One Knowledge Graph to Rule Them All? Analyzing the Differences Between DBpedia, YAGO, Wikidata & co

Public Knowledge Graphs (KGs) on the Web are considered a valuable asset for developing intelligent applications. They contain general knowledge which can be used, e.g., for improving data analytics tools, text processing pipelines, or recommender systems. While the large players, e.g., DBpedia, YAGO, or Wikidata, are often considered similar in nature and coverage, there are, in fact, quite a few differences. In this paper, we quantify those differences, and identify the overlapping and the complementary parts of public KGs. From those considerations, we can conclude that the KGs are hardly interchangeable, and that each of them has its strenghts and weaknesses when it comes to applications in different domains. 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 [3] and YAGO [9], both of which are created by extracting information from Wikipedia (the latter exploiting WordNet on top), the community edited Wikidata [10], which imports other datasets, e.g., from national libraries, as well as from the discontinued Freebase [7], the expert curated OpenCyc [4], and NELL [1], which exploits pattern-based knowledge extraction from a large Web corpus. Although all these knowledge graphs contain a lot of valuable information, choosing one KG for building a specific application is not a straight forward 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 [6], and focus on other aspects, such as data quality [2]. The question which KG fits which purpose, however, has not been answered so far. 1 https://googleblog.blogspot.de/2012/05/introducing-knowledge-graphthings-not.html 2 https://www.wikidata.org/wiki/Wikidata:Data_donation Table 1: Global Properties of the Knowledge Graphs compared in this paper DBpedia YAGO Wikidata OpenCyc NELL Version 2016-04 YAGO3 2016-08-01 2016-09-05 08m.995 # instances 5,109,890 5,130,031 17,581,152 118,125 1,974,297 # axioms 397,831,457 1,435,808,056 1,633,309,138 2,413,894 3,402,971 avg. indegree 13.52 17.44 9.83 10.03 5.33 avg. outdegree 47.55 101.86 41.25 9.23 1.25 # classes 754 576,331 30,765 116,822 290 # relations 3,555 93,659 11,053 165 1,334 2 Overall Size and Shape of Knowledge Graphs For the analysis in this paper, we focus on the public knowledge graphs DBpedia, YAGO, Wikidata, OpenCyc, and NELL. For those five KGs, we used the most recent available versions at the time of this analysis, as shown in Table 1. We can observe that DBpedia and YAGO have roughly the same number of instances, which is not surprising, due to their construction process, which creates an instance per Wikipedia page. Wikidata, which uses additional sources plus a community editing process, has about tree times more instances. It is remarkable that YAGO and Wikidata have roughly the same number of axioms, although Wikidata has three times more instances. This hints at a higher level of detail in YAGO, which is also reflected in the degree distributions. OpenCyc and NELL are much smaller. NELL is particularly smaller w.r.t. axioms, not instances, i.e., the graph is less dense. This is also reflected in the degree of instances, which depicts that on average, each instance has less than seven connections. The other graphs are much denser, e.g., each instance in Wikidata has about 50 connections on average, each instance in DBpedia has about 60, and each instance in YAGO has even about 120 connections on average. The schema sizes also differ widely. In particular the number of classes are very different. This can be explained by different modeling styles: YAGO automatically generates very fine-grained classes, based on Wikipedia categories. Those are often complex types encoding various facts, such as “American Rock Keyboardists”. KGs like DBpedia or NELL, on the other hand, use well-defined, manually curated ontologies with much fewer classes. Since Wikidata provides live updates, it is the most timely source (together with DBpedia Live, which is a variant of DBpedia fed from an update stream of Wikipedia). From the non-live sources, NELL has the fastest release cycle, providing a new release every few days. However, NELL uses a fixed corpus of Web pages, which is not updated as regularly. Thus, the short release cycles do not necessarily lead to more timely information. DBpedia has biyearly releases, and YAGO and OpenCyc have update cycles longer than a year. 3 Freebase was discarded as it is discontinued, and non-public KGs were not considered, as it is impossible to run the analysis on non-public data. 4 Scripts are available at https://github.com/dringler/KnowledgeGraphAnalysis. DBpedia YAGO Wikidata