The Internet plays a major role as a source of news. Many publishers o er 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.
Traditional print media are nowadays replaced or complemented by online media. Most publishers of international, national and local newspapers use the Web as an additional communication channel. Many news sites that are connected to a print newspaper also o er online subscriptions or provide content as pay-per-view. A commonly used solution is subscription-based access to an online newspaper, which is a digital copy of the print newspaper, often with additional features for search, recommendation or archiving. However, in most cases, these additional features are based on content analysis, manual interlinking by the editors and collaborative filtering. In this paper, we present our work towards an semantically enriched online newspaper, which is a currently running collaboration between the L3S Research Center and Madsack GmbH & Co. KG. 1.1
Madsack
The digital business of Madsack media group includes the distribution of editorial content (e-paper, mobile apps, usually using the brand of the corresponding daily newspapers), marketing services (e.g. programming of websites and apps) as well as collaborations with online marketplaces.
We focus on Madsack’s e-paper product. The e-paper is a Web environment that allows subscribers to access the daily editions of the newspaper in digital format. The environment is restricted to paying subscribers, who are required to log in with their personal username and password. Once they are logged in, the website presents the reader current daily newspaper editions. The online newspaper holds the same design as the printed version. Every morning, except on Sundays (there are no editions printed on Sundays), a news daily edition is available on the website. 2
In order to e ectively archive, categorize and publish news articles, most larger
media companies have documentation departments that assign labels, categories or terms
to news articles. Due to the increasingly large amount of items and the need for the
term assignment to be quick [
We semantically enriched the news articles by identifying entities and types. For this
purpose, we use the Wikipedia Miner[
After annotating the content of the news articles, with the identified entities in hand, we query DBpedia in order to gather further information regarding the entities. Specifically, we explore their relationships through the predicate rdf:type to extract the type of the entity given by DBpedia’s ontology (dbpedia-owl). Although several di erent types are identified, we selected the three most relevant types for news articles: dbpediaowl:Place, dbpedia-owl:Person and dbpedia-owl:Organisation. These three types were reported by Madsack’s editorial sta to be the most relevant for their readers. Additionally, as we describe in Section 3, it is important to avoid an overload of features and information to the readers. Thus, we aim at having just a few and very useful facets that can improve relevant news retrieval. 3 http://dumps.wikimedia.org/dewiki/20140216/ From the end users’ (the readers’) perspective, the main innovation of the e-paper is the so called ‘Themenradar’ (Theme Radar). The Theme Radar provides users with shortcuts to news articles that pertain to their particular interests - as explicitly indicated by subscribing to an entity (which represents a theme or topic), combined with the entities that most often occur in the articles that the user read so far. Augmenting the ‘Theme Radar’ with the assistance of semantically enriched data is, in fact, one of our main goals in this collaboration.
Figure 1 depicts the first prototype of the ‘Theme Radar’. It consists of a dashboard of the readers’ interests. The ‘Theme Radar’ works as a semantically enhanced topic dashboard that enables readers to get suggestions for themes and topics, to manage their topics and to get personalized news recommendations based on entity co-occurrences, linked data relations and the aforementioned semantic properties types.
Based on the users’ activity logs, the system automatically builds the ‘Theme Radar’. Top entities of interest are presented to the users in their ‘Theme Radar’ with additional suggested entities and recommended articles (based on entity co-occurrence). In the interface, these entities are grouped by type and also by ‘Book’ (Books are sections within the newspaper, as predefined by the editors - such as ‘Sports’ and ‘Politics’). Additionally, the users can manually add entities to their profiles, which get higher weights in the news recommendation process. 4
In this paper, we presented our work towards a semantically enriched online newspaper, which - to the best of our knowledge - is the first of its kind in a fully commercial setup. We are currently on a stage of interface designing which, in a commercial product, requires the validation and approval from several stakeholders. As future work, we plan to evaluate the quality of the annotations with user feedback and to perform an analysis of online reading behavior, with a focus on the semantic aspects. Building upon these steps, we plan to develop and entity-based news recommender that fulfills Madsack’s online readers’ interests, and to evaluate them in practice. 5
We would like to thank the Madsack Online GmbH & Co. KG team for the collaboration opportunity and the support during the implementation presented in this work.