Research & Innovation

Infusing autonomous artificial systems with knowledge about the physical world they inhabit is of utmost importance and a long lasting goal in Artificial Intelligence (AI) research. Training systems with relevant data is a common approach; yet, it is not always feasible to find the data needed, especially since a big portion of this knowledge is commonsense.

Learned latent vector representations are key to the success of many recommender systems in recent years. However, traditional approaches like matrix factorization, produce vector representations that capture global distributions of a static recommendation scenario only.

The last decades witnessed a significant evolution in terms of data generation, management, and maintenance. This has resulted in vast amounts of data available in a variety of forms and formats such as RDF.

We describe the use of Linguistic Linked Open Data (LLOD) to support a cross-lingual transfer framework for concept detection in online health communities. Our goal is to develop multilingual text analytics as an enabler for analyzing health-related quality of life (HRQoL) from self-reported patient narratives.

The growing web of data warrants better data management strategies. Data silos are single points of failure and they face availability problems which lead to broken links. Furthermore the dynamic nature of some datasets increases the need for a versioning scheme. In this work, we propose a novel architecture for a linked open data infrastructure, built on open decentralized technologies.

RDF Stream Processing (RSP) has been proposed as a way of bridging the gap between the Complex Event Processing (CEP) paradigm and the Semantic Web standards. Uncertainty has been recognized as a critical aspect in CEP, but it has received little attention within the context of RSP. In this paper, we investigate the impact of different RSP optimization strategies for uncertainty management.

This paper presents a knowledge graph describing the Members of Parliament in Finland and related actors in politics, extracted from the databases and textual descriptions of the Parliament of Finland. The data has been interlinked internally and enriched with data linking to external data sources according to the 5-star Linked Data model.

In Faceted Search Systems (FSS), users navigate the information space through facets, which are attributes or meta-data that describe the underlying content of the collection. Type-based facets (aka t-facets) help explore the categories associated with the searched objects in structured information space.

This paper describes the steps taken to model a valency lexicon for Latin (Latin Vallex) according to the principles of the Linguistic Linked Open Data standards, and to interlink its valency frames with the lexical senses recorded in a manually checked subset of the Latin WordNet.

The usage of Named Entity Recognition tools on domain specific corpora is often hampered by insufficient training data. We investigate an approach to produce fine-grained named entity annotations from a small training dataset to later apply it on a large corpus of Austrian court decisions.