Wikipedia tables represent an important resource, where information is organized w.r.t table schemas consisting of columns. In turn each column, may contain instance values that point to other Wikipedia articles or primitive values (e.g. numbers, strings etc.).In this work, we focus on the problem of interlinking Wikipedia tables for two types of table relations: equivalent and subPartOf. Through such relations, we can further harness semantically related information by accessing related tables or facts therein. Determining the relation type of a table pair is not trivial, as it is dependent on the schemas, the values therein, and the semantic overlap of the cell values in the corresponding tables.We propose TableNet, an approach that constructs a knowledge graph of interlinked tables with subPartOf and equivalent relations. TableNet consists of two main steps: (i) for any source table we provide an efficient algorithm to find all candidate related tables with high coverage, and (ii) a neural based approach, which takes into account the table schemas, and the corresponding table data, we determine with high accuracy the table relation for a table pair. We perform an extensive experimental evaluation on the entire Wikipedia with more than 3.2 million tables. We show that with more than 88% we retain relevant candidate tables pairs for alignment. Consequentially, with an accuracy of 90% we are able to align tables with subPartOf or equivalent relations. Comparisons with existing competitors show that TableNet has superior performance in terms of coverage and alignment accuracy. ACM Reference Format:

Many data providers make their data available through Web service APIs. In order to unleash the potential of these sources for intelligent applications, the data has to be combined across different APIs. However, due to the heterogeneity of schemas, the integration of different APIs remains a mainly manual task to date. In this paper, we model an API method as a view with binding patterns over a global RDF schema. We present an algorithm that can automatically infer the view definition of a method in the global schema. We also show how to compute transformation functions that can transform API call results into this schema. The key idea of our approach is to exploit the intersection of API call results with a knowledge base and with other call results. Our experiments on more than 50 real Web services show that we can automatically infer the schema with a precision of 81%-100%.

Optical character recognition (OCR) is crucial for a deeper access to historical collections. OCR needs to account for orthographic variations, typefaces, or language evolution (i.e., new letters, word spellings), as the main source of character, word, or word segmentation transcription errors. For digital corpora of historical prints, the errors are further exacerbated due to low scan quality and lack of language standardization. For the task of OCR post-hoc correction, we propose a neural approach based on a combination of recurrent (RNN) and deep convolutional network (ConvNet) to correct OCR transcription errors. At character level we flexibly capture errors, and decode the corrected output based on a novel attention mechanism. Accounting for the input and output similarity, we propose a new loss function that rewards the model’s correcting behavior. Evaluation on a historical book corpus in German language shows that our models are robust in capturing diverse OCR transcription errors and reduce the word error rate of 32.3% by more than 89%.

The large number of linked datasets in the Web, and their diversity in terms of schema representation has led to a fragmented dataset landscape. Querying and addressing information needs that span across disparate datasets requires the alignment of such schemas. Majority of schema and ontology alignment approaches focus exclusively on class alignment. Yet, relation alignment has not been fully addressed, and existing approaches fall short on addressing the dynamics of datasets and their size. In this work, we address the problem of relation alignment across disparate linked datasets. Our approach focuses on two main aspects. First, online relation alignment, where we do not require full access, and sample instead for a minimal subset of the data. Thus, we address the main limitation of existing work on dealing with the large scale of linked datasets, and in cases where the datasets provide only query access. Second, we learn supervised machine learning models for which we employ various features or matchers that account for the diversity of linked datasets at the instance level. We perform an experimental evaluation on real-world linked datasets, DBpedia, YAGO, and Freebase. The results show superior performance against state-of-the-art approaches in schema matching, with an average relation alignment accuracy of 84%. In addition, we show that relation alignment can be performed efficiently at scale.

No abstract