Web-scale web table to knowledge base matching (2017)

Millions of relational HTML tables are found on the World Wide Web. In contrast\nto unstructured text, relational web tables provide a compact representation of entities\ndescribed by attributes. The data within these tables covers a broad topical\nrange. Web table data is used for question answering, augmentation of search results,\nand knowledge base completion. Until a few years ago, only search engines\ncompanies like Google and Microsoft owned large web crawls from which web\ntables are extracted. Thus, researches outside the companies have not been able to\nwork with web tables.\nIn this thesis, the first publicly available web table corpus containing millions of\nweb tables is introduced. The corpus enables interested researchers to experiment\nwith web tables. A profile of the corpus is created to give insights to the characteristics\nand topics. Further, the potential of web tables for augmenting cross-domain\nknowledge bases is investigated. For the use case of knowledge base augmentation,\nit is necessary to understand the web table content. For this reason, web\ntables are matched to a knowledge base. The matching comprises three matching\ntasks: instance, property, and class matching. Existing web table to knowledge\nbase matching systems either focus on a subset of these matching tasks or are evaluated\nusing gold standards which also only cover a subset of the challenges that\narise when matching web tables to knowledge bases.\nThis thesis systematically evaluates the utility of a wide range of different features\nfor the web table to knowledge base matching task using a single gold standard.\nThe results of the evaluation are used afterwards to design a holistic matching\nmethod which covers all matching tasks and outperforms state-of-the-art web table\nto knowledge base matching systems. In order to achieve these goals, we first\npropose the T2K Match algorithm which addresses all three matching tasks in an\nintegrated fashion. In addition, we introduce the T2D gold standard which covers\na wide variety of challenges. By evaluating T2K Match against the T2D gold standard,\nwe identify that only considering the table content is insufficient. Hence, we\ninclude features of three categories: features found in the table, in the table context\nlike the page title, and features that base on external resources like a synonym dictionary.\nWe analyze the utility of the features for each matching task. The analysis\nshows that certain problems cannot be overcome by matching each table in isolation\nto the knowledge base. In addition, relying on the features is not enough for the\nproperty matching task. Based on these findings, we extend T2K Match into T2K\nMatch++ which exploits indirect matches to web tables about the same topic and\nuses knowledge derived from the knowledge base. We show that T2K Match++\noutperforms all state-of-the-art web table to knowledge base matching approaches\non the T2D and Limaye gold standard. Most systems show good results on one\nmatching task but T2K Match++ is the only system that achieves F-measure scores\nabove 0:8 for all tasks. Compared to results of the best performing system TableMiner+,\nthe F-measure for the difficult property matching task is increased by 0.08,\nfor the class and instance matching task by 0.05 and 0.03, respectively.