Wikimedia Research/Showcase/Archive/2017/06
June 2017[edit]
June 21, 2017, 11:30am PDT Video: YouTubecommons
- Problematizing and Addressing the Article-as-Concept Assumption in Wikipedia
- By Allen Yilun Lin
- Wikipedia-based studies and systems frequently assume that each article describes a separate concept. However, in this paper, we show that this article-as-concept assumption is problematic due to editorsâ tendency to split articles into parent articles and sub-articles when articles get too long for readers (e.g. âUnited Statesâ and âAmerican literatureâ in the English Wikipedia). In this paper, we present evidence that this issue can have significant impacts on Wikipedia-based studies and systems and introduce the subarticle matching problem. The goal of the sub-article matching problem is to automatically connect sub-articles to parent articles to help Wikipedia-based studies and systems retrieve complete information about a concept. We then describe the first system to address the sub-article matching problem. We show that, using a diverse feature set and standard machine learning techniques, our system can achieve good performance on most of our ground truth datasets, significantly outperforming baseline approaches.
- Related CSCW 2017 paper: (preprint, citation), Open-source code
- Slides: Commons, Figshare
- Wikipedia-based studies and systems frequently assume that each article describes a separate concept. However, in this paper, we show that this article-as-concept assumption is problematic due to editorsâ tendency to split articles into parent articles and sub-articles when articles get too long for readers (e.g. âUnited Statesâ and âAmerican literatureâ in the English Wikipedia). In this paper, we present evidence that this issue can have significant impacts on Wikipedia-based studies and systems and introduce the subarticle matching problem. The goal of the sub-article matching problem is to automatically connect sub-articles to parent articles to help Wikipedia-based studies and systems retrieve complete information about a concept. We then describe the first system to address the sub-article matching problem. We show that, using a diverse feature set and standard machine learning techniques, our system can achieve good performance on most of our ground truth datasets, significantly outperforming baseline approaches.
- By Allen Yilun Lin
- Understanding Wikidata Queries
- By Markus Kroetzsch
- Wikimedia provides a public service that lets anyone answer complex questions over the sum of all knowledge stored in Wikidata. These questions are expressed in the query language SPARQL and range from the most simple fact retrievals ("What is the birthday of Douglas Adams?") to complex analytical queries ("Average lifespan of people by occupation"). The talk presents ongoing efforts to analyse the server logs of the millions of queries that are answered each month. It is an important but difficult challenge to draw meaningful conclusions from this dataset. One might hope to learn relevant information about the usage of the service and Wikidata in general, but at the same time one has to be careful not to be misled by the data. Indeed, the dataset turned out to be highly heterogeneous and unpredictable, with strongly varying usage patterns that make it difficult to draw conclusions about "normal" usage. The talk will give a status report, present preliminary results, and discuss possible next steps. (Project page on meta)
- By Markus Kroetzsch