Towards a Semantically Enriched Online Newspaper

The Internet plays a major role as a source of news. Many publishers offer online versions of their newspapers to paying customers. Online newspapers bear more similarity with traditional print papers than with regular news sites. In a close collaboration with Mediengruppe Madsack – publisher of newspapers in several German federal states, we aim at providing a semantically enriched online newspaper. News articles are annotated with relevant entities – places, persons and organizations. These annotations form the basis for an entity-based `Theme Radar’, a dashboard for monitoring articles related to the users’ explicitly indicated and inferred interests.

Authors: Ricardo Kawase, Eelco Herder, Patrick Siehndel

PDF: kawase-iswc2014

Interlinking Documents based on Semantic Graphs

Connectivity and relatedness of Web resources are two concepts that define to what extent different parts are connected or related to one another. Measuring connectivity and relatedness between Web resources is a growing field of research, often the starting point of recommender systems. Although relatedness is liable to subjective interpretations, connectivity is not. Given the Semantic Web’s ability of linking Web resources, connectivity can be measured by exploiting the links between entities. Further, these connections can be exploited to uncover relationships between Web resources. In this paper, we apply and expand a relationship assessment methodology from social network theory to measure the connectivity between documents. The connectivity measures are used to identify connected and related Web resources. Our approach is able to expose relations that traditional text-based approaches fail to identify. We validate and assess our proposed approaches through an evaluation on a real world dataset, where results show that the proposed techniques outperform state of the art approaches.

Authors: Bernardo Pereira Nunes, Ricardo Kawase, Besnik Fetahu, Stefan Dietze, Marco A. Casanova and Diana Maynard

PDF: nunes-kes2013

Combining a Co-occurrence-Based and a Semantic Measure for Entity Linking

One key feature of the Semantic Web lies in the ability to link related Web resources. However, while relations within particular datasets are often well-defined, links between disparate datasets and corpora of Web resources are rare. The increasingly widespread use of cross-domain reference datasets, such as Freebase and DBpedia for annotating and enriching datasets as well as documents, opens up opportunities to exploit their inherent semantic relationships to align disparate Web resources. In this paper, we present a combined approach to uncover relationships between disparate entities which exploits (a) graph analysis of reference datasets together with (b) entity co-occurrence on the Web with the help of search engines. In (a), we introduce a novel approach adopted and applied from social network theory to measure the connectivity between given entities in reference datasets. The connectivity measures are used to identify connected Web resources. Finally, we present a thorough evaluation of our approach using a publicly available dataset and introduce a comparison with established measures in the field.

Authors: Bernardo Pereira Nunes, Stefan Dietze, Marco Antonio Casanova, Ricardo Kawase, Besnik Fetahu and Wolfgang Nejdl

PDF: nunes-eswc2013

Generating resource profiles by exploiting the context of social annotations

Fabian Abel presenting @ISWC2011

Typical tagging systems merely capture that part of the tagging interactions that enrich the semantics of tag assignments according to the system’s purposes. The common practice is to build tag-based resource or user profiles on the basis of statistics about tags, disregarding the additional evidence that pertain to the resource, the user or the tag assignment itself. Thus, the main bulk of this valuable information is ignored when generating user or resource profiles.

In this work, we formalize the notion of tag-based and context-based resource profiles and introduce a generic strategy for building such profiles by incorporating available context information from all parts involved in a tag assignment. Our method takes into account not only the contextual information attached to the tag, the user and the resource, but also the metadata attached to the tag assignment itself. We demonstrate and evaluate our approach on two different social tagging systems and analyze the impact of several context-based resource modeling strategies within the scope of tag recommendations. The outcomes of our study suggest a significant improvement over other methods typically employed for this task.

Venue: ISWC2011

Presentation: video and slides (by Fabian Abel)

Authors: Ricardo Kawase, George Papadakis, Fabian Abel

PDF: kawase.iswc2011

The Impact of Multifaceted Tagging on Learning Tag Relations and Search

In this paper, we present a model for multifaceted tagging, i.e. tagging enriched with contextual information. We present TagMe!, a social tagging front-end for Flickr images, that provides multifaceted tagging functionality: It enables users to attach tag assignments to a specific area within an image and to categorize tag assignments. Moreover, TagMe! maps tags and categories to DBpedia URIs to clearly define the meaning of freely-chosen words. Our experiments reveal the benefits of these additional tagging facets. For example, the exploitation of the facets significantly improves the performance of FolkRank-based search. Further, we demonstrate the benefits of TagMe! tagging facets for learning semantics within folksonomies.

Venue: ESWC2010

Authors: Fabian Abel, Nicola Henze, Ricardo Kawase and Daniel Krause

PDF: abel-eswc2010