”; we might want to include semantics concepts as potential tag-categories.
• We provide a corpus of semantically marked up documents in the OMDoc format and respective workflows which allow the automatic extraction of mathematical symbols (which we want to use as semantic tags). For example, the panta rhei system provides an import for OMDoc during which it extracts all symbols; we simply need to memorizes the relation of symbols and the imported content snippets to provide the respective tags.
• Moreover, I suggest to distinguish two types of semantic tags: acquired symbols and required symbols (prerequisites). Based on our OMDoc markup we can identify which symbols are required for the illustration in a mathematical theory and which symbols are acquired when studying the theory: Required symbols are specified via the OMDoc import-elements (which imports symbols from another mathematical theory) and acquired symbols, which are simply all remaining symbols that are not imported from other mathematical theories. Acquired symbols are defined/ introduced in the given theory.
• Based on the extracted tag, we can visualize tag clouds for each content (e.g. in panta rhei)
• We should also provide a user interface for creating tags:
  • Users can associate symbols to content snippets in the system (in particular to non-semantic content such as the forum, the library entries, manually entered problems — this allows us to use the semantic objects to bring order/structure in the collection of non-semantic content);
  • Users can create new tags (new symbols); this interface needs to be very intuitive, easy, and usable.
  • Maybe we can also allow users to use keywords for tagging: But these are non-semantic tags and should be disntiguished
• Based on tagging-structure we can implement tag-based browsing: Given a tag cloud; the selection of a tag provides (i) all resources tag with this tag and (ii) all users that used this tag; clicking on a resources provides the collection of all tags of this resources and the collection of all users that tag this resource; selecting a user provides all his tags and tagged resources …
• However: the tagging of non-semantic content restricts the granularity of the tags (as we cannot annotate fine granular content inside e.g. a post, we do not have IDs; maybe we have to consider a different annotation approach – e.g. based on xpointer as annotae is doing it); However, for now we neglect the granularity. If a posting annotates a content, we extract the symbols of the annotated area and use them to automatically tag the posting; if the posting links to other content we propagate the tags to this content

Further Readings

• How do others define/ interpret semantic tags? e.g. see [1]; [2]; [3] (German)

Presentation by Przemyslaw Kazienko at the 1st World Summit of the Open Knowledge Society, Athens, 24-26 September 2008. Track: Social & Humanistic Computing for the Knowledge Society: Emerging Technologies and Systems for the Society and Humanity.

Two users participate in common activity related to the certain object with the same/ different role a: e.g. two users comment on the same image. The weights of relations depend on intensity, frequency and quantity. Distinction of different layers of the social network: contact lists, tags, groups, favourites, opinions, multi relational social network. Some layers have rather social (contacts, opinion-author, author-opinion); others have more semantic relations (tags, opinion-opinion relation).

The goal of the system is to recommend people to people. First relations are extracted – building the different layers of the social network (distinction between direct relations (contacts) and object-based relations (tags, opinions, favourites, groups). Based on the layers we create weights for the importance of each layer (consisting personal weight = the user’s individual weight of the user for each layer; and a system weight = aggregation over all users). Afterwards, a social filtering is applied: that is rejection based on the user’s contact lists; rejection of users blocked by the user, damp already viewed users. Rotation mechanism for more random results. Finally, the recommendation is presented to the user. Users are then asked to rate the recommendations.

Presentation by Michal Zemlicka (Charles University Prague; Faculty of Mathematics and Physics; Department of Software Engineering) at the 1st World Summit of the Open Knowledge Society, Athens, 24-26 September 2008. Track: Social & Humanistic Computing for the Knowledge Society: Emerging Technologies and Systems for the Society and Humanity.

Problem: STEM disciplines are very unpopular. Scientists and engineers are usually presented as crazy. Learning STEM is quite hard and requires hard work (and some minimal talent). STEM are taught less than before. Educated and well prepared teachers are required and they are expensive. Labs are expensive. People able to teach STEM can succeed also in other profession (we are lacking STEM specialists).

Approach to change the public opinion: The authors propose to create a system showing how successful alumnies of different school have become. They want to show that the knowledge of mathematicians is an advantage for being employed and having big income (tax statements).

Limits: All technical issues seem to have solved (architecture, certification, encryption, privacy). The main problem is that the use of the existing data is prohibited by law. There will be a powerful lobby of poor schools against such a system.

The authors want to prove the quality of an educational system by using the average success of alumnies. In the discussion it was criticized that “employment” is a rather poor parameter for a complex system such as education and that a more sophisticated approach needs to be taken.

Presentation by Pui-Yee Chiu at the 1st World Summit of the Open Knowledge Society, Athens, 24-26 September 2008. Track: Social & Humanistic Computing for the Knowledge Society: Emerging Technologies and Systems for the Society and Humanity.

see also Do we know how to use facebook?

