Ontology Alignment at the Instance and Schema Level

We present PARIS, an approach for the automatic alignment of ontologies. PARIS aligns not only instances, but also relations and classes. Alignments at the instance-level cross-fertilize with alignments at the schema-level. Thereby, our system provides a truly holistic solution to the problem of ontology alignment. The heart of the approach is probabilistic. This allows PARIS to run without any parameter tuning. We demonstrate the efficiency of the algorithm and its precision through extensive experiments. In particular, we obtain a precision of around 90% in experiments with two of the world's largest ontologies.Comment: Technical Report at INRIA RT-040

The Locality and Symmetry of Positional Encodings

Positional Encodings (PEs) are used to inject word-order information into transformer-based language models. While they can significantly enhance the quality of sentence representations, their specific contribution to language models is not fully understood, especially given recent findings that various positional encodings are insensitive to word order. In this work, we conduct a systematic study of positional encodings in \textbf{Bidirectional Masked Language Models} (BERT-style) , which complements existing work in three aspects: (1) We uncover the core function of PEs by identifying two common properties, Locality and Symmetry; (2) We show that the two properties are closely correlated with the performances of downstream tasks; (3) We quantify the weakness of current PEs by introducing two new probing tasks, on which current PEs perform poorly. We believe that these results are the basis for developing better PEs for transformer-based language models. The code is available at \faGithub~ \url{https://github.com/tigerchen52/locality\_symmetry}Comment: Long Paper in Findings of EMNLP2

A la recherche des connaissances du Web...

International audienceLe Web contient une masse impressionnante de données, plus ou moins explicites et plus ou moins accessibles aux machines. Nous discutons ici des grandes tendances pour le management de ces données : l’extraction de connaissances du Web, l’enrichissement des connaissances par la communauté des internautes, leur représentation sous forme logique, et leur distribution à travers toutes les facettes du web. Nous allons montrer comment ces développements rendent les données sur le Web plus sémantiques, plus maniables par les machines, plus accessibles aux applications et donc finalement plus utiles pour l’humain

Combining linguistic and statistical analysis to extract relations from web documents

Saarbrücken/Germany suchanek aO mpii.mpg.de The World Wide Web provides a nearly endless source of knowledge, which is mostly given in natural language. A first step towards exploiting this data automatically could be to extract pairs of a given semantic relation from text documents – for example all pairs of a person and her birthdate. One strategy for this task is to find text patterns that express the semantic relation, to generalize these patterns, and to apply them to a corpus to find new pairs. In this paper, we show that this approach profits significantly when deep linguistic structures are used instead of surface text patterns. We demonstrate how linguistic structures can be represented for machine learning, and we provide a theoretical analysis of the pattern matching approach. We show the practical relevance of our approach by extensive experiments with our prototype system Leila

Keynote: A Hitchhiker's Guide to Ontology

International audience<p>In this talk, I will present our recent work in the area ofknowledge bases. It covers 4 areas of research aroundontologies and knowledge bases: The first area is theconstruction of the YAGO knowledge base. YAGO is nowmulitlingual, and has grown into a larger project at the MaxPlanck Institute for Informatics and Télécom ParisTech. Thesecond area is the alignment of knowledge bases. Thisincludes the alignment of classes, instances, and relationsacross knowledge bases. The third area is rule mining. Ourproject finds semantic correlations in the form of Horn rulesin the knowledge base. I will also talk about watermarkingapproaches to trace the provenance of ontological data.Finally, I will show applications of the knowledge base formining news corpora.</p

LEILA: Learning to Extract Information by Linguistic Analysis

One of the challenging tasks in the context of the Semantic Web is to automatically extract instances of binary relations from Web documents – for example all pairs of a person and the corresponding birthdate. In this paper, we present LEILA, a system that can extract instances of arbitrary given binary relations from natural language Web documents – without human interaction. Different from previous approaches, LEILA uses a deep syntactic analysis. This results in consistent improvements over comparable systems (such as e.g. Snowball or TextToOnto)