Open Directory Project based universal taxonomy for Personalization of Online (Re)sources

Content personalization reflects the ability of content classification into (predefined) thematic units or information domains. Content nodes in a single thematic unit are related to a greater or lesser extent. An existing connection between two available content nodes assumes that the user will be interested in both resources (but not necessarily to the same extent). Such a connection (and its value) can be established through the process of automatic content classification and labeling. One approach for the classification of content nodes is the use of a predefined classification taxonomy. With the help of such classification taxonomy it is possible to automatically classify and label existing content nodes as well as create additional descriptors for future use in content personalization and recommendation systems. For these purposes existing web directories can be used in creating a universal, purely content based, classification taxonomy. This work analyzes Open Directory Project (ODP) web directory and proposes a novel use of its structure and content as the basis for such a classification taxonomy. The goal of a unified classification taxonomy is to allow for content personalization from heterogeneous sources. In this work we focus on the overall quality of ODP as the basis for such a classification taxonomy and the use of its hierarchical structure for automatic labeling. Due to the structure of data in ODP different grouping schemes are devised and tested to find the optimal content and structure combination for a proposed classification taxonomy as well as automatic labeling processes. The results provide an in-depth analysis of ODP and ODP based content classification and automatic labeling models. Although the use of ODP is well documented, this question has not been answered to date

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Semantic Sort: A Supervised Approach to Personalized Semantic Relatedness

We propose and study a novel supervised approach to learning statistical semantic relatedness models from subjectively annotated training examples. The proposed semantic model consists of parameterized co-occurrence statistics associated with textual units of a large background knowledge corpus. We present an efficient algorithm for learning such semantic models from a training sample of relatedness preferences. Our method is corpus independent and can essentially rely on any sufficiently large (unstructured) collection of coherent texts. Moreover, the approach facilitates the fitting of semantic models for specific users or groups of users. We present the results of extensive range of experiments from small to large scale, indicating that the proposed method is effective and competitive with the state-of-the-art.Comment: 37 pages, 8 figures A short version of this paper was already published at ECML/PKDD 201

A domain categorisation of vocabularies based on a deep learning classifier.

The publication of large amounts of open data has become a major trend nowadays. This is a consequence of pro-jects like the Linked Open Data (LOD) community, which publishes and integrates datasets using techniques like Linked Data. Linked Data publishers should follow a set of principles for dataset design. This information is described in a 2011 document that describes tasks as the consideration of reusing vocabularies. With regard to the latter, another project called Linked Open Vocabularies (LOV) attempts to compile the vocabularies used in LOD. These vocabularies have been classified by domain following the subjective criteria of LOV members, which has the inherent risk introducing personal biases. In this paper, we present an automatic classifier of vocabularies based on the main categories of the well-known knowledge source Wikipedia. For this purpose, word-embedding models were used, in combination with Deep Learning techniques. Results show that with a hybrid model of regular Deep Neural Network (DNN), Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), vocabularies could be classified with an accuracy of 93.57 per cent. Specifically, 36.25 per cent of the vocabularies belong to the Culture category.pre-print304 K