Improving web search results with explanation-aware snippets: an experimental study

In this paper, we focus on a typical task on a web search, in which users want to discover the coherency between two concepts on the Web. In our point of view, this task can be seen as a retrieval process: starting with some source information, the goal is to find target information by following hyperlinks. Given two concepts, e.g. chemistry and gunpowder, are search engines able to find the coherency and explain it? In this paper, we introduce a novel way of linking two concepts by following paths of hyperlinks and collecting short text snippets. We implemented a proof-of-concept prototype, which extracts paths and snippets from Wikipedia articles. Our goal is to provide the user with an overview about the coherency, enriching the connection with a short but meaningful description. In our experimental study, we compare the results of our approach with the capability of web search engines. The results show that 72% of the participants find ours better than these of web search engines. (author's abstract

Microdata Information System MISSY: Benefits for Research with Official Microdata, DDI-Based Implementation, and Evaluation with Regard to FAIR Criteria

This paper presents the microdata information system (MISSY). MISSY is a service of the German Mi-crodata Lab (GML) for empirically working scientists conducting research using microdata from official statistics. MISSY provides detailed metadata on individual data sets from the German (Microcensus) and European official statistics (e.g. EU-SILC, EU-LFS) and aims to facilitate the use of the data through user-friendly and quickly accessible data documentation. We address the documentation requirements of official microdata, elaborate the benefits of structured metadata for researchers and describe the resulting objectives and contents of MISSY. Subsequently, we introduce the specific technical implementation: A general description of the technical infrastruc-ture as well as the basic data model (DDI-based) and the import/export interfaces of the database. Finally, we discuss MISSY with regard to the FAIR criteria and show how MISSY contributes to official microdata being "FAIR"

TweetsCOV19 -- A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic

Publicly available social media archives facilitate research in the social sciences and provide corpora for training and testing a wide range of machine learning and natural language processing methods. With respect to the recent outbreak of the Coronavirus disease 2019 (COVID-19), online discourse on Twitter reflects public opinion and perception related to the pandemic itself as well as mitigating measures and their societal impact. Understanding such discourse, its evolution, and interdependencies with real-world events or (mis)information can foster valuable insights. On the other hand, such corpora are crucial facilitators for computational methods addressing tasks such as sentiment analysis, event detection, or entity recognition. However, obtaining, archiving, and semantically annotating large amounts of tweets is costly. In this paper, we describe TweetsCOV19, a publicly available knowledge base of currently more than 8 million tweets, spanning October 2019 - April 2020. Metadata about the tweets as well as extracted entities, hashtags, user mentions, sentiments, and URLs are exposed using established RDF/S vocabularies, providing an unprecedented knowledge base for a range of knowledge discovery tasks. Next to a description of the dataset and its extraction and annotation process, we present an initial analysis and use cases of the corpus

RDQuery ∗- Querying Relational Databases on-the-fly with RDF-QL

One of the main drawbacks of the Semantic Web is the lack of semantically rich data, since most of the information is still stored in relational databases. We present RDQuery, a wrapper system which enables Semantic Web applications to access and query data actually stored in relational databases using their own built-in functionality. RDQuery automatically translates SPARQL and RDQL queries into SQL. The translation process is based on the Relational.OWL representation of relational databases and does not depend on the local schema or the underlying database management system. 1