Advanced Knowledge Technologies at the Midterm: Tools and Methods for the Semantic Web

The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the author’s and shouldn’t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.In a celebrated essay on the new electronic media, Marshall McLuhan wrote in 1962:Our private senses are not closed systems but are endlessly translated into each other in that experience which we call consciousness. Our extended senses, tools, technologies, through the ages, have been closed systems incapable of interplay or collective awareness. Now, in the electric age, the very instantaneous nature of co-existence among our technological instruments has created a crisis quite new in human history. Our extended faculties and senses now constitute a single field of experience which demands that they become collectively conscious. Our technologies, like our private senses, now demand an interplay and ratio that makes rational co-existence possible. As long as our technologies were as slow as the wheel or the alphabet or money, the fact that they were separate, closed systems was socially and psychically supportable. This is not true now when sight and sound and movement are simultaneous and global in extent. (McLuhan 1962, p.5, emphasis in original)Over forty years later, the seamless interplay that McLuhan demanded between our technologies is still barely visible. McLuhan’s predictions of the spread, and increased importance, of electronic media have of course been borne out, and the worlds of business, science and knowledge storage and transfer have been revolutionised. Yet the integration of electronic systems as open systems remains in its infancy.Advanced Knowledge Technologies (AKT) aims to address this problem, to create a view of knowledge and its management across its lifecycle, to research and create the services and technologies that such unification will require. Half way through its sixyear span, the results are beginning to come through, and this paper will explore some of the services, technologies and methodologies that have been developed. We hope to give a sense in this paper of the potential for the next three years, to discuss the insights and lessons learnt in the first phase of the project, to articulate the challenges and issues that remain.The WWW provided the original context that made the AKT approach to knowledge management (KM) possible. AKT was initially proposed in 1999, it brought together an interdisciplinary consortium with the technological breadth and complementarity to create the conditions for a unified approach to knowledge across its lifecycle. The combination of this expertise, and the time and space afforded the consortium by the IRC structure, suggested the opportunity for a concerted effort to develop an approach to advanced knowledge technologies, based on the WWW as a basic infrastructure.The technological context of AKT altered for the better in the short period between the development of the proposal and the beginning of the project itself with the development of the semantic web (SW), which foresaw much more intelligent manipulation and querying of knowledge. The opportunities that the SW provided for e.g., more intelligent retrieval, put AKT in the centre of information technology innovation and knowledge management services; the AKT skill set would clearly be central for the exploitation of those opportunities.The SW, as an extension of the WWW, provides an interesting set of constraints to the knowledge management services AKT tries to provide. As a medium for the semantically-informed coordination of information, it has suggested a number of ways in which the objectives of AKT can be achieved, most obviously through the provision of knowledge management services delivered over the web as opposed to the creation and provision of technologies to manage knowledge.AKT is working on the assumption that many web services will be developed and provided for users. The KM problem in the near future will be one of deciding which services are needed and of coordinating them. Many of these services will be largely or entirely legacies of the WWW, and so the capabilities of the services will vary. As well as providing useful KM services in their own right, AKT will be aiming to exploit this opportunity, by reasoning over services, brokering between them, and providing essential meta-services for SW knowledge service management.Ontologies will be a crucial tool for the SW. The AKT consortium brings a lot of expertise on ontologies together, and ontologies were always going to be a key part of the strategy. All kinds of knowledge sharing and transfer activities will be mediated by ontologies, and ontology management will be an important enabling task. Different applications will need to cope with inconsistent ontologies, or with the problems that will follow the automatic creation of ontologies (e.g. merging of pre-existing ontologies to create a third). Ontology mapping, and the elimination of conflicts of reference, will be important tasks. All of these issues are discussed along with our proposed technologies.Similarly, specifications of tasks will be used for the deployment of knowledge services over the SW, but in general it cannot be expected that in the medium term there will be standards for task (or service) specifications. The brokering metaservices that are envisaged will have to deal with this heterogeneity.The emerging picture of the SW is one of great opportunity but it will not be a wellordered, certain or consistent environment. It will comprise many repositories of legacy data, outdated and inconsistent stores, and requirements for common understandings across divergent formalisms. There is clearly a role for standards to play to bring much of this context together; AKT is playing a significant role in these efforts. But standards take time to emerge, they take political power to enforce, and they have been known to stifle innovation (in the short term). AKT is keen to understand the balance between principled inference and statistical processing of web content. Logical inference on the Web is tough. Complex queries using traditional AI inference methods bring most distributed computer systems to their knees. Do we set up semantically well-behaved areas of the Web? Is any part of the Web in which semantic hygiene prevails interesting enough to reason in? These and many other questions need to be addressed if we are to provide effective knowledge technologies for our content on the web

