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

Extending Tables with Data from over a Million Websites

Abstract. This Big Data Track submission demonstrates how the BTC 2014 dataset, Microdata annotations from thousands of websites, as well as millions of HTML tables are used to extend local tables with additional columns. Ta-ble extension is a useful operation within a wide range of application scenarios: Imagine you are an analyst having a local table describing companies and you want to extend this table with the headquarter of each company. Or imagine you are a film enthusiast and want to extend a table describing films with attributes like director, genre, and release date of each film. The Mannheim Search Joins Engine automatically performs such table extension operations based on a large data corpus gathered from over a million websites that publish structured data in various formats. Given a local table, the Mannheim Search Joins Engine searches the corpus for additional data describing the entities of the input table. The dis-covered data are then joined with the local table and their content is consolidated using schema matching and data fusion methods. As result, the user is presented with an extended table and given the opportunity to examine the provenance o

Results of the Ontology Alignment Evaluation Initiative 2014

dragisic2014aInternational audienceOntology matching consists of finding correspondences between semantically related entities of two ontologies. OAEI campaigns aim at comparing ontology matching systems on precisely defined test cases. These test cases can use ontologies of different nature (from simple thesauri to expressive OWL ontologies) and use different modalities, e.g., blind evaluation, open evaluation and consensus. OAEI 2014 offered 7 tracks with 9 test cases followed by 14 participants. Since 2010, the campaign has been using a new evaluation modality which provides more automation to the evaluation. This paper is an overall presentation of the OAEI 2014 campaign

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