Adrian joined Modul University as a Researcher in the Department of New Media Technology in November 2011, after an 8-month internship. Adrian's research interests are in the following areas: Semantic Web (Linked Data, OBDA, Reasoning), NLP & Information Extraction (NER, NEL, KBP, AKBC,resources and evaluation techniques), Information Abstraction (summarization, automated ontology construction), Information Visualization (D3, Vega and related tech) and Machine Learning.
Lyndon Nixon, Sabine Sedlacek, Ivo Ponocny, Adrian Brasoveanu, Jakob SteixnerEPOCH - Extracting and Predicting Events from Online Communication and Hybrid Datasets
EPOCH will measure the effects on statistical indicators of events being reported in the news and social media. Innovatively, it will use the measured effects of now past events to predict the future changes expected due to future events detected in the public dialogue. Through the EPOCH dashboard, organizations can identify and thus better prepare for these changes, adapting their communications, marketing and resources accordingly. This will be demonstrated in the domains of purchase price forecasting and public relations.
Organisations: MODUL University Vienna, Department of New Media Technology, Department of Sustainability, Governance, and Methods
Author: Lyndon Nixon, Sabine Sedlacek, Ivo Ponocny, Adrian Brasoveanu, Jakob Steixner
Date: 01.01.2019 - 31.12.2021
Managed By: Modul Technology GmbH
Lyndon Nixon, Adrian Brasoveanu, Jakob Steixner, Adriana Bassani, Pavel FilippovReTV - Enhancing and Repurposing TV Content for Trans-Vector Engagement
ReTV aims to provide broadcasters and content distributors with technologies and insights to leverage the converging digital media landscape. By advancing the state of the art in the analysis of this media landscape and providing novel methods to dynamically re-purpose content for an array of media vectors (= all relevant digital channels), a Trans-Vector Platform (TVP) will provide these stakeholders with the ability to "publish to all media vectors with the effort of one". It will empower broadcasters and brands to measure and predict the success of their content and advertisments in terms of reach and audience engagement across vectors.
Organisations: MODUL University Vienna, Department of New Media Technology, Modul Technology GmbH
Author: Lyndon Nixon, Adrian Brasoveanu, Jakob Steixner, Adriana Bassani, Pavel Filippov
Date: 01.01.2018 - 31.12.2020
Managed By: Modul Technology GmbH
Marta Sabou, Adrian Brasoveanu, Irem Önder, Arno Scharl, Karl WöberETIHQ - Exposing Tourism Indicators as High Quality Linked Data
Although the tourism domain heavily relies on complex decision making, it currently lacks decision support systems with the capability to seamlessly integrate and visualise data from multiple data sources of tourism (and other) indicators. Linked Data technologies, by contrast, especially when adopted at large scale, greatly facilitate data integration at the syntactic and semantic level alike by providing a uniform data encoding format. Such technologies also help to clearly specify the meaning of the data and to establish links between various datasets. In this project we will use Linked Data technologies to create a reference repository of tourism indicators (ETIHQ) by exposing the content of TourMIS, a major source of European tourism statistics, as high-quality Linked Data. We will ensure data quality by providing semantically rich vocabularies that will support (i) the specification of the meaning of tourism statistics and (ii) the provenance of the data items. To demonstrate the benefits of using Linked Data, we will design and implement a decision support system that makes use of the ETIHQ repository and leverages its detailed semantic specifications to provide appropriate access control mechanisms.
Organisations: Department of New Media Technology, Department of Tourism and Service Management, MODUL University Vienna
Author: Marta Sabou, Adrian Brasoveanu, Irem Önder, Arno Scharl, Karl Wöber
Date: 01.10.2013 - 01.10.2014
Managed By: Department of New Media Technology
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Lyndon Nixon, Adrian Brasoveanu"StoryLens: A Multiple Views Corpus for Location and Event Detection"2018
The news media landscape tends to focus on long-running narratives. Correctly processing new information, therefore, requires considering multiple lenses when analyzing media content. Traditionally it would have been considered sufficient to extract the topics or entities contained in a text in order to classify it, but today it is important to also look at more sophisticated annotations related to fine-grained geolocation, events, stories and the relations between them. In order to leverage such lenses we propose a new corpus that offers a diverse set of annotations over texts collected from multiple media sources. We also showcase the framework used for creating the corpus, as well as how the information from the various lenses can be used in order to support different use cases in the EU project InVID for verifying the veracity of online video.
Author(s): Lyndon Nixon, Adrian Brasoveanu
Publication date: 27. 6. 2018
Publisher: ACM Digital Library
Adrian Brasoveanu, Giuseppe Rizzo, Philip Kuntschik, Albert Weichselbraun, Lyndon Nixon"Framing Named Entity Linking Error Types"2018
Author(s): Adrian Brasoveanu, Giuseppe Rizzo, Philip Kuntschik, Albert Weichselbraun, Lyndon Nixon
Publication date: 2018
"Visualizing statistical linked knowledge for decision support"2017 in: Semantic Web Journal. Volume: 8. Issue number: 1 Pages: 113-137
In a global and interconnected economy, decision makers often need to consider information from various domains. A tourism destination manager, for example, has to correlate tourist behavior with financial and environmental indicators to allocate funds for strategic long-term investments. Statistical data underpins a broad range of such cross-domain decision tasks. A variety of statistical datasets are available as Linked Open Data, often incorporated into visual analytics solutions to support decision making. What are the principles, architectures, workflows and implementation design patterns that should be followed for building such visual cross-domain decision support systems. This article introduces a methodology to integrate and visualize cross-domain statistical data sources by applying selected RDF Data Cube (QB) principles. A visual dashboard built according to this methodology is presented and evaluated in the context of two use cases in the tourism and telecommunications domains.
Author(s): Adrian Brasoveanu, Marta Sabou, Arno Scharl, Alexander Hubmann-Haidvogel, Daniel Fischl
Publication date: 2017
Issue number: 1
Electronic version(s), related files and links: http://dx.doi.org/10.3233/SW-160225
"A Regional News Corpora for Contextualized Entity Discovery and Linking"2016
Author(s): Lyndon Nixon, Arno Scharl, Adrian Brasoveanu, Albert Weichselbraun
Publication date: 1. 5. 2016
"Towards cross-domain data analytics in tourism: a linked data based approach"2016 in: Journal of Information Technology & Tourism. Volume: 16. Issue number: 1 Pages: 71-101
The complexity of the social, political and economical settings in which tourism enterprises operate, increasingly require them to perform data analytics tasks that rely on data from various domains (e.g., economy, environmental sustainability). A survey of tourism practitioners performed in this study showed that although such cross-domain analytics are important, they are primarily performed by relying on manual data collection and aggregation, which is both time-consuming and error-prone. This paper investigates the suitability of Linked Data technologies to support data aggregation tasks needed for establishing such complex analytics systems. To that end, a prototypical implementation is developed that relies on Linked Data as a technological platform for integrating data from three major tourism data sources: TourMIS, World Bank and Eurostat. Enabled by this integrated data, the ETIHQ Dashboard for data analytics was implemented, the first visual data analytics system that supports cross-domain analytics over tourism, economic and sustainability indicators. An exploratory evaluation performed with practitioners shows that this Linked Data enabled system could potentially bring important improvements in terms of execution times and answer quality when compared to current manual approaches typically used by tourism practitioners in daily practice.
Author(s): Marta Sabou, Irem Önder, Adrian Brasoveanu, Arno Scharl
Publication date: 3. 2016
Issue number: 1
Electronic version(s), related files and links: http://dx.doi.org/10.1007/s40558-015-0049-5
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