- Some errors were caused by errors in YAGO**, usually caused by incorrect**
references from Wikipedia pages to IMDb **movies.**
- paris **sometimes aligned instances in** YAGO **with instances in** IMDb that were not equivalent, but very closely related: for example, **King of**
the Royal Mounted was aligned with *The Yukon Patrol***, a feature version** of this TV series with the same cast and crew; *Out 1***, a 13-hour movie,**
was aligned with *Out 1: Spectre***, its shortened 4-hour variation.**
- Some errors were caused by the very naive string comparison **approach,**
that fails to discover, e.g., that *Sugata Sanshirô* and *Sanshiro Sugata* **refer**
to the same movie. It is very likely that using an improved string comparison technique would further increase precision and recall of paris.

paris **could align 80 % of the relations of** IMDb and YAGO**, with a precision** of 100 %. paris **mapped half of the** IMDb **classes correctly to more general or** equal YAGO **classes (at threshold 0). It performs less well in the other direction.**
This is because YAGO **contains mostly famous people, many of whom appeared**
in some movie or documentary on IMDb**. Thus,** paris **believes that a class such**
as *People from Central Java* is a subclass of *actor*.

As illustrated here, alignment of instances and relations work very well in paris**, whereas class alignment leaves still some room for improvement. Overall,**
the results are very satisfactory, as this constitutes, to the best of our knowledge, the first holistic alignment of instances, relations, and classes on some of the world's largest ontologies, without any prior knowledge, tuning, or training.

## 7 Conclusion

We have presented paris**, an algorithm for the automated alignment of RDFS**
ontologies. Unlike most other approaches, paris **computes alignments not only**
for instances, but also for classes and relations. It does not need training data and it does not require any parameter tuning. paris **is based on a probabilistic**
framework that captures the interplay between schema alignment and instance matching in a natural way, thus providing a holistic solution to the ontology alignment problem. Experiments show that our approach works extremely well in practice.

paris **does not use any kind of heuristics on relation names, which allows**
aligning relations with completely different names. We conjecture that the name heuristics of more traditional schema-alignment techniques could be factored into the model.

Currently, paris **cannot deal with structural heterogeneity. If one ontology**
models an event by a relation (such as *wonAward***), while the other one models** it by an event entity (such as *winningEvent*, with relations winner, award, *year*),
then paris **will not be able to find matches. The same applies if one ontology**
is more fine-grained than the other one (specifying, e.g., cities as birth places instead of countries), or if one ontology treats cities as entities, while the other one refers to them by strings. For future work, we plan to address these types of challenges. We also plan to analyze under which conditions our equations are guaranteed to converge. It would also be interesting to apply paris **to more** than two ontologies. This would further increase the usefulness of paris **for the**
dream of the Semantic Web.