- aceMedia has taken the challenge of organising personal content by creating tools to automatically generate metadata descriptors and search content intuitively. In this paper we review part of the approach taken by aceMedia to create semantic metadata (ontologies) and use of this to enable more appropriate search and matching and managing of content. However, there is further benefit the end user can gain from this semantic metadata and this is from adding intelligence in the software. The benefits and how intelligence can be added is described with a particular focus on assisting the user in the creation of privacy preference rules when sharing content: the creation of self-governing inferencing.
Index Terms— semantic metadata, ontologies, intelligent software, inferencing, self-governance. Topic “Integration of multimedia processing and Semantic Web technologies (SS3)”
Tto assist in advanced content management. This is to deal
HE vision of the aceMedia project is to provide the tools with the classic information overload, as users have not just access to content in many forms but also many tools and devices to create all types of content themselves. Management of content becomes increasingly difficult such as finding the right content, creating collections, annotating content etc.
aceMedia is researching methods to assist in information and content management via knowledge technologies and developments in the semantic web. The aceMedia approach involves creating and using metadata to enable intelligent applications such as advanced search and retrieval, personalisation, self-organisation of content, and autonomous content actions such as self-determined privacy. The use of metadata does not come without some key challenges itself. Many terms used within the metadata may refer to an implicit informal semantics and do not necessarily provide essential properties or relationships between terms to assist in any automated approach to be applied. However, the move towards the development of ontologies that model domains, preferences, policies and profiles provide an approach to assist in automating the matching and filtering of content searches. aceMedia uses the metadata and ontologies to provide context to assist in content management on behalf of the user. The metadata and ontologies are used to: 1. Enable the user to search for content: the user can enter natural language sentences that are parsed to provide a structured query for the knowledge base. 2. Enable personalisation methods to create and manage personal metadata to be applied to the search and retrieval of appropriate content, in particular ranking content, which is based on using machine learning techniques to weight preferences and content. 3. Enable visual methods, which use intelligent multimedia algorithms, through the low level descriptors, to assist the user in matching similar visual properties of particular content with other content.
As well as the above functionality to assist the user in content management, aceMedia has researched into the requirement and application of the intelligent layer, as part of the Autonomous Content Entity (ACE). An ACE is a concept which captures the content, metadata and the intelligence layer as a type of intelligent media object. There are two specific drivers that require the intelligence layer: 1. Digital Content is very nomadic; in that context it is better to have content management attached to the content such that the user can always optimally deal with the content, wherever it resides 2. Digital content can easily flow to other places where the owner of the content does not have control over this content; by carrying the rules of management with the content then the owner’s rights and privacy preferences are enabled.
The Intelligence Layer is defined as code executing in an aceMedia system that provides “intelligence” to autonomously support content management. This for example means how to present itself, maintain and enhance the metadata, handle privacy, self-adapt and self-organize etc. The intelligence layer can be transported together with the content and metadata as one object and the execution of the intelligence layer is done in a secure environment.
There are two specific applications which make use of the intelligence layer: personal content ownership rights to support personal preferences about privacy of content through self-governance and self-organising content to assist in the automation of content collections dependent on the devices and environments. The self-governance context is built on assisting the user with a means to declare their access preferences to their content. This in terms of concepts is close to digital access rights, so access preferences available to the user are captured in terms of digital rights access attributes.
The aceMedia system facilitates digital multimedia content
management through its software framework that enables the
execution of application modules and ACEs. The aceMedia
framework [
The intelligence layer provides a framework where code (rules, methods and inferencing techniques) can exploit the semantic structure of the explicit knowledge about the content (multimedia content plus metadata descriptors, users, devices and domains).
The original concept of the ACE was about creating a mechanism that would assist in the users in managing their content and enable designers and developers methods to support this managing of content while creating flexibility for: a) tailoring solutions to the trends (ease of configuration) and b) extensibility for future unknown development.
In the user studies done by the aceMedia project there were
indications that the users had concerns and wanted to have
some control over their content and have certain checks about
how their personal content is shared and used [
We capture the user’s privacy preference in a way that does
not demand constant attention from the user but does capture
the intended behaviour from the user by using a policy model
[
Some degree of self-governance of a computational system
is required if there is a need to support privacy
preferencebased access to content when content leaves the control of the
owner. Here we express a degree of self-governance and the
self-governance of a system is required to be grounded in
some knowledge and facts about itself. Although theoretically
computational self-governance could be infinite, for example,
if the architecture incorporates a reflective tower principle [
We are now in the first stages of including into the aceMedia framework computational intelligence. The use of semantic metadata has provided some of the key cues for enabling context-aware computations that provide computational intelligence functionality. It has provided the semantic structure and grounding knowledge that is required when performing intelligent behaviours.
The use of autonomy was drawn from the autonomic computing of defining a notion of “self”. This is supported by the aceMedia system: 1) the aceMedia framework enables a concept of “code” within an ACE itself, 2) the semantic metadata provide some context that can benefit and be benefited by some form of computational intelligence, and 3) the inclusion of the self-governing inference rules.
In providing the self-governing inference rules we have added to the semantic metadata and have incorporated more context cues through the use of policies that capture a user’s privacy preferences. The semantic metadata has meant we can define an inferencing system to build both the appropriate privacy preference context and rules that can be attached to the content creating an intelligence layer as part of the content.