Accommodations deduplication

The problem to address is the accommodations deduplication. The deduplication is a special case of entity resolution (ER) consisting in grouping different representa- tions of the same entity, usually coming from different sources. The deduplication is a complex process that requires several phases, being the most common ones, block- ing and pair resolution. A new phase is introduced in addition to the previous ones, clustering, that was not considered in previous work. We aim to build a framework able to cover the different phases and design a strategy of clustering maximizing the precision with the maximal possible recall

Toponym matching through deep neural networks

Toponym matching, i.e. pairing strings that represent the same real-world location, is a fundamental problemfor several practical applications. The current state-of-the-art relies on string similarity metrics, either specifically developed for matching place names or integrated within methods that combine multiple metrics. However, these methods all rely on common sub-strings in order to establish similarity, and they do not effectively capture the character replacements involved in toponym changes due to transliterations or to changes in language and culture over time. In this article, we present a novel matching approach, leveraging a deep neural network to classify pairs of toponyms as either matching or nonmatching. The proposed network architecture uses recurrent nodes to build representations from the sequences of bytes that correspond to the strings that are to be matched. These representations are then combined and passed to feed-forward nodes, finally leading to a classification decision. We present the results of a wide-ranging evaluation on the performance of the proposed method, using a large dataset collected from the GeoNames gazetteer. These results show that the proposed method can significantly outperform individual similarity metrics from previous studies, as well as previous methods based on supervised machine learning for combining multiple metrics

Accommodations deduplication

The problem to address is the accommodations deduplication. The deduplication is a special case of entity resolution (ER) consisting in grouping different representa- tions of the same entity, usually coming from different sources. The deduplication is a complex process that requires several phases, being the most common ones, block- ing and pair resolution. A new phase is introduced in addition to the previous ones, clustering, that was not considered in previous work. We aim to build a framework able to cover the different phases and design a strategy of clustering maximizing the precision with the maximal possible recall