Web-scale web table to knowledge base matching

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

Web table integration and profiling for knowledge base augmentation

HTML tables on web pages ("web tables") have been used successfully as a data source for several applications. They can be extracted from web pages on a large-scale, resulting in corpora of millions of web tables. But, until today only little is known about the general distribution of topics and specific types of data that are contained in the tables that can be found on the Web. But this knowledge is essential to understanding the potential application areas and topical coverage of web tables as a data source. Such knowledge can be obtained through the integration of web tables with a knowledge base, which enables the semantic interpretation of their content and allows for their topical profiling. In turn, the knowledge base can be augmented by adding new statements from the web tables. This is challenging, because the data volume and variety are much larger than in traditional data integration scenarios, in which only a small number of data sources is integrated. The contributions of this thesis are methods for the integration of web tables with a knowledge base and the profiling of large-scale web table corpora through the application of these methods. For this profiling, two corpora of 147 million and 233 million web tables, respectively, are created and made publicly available. These corpora are two of only three that are openly available for research on web tables. Their data profile reveals that most web tables have only very few rows, with a median of 6 rows per web table, and between 35% and 52% of all columns contain non-textual values, such as numbers or dates. These two characteristics have been mostly ignored in the literature about web tables and are addressed by the methods presented in this thesis. The first method, T2K Match, is an algorithm for semantic table interpretation that annotates web tables with classes, properties, and entities from a knowledge base. Other than most algorithms for these tasks, it is not limited to the annotation of columns that contain the names of entities. Its application to a large-scale web table corpus results in the most fine-grained topical data profile of web tables at the time of writing, but also reveals that small web tables cannot be processed with high quality. For such small web tables, a method that stitches them into larger tables is presented and shown to drastically improve the quality of the results. The data profile further shows that the majority of the columns in the web tables, where classes and entities can be recognised, have no corresponding properties in the knowledge base. This makes them candidates for new properties that can be added to the knowledge base. The current methods for this task, however, suffer from the oversimplified assumption that web tables only contain binary relations. This results in the extraction of incomplete relations from the web tables as new properties and makes their correct interpretation impossible. To increase the completeness, a method is presented that generates additional data from the context of the web tables and synthesizes n-ary relations from all web tables of a web site. The application of this method to the second large-scale web table corpus shows that web tables contain a large number of n-ary relations. This means that the data contained in web tables is of higher complexity than previously assumed

An ontology driven annotation of data tables

This paper deals with the integration of data extracted from the web into an existing data warehouse indexed by a domain ontology. We are specially interested in data tables extracted from scientific publications found on the web. We propose a way to annotate data tables from the web according to a given domain ontology. In this paper we present the different steps of our annotation process. The columns of a web data table are first segregated according to whether they represent numeric or symbolic data. Then, we annotate the numeric (resp.symbolic) columns with their corresponding numeric (resp. symbolic) type found in the ontology. Our approach combines different evidences from the column contents and from the column title to find the best corresponding type in the ontology. The relations represented by the web data table are recognized using both the table title and the types of the columns that were previously annotated. We give experimental results of our annotation process, our application domain being food microbiology