We demonstrate a system to automatically grab data from data intensive web sites. The system first infers a model that describes at the intensional level the web site as a collection of classes; each class represents a set of structurally homogeneous pages, and it is associated with a small set of representative pages. Based on the model a library of wrappers, one per class, is then inferred, with the help an external wrapper generator. The model, together with the library of wrappers, can thus be used to navigate the site and extract the data.

Knowledge Graphs (KGs) have found many applications in industrial and in academic settings, which in turn, have motivated considerable research efforts towards large-scale information extraction from a variety of sources. Despite such efforts, it is well known that even the largest KGs suffer from incompleteness; Link Prediction (LP) techniques address this issue by identifying missing facts among entities already in the KG. Among the recent LP techniques, those based on KG embeddings have achieved very promising performance in some benchmarks. Despite the fast-growing literature on the subject, insufficient attention has been paid to the effect of the design choices in those methods. Moreover, the standard practice in this area is to report accuracy by aggregating over a large number of test facts in which some entities are vastly more represented than others; this allows LP methods to exhibit good results by just attending to structural properties that include such entities, while ignoring the remaining majority of the KG. This analysis provides a comprehensive comparison of embedding-based LP methods, extending the dimensions of analysis beyond what is commonly available in the literature. We experimentally compare the effectiveness and efficiency of 18 state-of-the-art methods, consider a rule-based baseline, and report detailed analysis over the most popular benchmarks in the literature.

We demonstrate a system to automatically grab data from data intensive web sites. The system first infers a model that describes at the intensional level the web site as a collection of classes; each class represents a set of structurally homogeneous pages, and it is associated with a small set of representative pages. Based on the model a library of wrappers, one per class, is then inferred, with the help an external wrapper generator. The model, together with the library of wrappers, can thus be used to navigate the site and extract the data.

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