Our paper on “CoPit – the community of practice toolkit based on semantically marked up artifacts” received one of the best paper awards at the 1st World Summit of the Open Knowledge Society, Athens, 24-26 September 2008: The paper scored 2nd in the track “Social & Humanistic Computing for the Knowledge Society: Emerging Technologies and Systems for the Society and Humanity”.

At the beginning I was confused that there were several best paper awards for each conference track (altogether 10?). But having a broader recognition of contribution, actually allows a much better overview on the conference’s focus and expectations. Below I am listing the best papers of WSKS 2008 (list is not yet complete).

Track: Social & Humanistic Computing for the Knowledge Society: Emerging Technologies and Systems for the Society and Humanity

1. Inclusive Social Tagging
2. CoPit – the community of practice toolkit based on semantically marked up artifacts

Track: Knowledge, Learning, Education, Learning Technologies, and eLearning for the Knowledge Society.

1. Educational Games Design Issues: Motivation and Multimodal Interaction

Proposed Projects:

• Open Research Society (ORS) Journal Collections: new members become associate editors of open journals
• ORS Book Collections
• ORS Learning Resource Collection: Encouraging initiatives for open educational repositories; but provide quality control; e.g. created an ORS lens in Connexions; new members can become members of the assessment board for open learning materials
• ORS University: vision of an open university with open courses

Organization of ORS via open policies, which are documents that contain procedures or principles for the different areas of actions of the ORS. See further information on ors.org (see also their wiki)

See paper “Open research – the ORS way” for the vision of the Open Knowledge Society.

Presentation by Juan M. Santos (University of Vigo, Spain) at the 1st World Summit of the Open Knowledge Society, Athens, 24-26 September 2008. Track: Knowledge, Learning, Education, Learning Technologies, and eLearning for the Knowledge Society.

Traditional search engines are not context-specific and offer limited efficiency in areas like online learning. Consequently, course materials can not be semantically analysed and LMS hide course contents. We need specialized brokers that automatically gather and integrate existing educational efforts. A broker is a knowledge base or expert system. Knowledge base is a repository of information collected and inferred by the broker. Technically, knowledge base consists of the Abox (facts) and Tbox (axioms). The broker is based on the ELEARNING-ONT ontology, defining the concepts and interrelations (course, educational service provider, educational platform, student) based on existing standards DC LOM, IMS ACCMO, CDH, IMS UP, IMS Accessibility for LIP etc (appreciations might be wrong). System as a Service-oriented Architecture (SOA).

Well thought and presented architecture (consisting of several layers and models), Java prototype has been implemented to prove that the identified models can be successfully implemented and used. Personalisation is a key features: For example, administrator can choose between different inference engines and several further components. The authors are now working on the implementation of a “real” brokerage system to publish courses from the three Galician region universities and some involved enterprises. The main problem the face is the lack of a standardized metadata model for the description of courses.

How can we embed a learning theory in the presented architecture? How hard would it be to implement a presentation layer that considers the learners context? The system is not meant for learning.

Presentation by Vlado Glavinic at the 1st World Summit of the Open Knowledge Society, Athens, 24-26 September 2008. Track: Knowledge, Learning, Education, Learning Technologies, and eLearning for the Knowledge Society.

eLearning systems used to be very static (HTML), interactivity increased (ASP, JSP, PHP) and applications based on Service Oriented Architecture (J2EE, .NET, XML) arise. Semantic Web allows cooperative work among agents based on displaying relations among knowledge elements and inference rules on data (OWL, DAML). Web 2.0 focus on cooperation, sharing, and creativity. Both affect the conceptualization of any web-oriented knowledge-based system.

The tool (TEx-Sys) presented arose from an on-site version, to web-oriented version, and finally to a web service based version. Transformation of the intelligent tutoring to the new web generation faces some challenges: heterogeneity of users, information overload, interoperability between systems, heterogeneity of access devices. So we need a new pedagogical paradigm to address the new learning community: So TEx-Sys is transformed into a multi-agent system. Agents should adapt to each students, model a student profile (learning background, previous test results, capabilities, preferences, learning goals …), agents can suggest students a revision of previous lessons, an exercise, a related test. Agent have knowledge on the domain, teaching methods. Agents shall enable communication between users, form interest groups, support cooperative learning etc. System allows teachers to enrich teaching content with further resources, student to download students notes. Agents provide search to reduce information overload of students (based on the student profile). A module of the system allows the use of mobile devices: This allows easy, contextualized, and ubiquitous access to knowledge.

The presented work is a vision paper listing requirements for a suited intelligent tutoring system. A first prototype exists and needs to be adapted to the new web generation, to the new agent-based framework. The knowledge society aims at commercializing ideas of such intelligent systems. Intelligent Agent-based system are still not ready for commercialization;most systems are research prototypes.