Search results

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)

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 is a semantically annotated corpus of Tweets about the COVID-19 pandemic. It is a subset of TweetsKB and aims at capturing online discourse about various aspects of the pandemic and its societal impact. Metadata information about the tweets as well as extracted entities, sentiments, hashtags, user mentions, and resolved URLs are exposed in RDF using established RDF/S vocabularies (for the sake of privacy, we anonymize user IDs and we do not provide the text of the tweets). More information are available through TweetsCOV19's home page: https://data.gesis.org/tweetscov19/.

We also provide a tab-separated values (tsv) version of the dataset. Each line contains features of a tweet instance. Features are separated by tab character ("\t"). The following list indicate the feature indices:

1. Tweet Id: Long.

2. Username: String. Encrypted for privacy issues.

3. Timestamp: Format ( "EEE MMM dd HH:mm:ss Z yyyy" ).

4. #Followers: Integer.

5. #Friends: Integer.

6. #Retweets: Integer.

7. #Favorites: Integer.

8. Entities: String. For each entity, we aggregated the original text, the annotated entity and the produced score from FEL library. Each entity is separated from another entity by char ";". Also, each entity is separated by char ":" in order to store "original_text:annotated_entity:score;". If FEL did not find any entities, we have stored "null;".

9. Sentiment: String. SentiStrength produces a score for positive (1 to 5) and negative (-1 to -5) sentiment. We splitted these two numbers by whitespace char " ". Positive sentiment was stored first and then negative sentiment (i.e. "2 -1").

10. Mentions: String. If the tweet contains mentions, we remove the char "@" and concatenate the mentions with whitespace char " ". If no mentions appear, we have stored "null;".

11. Hashtags: String. If the tweet contains hashtags, we remove the char "#" and concatenate the hashtags with whitespace char " ". If no hashtags appear, we have stored "null;".

12. URLs: String: If the tweet contains URLs, we concatenate the URLs using ":-: ". If no URLs appear, we have stored "null;"

To extract the dataset from TweetsKB, we compiled a seed list of 268 COVID-19-related keywords.

You can find the previous part 3 at https://doi.org/10.5281/zenodo.4593523 .