Recommender Systems for the Semantic Web

This paper presents a semantic approach to Recommender Systems (RS), to exploit available contextual information about both the items to be recommended and the recommendation process, in an attempt to overcome some of the shortcomings of traditional RS implementations. An ontology is used as a backbone to the system in the proposed architecture to represent the problem domain, while multiple web services are orchestrated to compose a suitable recommendation model, matching the current recommendation context at run-time. In order to allow for such dynamic behaviour, the proposed system tackles the recommendation problem by applying existing RS techniques on three different levels: the selection of appropriate sets of features, recommendation model and recommendable items

Multimedia Markup Tools for OpenKnowledge

OpenKnowledge is a peer-to-peer system for sharing knowledge and is driven by interaction models that give the necessary context for mapping of ontological knowledge fragments necessary for the interaction to take place. The OpenKnowledge system is agnostic to any specific data formats that are used in the interactions, relying on ontology mapping techniques for shimming the messages. The potentially large search space for matching ontologies is reduced by the shared context of the interaction. In this paper we investigate what this means for multimedia data on the OpenKnowledge network by discussing how an existing application that provides multimedia annotation (the Semantic Logger) can be migrated into the OpenKnowledge domain

Developing Prognosis Tools to Identify Learning Difficulties in Children Using Machine Learning Technologies

The Mental Attributes Profiling System was developed in 2002 (Laouris and Makris, Proceedings of multilingual & cross-cultural perspectives on Dyslexia, Omni Shoreham Hotel, Washington, D.C, 2002), to provide a multimodal evaluation of the learning potential and abilities of young children’s brains. The method is based on the assessment of non-verbal abilities using video-like interfaces and was compared to more established methodologies in (Papadopoulos, Laouris, Makris, Proceedings of IDA 54th annual conference, San Diego, 2003), such as the Wechsler Intelligence Scale for Children (Watkins et al., Psychol Sch 34(4):309–319, 1997). To do so, various tests have been applied to a population of 134 children aged 7–12 years old. This paper addresses the issue of identifying a minimal set of variables that are able to accurately predict the learning abilities of a given child. The use of Machine Learning technologies to do this provides the advantage of making no prior assumptions about the nature of the data and eliminating natural bias associated with data processing carried out by humans. Kohonen’s Self Organising Maps (Kohonen, Biol Cybern 43:59–69, 1982) algorithm is able to split a population into groups based on large and complex sets of observations. Once the population is split, the individual groups can then be probed for their defining characteristics providing insight into the rationale of the split. The characteristics identified form the basis of classification systems that are able to accurately predict which group an individual will belong to, using only a small subset of the tests available. The specifics of this methodology are detailed herein, and the resulting classification systems provide an effective tool to prognose the learning abilities of new subjects

Explicit interaction information from WikiPathways in RDF facilitates drug discovery in the Open PHACTS Discovery Platform [version 2; referees: 2 approved]

Open PHACTS is a pre-competitive project to answer scientific questions developed recently by the pharmaceutical industry. Having high quality biological interaction information in the Open PHACTS Discovery Platform is needed to answer multiple pathway related questions. To address this, updated WikiPathways data has been added to the platform. This data includes information about biological interactions, such as stimulation and inhibition. The platform's Application Programming Interface (API) was extended with appropriate calls to reference these interactions.  These new methods of the Open PHACTS API are available now

How to recommend music to film buffs: enabling the provision of recommendations from multiple domains

In broad terms, Recommender Systems use machine learning techniques to process historical data about their user's interests, encoded in user profiles. Once the algorithms used have been trained on user profiles, their output is used to compile a ranked list of all resources available for recommendation, based on each profile. Collaborative Filtering is the most widespread method of carrying this out, building on the intuition that similar people will be interested in the same things. The point of failure in this approach lies in that similarity can only be assessed between users that have expressed their preferences on a common set of resources. This requirement prohibits the sharing of preference data across different systems, and causes additional problems when new resources for recommendation become available, or when new users subscribe to the system.I propose that the difficulty can be overcome by identifying and exploiting semantic relationships between the resources available for recommendation themselves. Moreover, systems that are able to assess the strength of the relationship between any two resources can provide recommendations from multiple domains. For example, music recommendations can be made based on a person's film taste if strong semantic relationships can be identified between certain films and the music he/she listens to.As such the contributions made by this dissertation can be summarised in the following:1. Facilitating the comparison of heterogeneous resourcesThe use of Wikipedia is proposed for this purpose, under the assumption that hyper-links between articles in Wikipedia convey latent semantic relationships between the concepts they describe. Thus, a methodology for projecting domain resources onto Wikipedia has been developed. The assumption is then validated by showing evidence that the projections are successful in retaining similarity between domain resources, in three independent domains.2. Enabling the provision of recommendations from multiple domains The aforementioned projections encode the links present in Wikipedia articles that are found to correspond to domain resources, and can be viewed collectively as a graph. In addition, the Internet is populated with social networks of people who express their preferences on a given set of resources in the form of ratings. Members of such communities are included as nodes in the graph and ratings regarding domain resources represented as edges. A reversible Markov chain model was implemented to describe the probabilities associated with the traversal of edges in the integrated graph. Nodes that represent resources and other concepts the user is known to be interested in are then identified in the graph. Using these nodes as a starting point, the resource nodes most likely to be reached after an arbitrarily large number of edge traversals are considered the most relevant to the user and are recommended. Experimental results show that the framework is successful in predicting user preferences in domains different to those of the input

Recommender Systems for the Semantic Web

This paper presents a semantics-based approach to Recommender Systems (RS), to exploit available contextual information about both the items to be recommended and the recommendation process, in an attempt to overcome some of the shortcomings of traditional RS implementations. An ontology is used as a backbone to the system, while multiple web services are orchestrated to compose a suitable recommendation model, matching the current recommendation context at run-time. To achieve such dynamic behaviour the proposed system tackles the recommendation problem by applying existing RS techniques on three different levels: the selection of appropriate sets of features, recommendation model and recommendable items.

The Semantic Logger: Supporting Service Building from Personal Context

The Semantic Logger SL) is presentedas a system for the importing, housing, and exploiting of personal information. The system has been implemented using a number of Semantic Web enabling technologies, and attempts to store the information in a manner adhering to as many W3C recommendations as possible. The Semantic Logger's utility is grounded in two context-based applications, namely a recommender system, and a photo-annotation tool