Towards Profiling Knowledge Graphs

Knowledge Graphs, such as DBpedia, YAGO, or Wikidata, are valuable resources for building intelligent applications like data analytics tools or recommender systems. Understanding what is in those knowledge graphs is a crucial prerequisite for selecing a Knowledge Graph for a task at hand. Hence, Knowledge Graph profiling i.e., quantifying the structure and contents of knowledge graphs, as well as their differences is essential for fully utilizing the power of Knowledge Graphs. In this paper, I will discuss methods for Knowledge Graph profiling, depict crucial differences of the big, well-known Knowledge Graphs, like DBpedia, YAGO, and Wikidata, and throw a glance at current developments of new, complementary Knowledge Graphs such as DBkWik and WebIsALOD. 1 Knowledge Graphs on the Web The term “Knowledge Graph” was coined by Google when they introduced their knowledge graph as a backbone of a new Web search strategy in 2012, i.e., moving from pure text processing to a more symbolic representation of knowledge, using the slogan “things, not strings”. Various public knowledge graphs are available on the Web, including DBpedia [14] and YAGO [26], both of which are created by extracting information from Wikipedia (the latter exploiting WordNet on top), the community edited Wikidata [27], which imports other datasets, e.g., from national libraries, as well as from the discontinued Freebase [19], the expert curated OpenCyc [15], and NELL [4], which exploits pattern-based knowledge extraction from a large Web corpus. Furthermore, company-owned knowledge graphs exist, like the already mentioned Google Knowledge Graph, Google’s Knowledge Vault [5], Yahoo’s Knowledge Graph [2], Microsoft’s Satori, and Facebook’s Knowledge Graph. However, those are not publicly available, and hence neither suited to build applications by parties other than the owners, nor can they be analyzed in depth. The degree to which their size, structure, contents, and quality are known varies. Although all these knowledge graphs contain a lot of valuable information, choosing one KG for building a specific application is not a straight forward 1 https://googleblog.blogspot. de/2012/05/introducing-knowledge-graphthings-not.html 2 https://www.wikidata. org/wiki/Wikidata:Data_donation task. Depending on the domain and task at hand, some KGs might be better suited than others. However, there are no guidelines or best practices on how to choose a knowledge graph which fits a given problem. Previous works mostly report global numbers, such as the overall size of knowledge graphs, such as [16], and focus on other aspects, such as data quality [9]. In [20], we have taken a more in-depth look, showing detailed results for different classes. 2 Measures and Methods for Knowledge Graph Profiling In general, the aim of knowledge graph profiling is to understand whether a given knowledge graph suits a certain purpose. For example, for building an application for a specific domain, backed by a knowledge graph, requires that this knowledge graph contains a reasonable amount of information about the entities in that domain, and describes them at a suitable level of detail. There is a large body of work on measures and methods for dataset profiling for knowledge graphs and Linked datasets [6]. For analyzing knowledge graphs, we use the following three classes of metrics: Global Measures describe the knowledge graph as a whole, Class-based Measures describe the characteristics of entities in a given class, and Overlap Measures describe the difference between two or more knowledge graphs.