4,827 research outputs found
Enriching ontological user profiles with tagging history for multi-domain recommendations
Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals' tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites
A collaborative-project memory tool for participatory planning
Technology is more and more providing planners and designer with tools and methods to collect and communicate spatial data and assist spatial analysis. When we think about new technologies supporting planning we mainly think about GIS, urban modelling, simulation models and virtual reality. But many other challenges to the planning practice need for tools to support and improve planning activities. In this paper we discuss the need of new tools to support knowledge representation and knowledge sharing in participatory planning processes. The paper describes the use of a hypermedia and sensemaking tool (Compendium) to structure the knowledge produced in a real participatory planning process. In the present application Compendium has been used not for real-time capturing but for a post-hoc analysis of a real participatory planning experience.
Compendium has been used to represent and reconstruct the group memory of consultation meetings in order to allow both the planning team and the citizens to navigate into the contents of those meetings. Moreover the paper describes the main features and potential of the use of Compendium in Participatory Planning domain, and it describes the results of the group memory reconstruction. Finally the case study opens reflections on the need of new planning technologies supporting participatory knowledge generation, representation and management
Enrichment and ranking of the YouTube tag space and integration with the Linked Data cloud
The increase of personal digital cameras with video functionality and video-enabled camera phones has increased the amount of user-generated videos on the Web. People are spending more and more time viewing online videos as a major source of entertainment and āinfotainmentā. Social websites allow users to assign shared free-form tags to user-generated multimedia resources, thus generating annotations for objects with a minimum amount of effort. Tagging allows communities to organise their multimedia items into browseable sets, but these tags may be poorly chosen and related tags may be omitted. Current techniques to retrieve, integrate and present this media to users are deficient and could do with improvement. In this paper, we describe a framework for semantic enrichment, ranking and integration of web video tags using Semantic Web technologies. Semantic enrichment of folksonomies can bridge the gap between the uncontrolled and flat structures typically found in user-generated content and structures provided by the Semantic Web. The enhancement of tag spaces with semantics has been accomplished through two major tasks: a tag space expansion and ranking step; and through concept matching and integration with the Linked Data cloud. We have explored social, temporal and spatial contexts to enrich and extend the existing tag space. The resulting semantic tag space is modelled via a local graph based on co-occurrence distances for ranking. A ranked tag list is mapped and integrated with the Linked Data cloud through the DBpedia resource repository. Multi-dimensional context filtering for tag expansion means that tag ranking is much easier and it provides less ambiguous tag to concept matching
Interactive Search and Exploration in Online Discussion Forums Using Multimodal Embeddings
In this paper we present a novel interactive multimodal learning system,
which facilitates search and exploration in large networks of social multimedia
users. It allows the analyst to identify and select users of interest, and to
find similar users in an interactive learning setting. Our approach is based on
novel multimodal representations of users, words and concepts, which we
simultaneously learn by deploying a general-purpose neural embedding model. We
show these representations to be useful not only for categorizing users, but
also for automatically generating user and community profiles. Inspired by
traditional summarization approaches, we create the profiles by selecting
diverse and representative content from all available modalities, i.e. the
text, image and user modality. The usefulness of the approach is evaluated
using artificial actors, which simulate user behavior in a relevance feedback
scenario. Multiple experiments were conducted in order to evaluate the quality
of our multimodal representations, to compare different embedding strategies,
and to determine the importance of different modalities. We demonstrate the
capabilities of the proposed approach on two different multimedia collections
originating from the violent online extremism forum Stormfront and the
microblogging platform Twitter, which are particularly interesting due to the
high semantic level of the discussions they feature
Knowledge-rich Image Gist Understanding Beyond Literal Meaning
We investigate the problem of understanding the message (gist) conveyed by
images and their captions as found, for instance, on websites or news articles.
To this end, we propose a methodology to capture the meaning of image-caption
pairs on the basis of large amounts of machine-readable knowledge that has
previously been shown to be highly effective for text understanding. Our method
identifies the connotation of objects beyond their denotation: where most
approaches to image understanding focus on the denotation of objects, i.e.,
their literal meaning, our work addresses the identification of connotations,
i.e., iconic meanings of objects, to understand the message of images. We view
image understanding as the task of representing an image-caption pair on the
basis of a wide-coverage vocabulary of concepts such as the one provided by
Wikipedia, and cast gist detection as a concept-ranking problem with
image-caption pairs as queries. To enable a thorough investigation of the
problem of gist understanding, we produce a gold standard of over 300
image-caption pairs and over 8,000 gist annotations covering a wide variety of
topics at different levels of abstraction. We use this dataset to
experimentally benchmark the contribution of signals from heterogeneous
sources, namely image and text. The best result with a Mean Average Precision
(MAP) of 0.69 indicate that by combining both dimensions we are able to better
understand the meaning of our image-caption pairs than when using language or
vision information alone. We test the robustness of our gist detection approach
when receiving automatically generated input, i.e., using automatically
generated image tags or generated captions, and prove the feasibility of an
end-to-end automated process
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
Multimedia Annotation Interoperability Framework
Multimedia systems typically contain digital documents of mixed media types, which are indexed on the basis of strongly divergent metadata standards. This severely hamplers the inter-operation of such systems. Therefore, machine understanding of metadata comming from different applications is a basic requirement for the inter-operation of distributed Multimedia systems. In this document, we present how interoperability among metadata, vocabularies/ontologies and services is enhanced using Semantic Web technologies. In addition, it provides guidelines for semantic interoperability, illustrated by use cases. Finally, it presents an overview of the most commonly used metadata standards and tools, and provides the general research direction for semantic interoperability using Semantic Web technologies
The state-of-the-art in personalized recommender systems for social networking
With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match usersā personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0
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