1,956 research outputs found
A Topic Recommender for Journalists
The way in which people acquire information on events and form their own
opinion on them has changed dramatically with the advent of social media. For many
readers, the news gathered from online sources become an opportunity to share points
of view and information within micro-blogging platforms such as Twitter, mainly
aimed at satisfying their communication needs. Furthermore, the need to deepen the
aspects related to news stimulates a demand for additional information which is often
met through online encyclopedias, such as Wikipedia. This behaviour has also
influenced the way in which journalists write their articles, requiring a careful assessment
of what actually interests the readers. The goal of this paper is to present
a recommender system, What to Write and Why, capable of suggesting to a journalist,
for a given event, the aspects still uncovered in news articles on which the
readers focus their interest. The basic idea is to characterize an event according to
the echo it receives in online news sources and associate it with the corresponding
readers’ communicative and informative patterns, detected through the analysis of
Twitter and Wikipedia, respectively. Our methodology temporally aligns the results
of this analysis and recommends the concepts that emerge as topics of interest from
Twitter and Wikipedia, either not covered or poorly covered in the published news
articles
Crowdsourced Evaluation of Semantic Patterns for Recommendations
Abstract. In this paper we explore the use of semantics to improve diversity in recommendations. We use semantic patterns extracted from Linked Data sources to surface new connections between items to provide diverse recommendations to the end users. We evaluate this methodology by adopting a bottom-up approach, i.e. we ask users of a crowdsourcing platform to choose a movie recommendation from among five options. We evaluate the results in terms of a diversity measure based on the semantic distance of topics and genres of the result list. The results of the experiment indicate that there are features of semantic patterns that can be used as an indicator of its suitability for the recommendation process.
Disaster Monitoring with Wikipedia and Online Social Networking Sites: Structured Data and Linked Data Fragments to the Rescue?
In this paper, we present the first results of our ongoing early-stage
research on a realtime disaster detection and monitoring tool. Based on
Wikipedia, it is language-agnostic and leverages user-generated multimedia
content shared on online social networking sites to help disaster responders
prioritize their efforts. We make the tool and its source code publicly
available as we make progress on it. Furthermore, we strive to publish detected
disasters and accompanying multimedia content following the Linked Data
principles to facilitate its wide consumption, redistribution, and evaluation
of its usefulness.Comment: Accepted for publication at the AAAI Spring Symposium 2015:
Structured Data for Humanitarian Technologies: Perfect fit or Overkill?
#SD4HumTech1
Assessing and refining mappings to RDF to improve dataset quality
RDF dataset quality assessment is currently performed primarily after data is published. However, there is neither a systematic way to incorporate its results into the dataset nor the assessment into the publishing workflow. Adjustments are manually -but rarely- applied. Nevertheless, the root of the violations which often derive from the mappings that specify how the RDF dataset will be generated, is not identified. We suggest an incremental, iterative and uniform validation workflow for RDF datasets stemming originally from (semi-) structured data (e.g., CSV, XML, JSON). In this work, we focus on assessing and improving their mappings. We incorporate (i) a test-driven approach for assessing the mappings instead of the RDF dataset itself, as mappings reflect how the dataset will be formed when generated; and (ii) perform semi-automatic mapping refinements based on the results of the quality assessment. The proposed workflow is applied to diverse cases, e.g., large, crowdsourced datasets such as DBpedia, or newly generated, such as iLastic. Our evaluation indicates the efficiency of our workflow, as it significantly improves the overall quality of an RDF dataset in the observed cases
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