Hypermedia Learning Objects System - On the Way to a Semantic Educational Web

While eLearning systems become more and more popular in daily education, available applications lack opportunities to structure, annotate and manage their contents in a high-level fashion. General efforts to improve these deficits are taken by initiatives to define rich meta data sets and a semanticWeb layer. In the present paper we introduce Hylos, an online learning system. Hylos is based on a cellular eLearning Object (ELO) information model encapsulating meta data conforming to the LOM standard. Content management is provisioned on this semantic meta data level and allows for variable, dynamically adaptable access structures. Context aware multifunctional links permit a systematic navigation depending on the learners and didactic needs, thereby exploring the capabilities of the semantic web. Hylos is built upon the more general Multimedia Information Repository (MIR) and the MIR adaptive context linking environment (MIRaCLE), its linking extension. MIR is an open system supporting the standards XML, Corba and JNDI. Hylos benefits from manageable information structures, sophisticated access logic and high-level authoring tools like the ELO editor responsible for the semi-manual creation of meta data and WYSIWYG like content editing.Comment: 11 pages, 7 figure

Canonical processes of media production

Creating compelling multimedia presentations is a complex task. It involves the capture of media assets, then editing and authoring these into one or more final presentations. Tools tend to concentrate on a single aspect of media production to reduce the complexity of the interface and to tailor support to a specific task. While these tools are suited to the task they are designed for, very often there is no consideration for input requirements for the next tool down the line. Each tool in the multimedia production chain has the potential for adding semantic annotations to the media asset at hand, describing relevant aspects of the asset and why it is being used for a particular purpose. These annotations need to be included in the information handed over to the next tool in the production chain. We specify inputs and outputs to a number of canonical processes we identify in multimedia production. We do not specify the intricate workings of the processes, but concentrate on the information flow between them. Our claim is that by specifying explicitly the input and output required for processes that occur in widely differing uses of media we can provide the tool-building community with a small set of building blocks that can be supported for semantically aware media production tool

Unified access to media metadata on the web: Towards interoperability using a core vocabulary.

The goal of the W3C's Media Annotation Working Group (MAWG) is to promote interoperability between multimedia metadata formats on the Web. As experienced by everybody, audiovisual data is omnipresent on today's Web. However, different interaction interfaces and especially diverse metadata formats prevent unified search, access, and navigation. MAWG has addressed this issue by developing an interlingua ontology and an associated API. This article discusses the rationale and core concepts of the ontology and API for media resources. The specifications developed by MAWG enable interoperable contextualized and semantic annotation and search, independent of the source metadata format, and connecting multimedia data to the Linked Data cloud. Some demonstrators of such applications are also presented in this article

Unsupervised Visual and Textual Information Fusion in Multimedia Retrieval - A Graph-based Point of View

Multimedia collections are more than ever growing in size and diversity. Effective multimedia retrieval systems are thus critical to access these datasets from the end-user perspective and in a scalable way. We are interested in repositories of image/text multimedia objects and we study multimodal information fusion techniques in the context of content based multimedia information retrieval. We focus on graph based methods which have proven to provide state-of-the-art performances. We particularly examine two of such methods : cross-media similarities and random walk based scores. From a theoretical viewpoint, we propose a unifying graph based framework which encompasses the two aforementioned approaches. Our proposal allows us to highlight the core features one should consider when using a graph based technique for the combination of visual and textual information. We compare cross-media and random walk based results using three different real-world datasets. From a practical standpoint, our extended empirical analysis allow us to provide insights and guidelines about the use of graph based methods for multimodal information fusion in content based multimedia information retrieval.Comment: An extended version of the paper: Visual and Textual Information Fusion in Multimedia Retrieval using Semantic Filtering and Graph based Methods, by J. Ah-Pine, G. Csurka and S. Clinchant, submitted to ACM Transactions on Information System

Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research

Background: In times of exponential data growth in the life sciences, machine-supported approaches are becoming increasingly important and with them the need for FAIR (Findable, Accessible, Interoperable, Reusable) and eScience-compliant data and metadata standards. Ontologies, with their queryable knowledge resources, play an essential role in providing these standards. Unfortunately, biomedical ontologies only provide ontological definitions that answer What is it? questions, but no method-dependent empirical recognition criteria that answer How does it look? questions. Consequently, biomedical ontologies contain knowledge of the underlying ontological nature of structural kinds, but often lack sufficient diagnostic knowledge to unambiguously determine the reference of a term. Results: We argue that this is because ontology terms are usually textually defined and conceived as essentialistic classes, while recognition criteria often require perception-based definitions because perception-based contents more efficiently document and communicate spatial and temporal information—a picture is worth a thousand words. Therefore, diagnostic knowledge often must be conceived as cluster classes or fuzzy sets. Using several examples from anatomy, we point out the importance of diagnostic knowledge in anatomical research and discuss the role of cluster classes and fuzzy sets as concepts of grouping needed in anatomy ontologies in addition to essentialistic classes. In this context, we evaluate the role of the biological type concept and discuss its function as a general container concept for groupings not covered by the essentialistic class concept. Conclusions: We conclude that many recognition criteria can be conceptualized as text-based cluster classes that use terms that are in turn based on perception-based fuzzy set concepts. Finally, we point out that only if biomedical ontologies model also relevant diagnostic knowledge in addition to ontological knowledge, they will fully realize their potential and contribute even more substantially to the establishment of FAIR and eScience-compliant data and metadata standards in the life sciences