The MiMoText graph contains information on first publication dates of all of the work items (novels) referenced by the Bibliographie du Genre Romanesque Français (Martin, Mylne, and Frautschi 1977). This enables us to retrieve change over time, optionally combining it with other properties.
Let’s start with a simple example: The following query shows the first publication date (P9) of all the novel items in the corpus.
We can observe an augmentation of novel production in the later decades, a finding also backed by secondary literature (see for example Franco Moretti on the rise of the novel: Moretti 2005: 9).
Furthermore, we can also combine information on first publication dates and a certain topic. Let’s see how the thematic concept “travel” evolved over time:
We could also have a look at all the themes in the corpus and their evolution over time, optionally filtering out themes with no or only one occurrence per year (?countyear > 1).
Please note that these include topics generated by topic modeling, so some topic labels may seem odd to you (topics with low semantic coherence are labeled using the three top topic words). If you are interested in how we generated the topics, see our paper on topic modeling of French novels 1750-1800 (Röttgermann, Julia, und Anne Klee. „“Nuit, correspondance, sentiment” - Topic Modeling auf einem Korpus von französischen Romanen 1750-1800“. apropos: Perspectives on Romania 9/2022, 57-86. DOI: 10.15460/apropos.9.1888).
We can also query for narrative form from a diachronic perspective:
If you are new to SPARQL, you can go through the (short)Tutorial,which will give you an overview of how to write basic queries based on examples inMiMoTextBase. It’s supposed to give newbies an introduction to SPARQL, but it cannot give you a deep knowledge of SPARQL – maybe theseresourcescan help you with that.
If you are interested in MiMoTextBase and its content onauthors,novels,spacesorthemesof the French novel in 1751-1800 with already some SPARQL knowledge, you can have a look at the links.
WithinGOING FURTHER there are some queries on the data containing overviews of items like dates of publication or themes changing over time and comparing the different sources of the data inMiMoTextBase together with some interpretation on the outcome which could show the potential of initial questions on further research.
If you want more detailed information about the structure and the aims of our tutorial, you can find it in theintroduction of the tutorial.Information on the infrastructure and the models behind MiMoTextBase you can findhere.
Having no results in the result table can have different reasons. A simple solution is to check whether the variables are spelled the same in the SELECT and the WHERE part of the query.
Another reason could be being too specific in the query. Not all items in MiMoTextBase contain all information on all properties due to its sources. So it can be helpful to add the OPTIONAL function on some of the properties in your query, seehere.
The solution is easy: We have to aggregate ?authorName by grouping. We can now get the results in descending order via order by desc(?count) and set a limit of 20 to get the top 20.
Sometimes you can get many results on a query which can slow down the result generation or impair the readability of some visualizations. In those cases you could add the LIMIT-operation (seehere)to only get the TOP x items or the HAVING COUNT-operation (seehere)if you want only results that lie above a certain threshold.
If some of the items appear more often in the results than they should, make sure you filter all labels for one language (FR, EN, DE) separately as the graph is multilingual and the output will represent all languages within the graph, seehere.
If you're looking for the right identifier for properties, novels, authors, themes or locations, the simplest way is to visitdata.mimotext.uni-trier.deand type in the label (for example “London” or “about” or “philosophy”) in the search bar. The numerical identifier of the property or the item is visible in the URL or behind the name of the item or the property.
You can also consult our lists of themes, locations and properties and their numerical identifier in the knowledge graph below.
For a list of all thematic concepts in the graph, see thisquerywhich lists all thematic concepts and their Q-identifier, ordered by occurrence:
List of locations
For a list of all narrative places in the graph, see thisquerywhich lists all narrative places and their Q-Identifier, ordered by occurrence:
These queries list themes or locations ordered by occurrence. We recommend using items or properties which have a certain number of connections in the graph, in order to get good results (with enough data points).
There are several possible reasons for a slowdown or a timeout of your query. It could be that the quantity of results is very high, so you might limit the results to check if the syntax of the query is OK. This is done by using theLIMITparameter. The LIMIT tells the algorithm where to stop, so if you insert for example LIMIT 100 at the end of your query, it will stop after 100 results. This can be helpful for debugging.
Parameters which potentially slow down the query are DISTINCT or ORDER BY. A strategy might be to comment them out to see if these slow down your query.
