The maintenance of many data sources takes place in laborious detail work by individual users. For example, the infoboxes on Wikipedia, which many search engines use as a source of factual knowledge, are maintained in this way. A user might update the number of inhabitants for all counties in Mississippi or update their mayors after a local election. For this purpose, (s)he searches for all articles for the individual counties and changes the semi-structured data of infoboxes there. However, different types of errors occur: the user may have forgotten counties or might confuse a county with an identical county in another state or use too many or too few trailing zeros.

In the Janus project [1], we are currently examining how this type of systematic change can follow a concise, logical description. For this purpose, we are translating a set of these changes into an update-query similar to SQL. But to do this, we first need to identify that set of changes that belongs to a systematic change. The recognition of the limits of systematic changes (i.e. when did the user start, when was (s)he finished, did (s)he stop the work and do something else in the meantime?) is a non-trivial matter, which shall be solved by this master thesis.

The work will develop a clustering procedure that considers both the temporal distribution of changes and the semantics of the changes. The resulting clusters should have a clearly recognizable systematic aspect and be complete with respect to this aspect (the user has not made any further changes that correspond to this aspect). Possibly the clusters could be organized hierarchically. Part of the work is the comparison against a baseline and selecting a suitable methodology for this comparison.

[1] https://IANVS.org/

For more information please contact Prof. Felix Naumann or Tobias Bleifuß.

Web tables constitute a rich and often free source of information. Unfortunately, not all information contained in them is always trustworthy and up-to-date. However, the table's change-history can help assess the trustworthiness and timeliness of its information: Who has authored the table? When was the table last updated? How much was changed? This analysis can, of course, be performed at table-level, but it is much more valuable if done on a cell- or value-level. Only then there is a chance to recognize quality discrepancies within a table.

Tracing the change history by hand is a very tedious task. Tables often change their layout and schema over the course of their lifetime, making it difficult to track which cells are predecessors or successors to each other. In addition, individual cells can be split into multiple cells and, vice-versa, merged into one. The goal of this thesis is to develop an algorithm that automatically constructs the edit history of individual cells in web tables as a graph. Each node in this graph represents one version of one cell at a particular point in time and each edge represents the relationship of these cell versions.

In the context of the Janus project [1] we have already developed an algorithm for matching tables over time and also a baseline algorithm that matches cells (with some constraints) over time. The master's thesis can build on this work and in particular overcome its limitations. For example, the work can focus on improved semantic interpretation of the tables and on scaling aspects. There is already a small gold standard for cell matching, but part of the task is to extend and possibly adapt it. The previous implementation is available in Java, so knowledge of this programming language is beneficial.

[1] https://www.IANVS.org

For more information please contact Prof. Felix Naumann or Tobias Bleifuß.