YAGO and DBpedia **(see next experiment) indicates that negative evidence**
can be helpful to distinguish entities of different types (movies and songs) that share one value (the title). However, in our settings, positive evidence proved sufficient.

## 6.4 Real-World Ontologies

We wanted to test paris **on real-world ontologies of a large scale, with a rich**
class and relation structure. At the same time, we wanted to restrict ourselves to cases where an error-free ground truth is available. Therefore, we first chose to align the YAGO **[28] and** DBpedia **[1] ontologies, and then to align** YAGO
with an ontology built out of the IMDb12.

| Ontology   | #Instances   | #Classes   | #Relations   |
|------------|--------------|------------|--------------|
| YAGO       | 2,795,289    | 292,206    | 67           |
| DBpedia    | 2,365,777    | 318        | 1,109        |
| IMDb       | 4,842,323    | 15         | 24           |

Table 2: YAGO **[28],** DBpedia **[1] and** IMDb.

YAGO vs. DBpedia. **With several million instances, these are some of the**
largest ontologies available. Each of them has thousands of **classes and at least**
dozens of relations. We took only the non-meta facts from YAGO**, and only**
the manually established ontology from DBpedia**, which yields the datasets described in Table 2. Both ontologies use Wikipedia identifiers for their instances,**
so that the ground truth for the instance matching can be computed trivially.13 However, the statements about the instances differ in both ontologies, so that the matching is not trivial. The class structure and the relationships of YAGO and DBpedia **were designed completely independently, making their alignment**
a challenging endeavor.

We ran paris **for 4 iterations, until convergence. Table 3 shows the results**
per iteration. To compute recall, we counted the number of shared instances in DBpedia and YAGO**. Since** YAGO **selects Wikipedia pages with many categories, and** DBpedia **selects pages with frequent infoboxes, the two resources**
share only 1.4 million entities. paris **can map them with a precision of 90 %**
and a recall of 73 %. If only entities with more than 10 facts in **DBpedia are**
considered, precision and recall jump to 97 % and 85 %, respectively.

| Instances   | Classes   | Relations   |      |      |       |            |            |      |            |            |    |       |     |      |
|-------------|-----------|-------------|------|------|-------|------------|------------|------|------------|------------|----|-------|-----|------|
| Change      | Time      | Prec        | Rec  | F    | Time  | YAGO ⊆ DBp | DBp ⊆ YAGO | Time | YAGO ⊆ DBp | DBp ⊆ YAGO |    |       |     |      |
| to prev.    | Num       | Prec        | Num  | Prec | Num   | Prec       | Num        | Prec |            |            |    |       |     |      |
| -           | 4h04m     | 86 %        | 69 % | 77 % | -     | -          | -          | -    | -          | 19min      | 30 | 93 %  | 134 | 90 % |
| 12.4 %      | 5h06m     | 89 %        | 73 % | 80 % | -     | -          | -          | -    | -          | 21min      | 32 | 100 % | 144 | 92 % |
| 1.1 %       | 5h00m     | 90 %        | 73 % | 81 % | -     | -          | -          | -    | -          | 21min      | 33 | 100 % | 149 | 92 % |
| 0.3 %       | 5h26m     | 90 %        | 73 % | 81 % | 2h14m | 137k       | 94 %       | 149  | 84 %       | 24min      | 33 | 100 % | 151 | 92 % |

Table 3: Results on matching YAGO and DBpedia **over iterations 1–4**
12**The Internet Movie Database,** http://www.imdb.com 13**We hid this knowledge from** paris.