Dominique Ritze

Matching HTML Tables to DBpedia

Millions of HTML tables containing structured data can be found on the Web. With their wide coverage, these tables are potentially very useful for filling missing values and extending cross-domain knowledge bases such as DBpedia, YAGO, or the Google Knowledge Graph. As a prerequisite for being able to use table data for knowledge base extension, the HTML tables need to be matched with the knowledge base, meaning that correspondences between table rows/columns and entities/schema elements of the knowledge base need to be found. This paper presents the T2D gold standard for measuring and comparing the performance of HTML table to knowledge base matching systems. T2D consists of 8 700 schema-level and 26 100 entity-level correspondences between the WebDataCommons Web Tables Corpus and the DBpedia knowledge base. In contrast related work on HTML table to knowledge base matching, the Web Tables Corpus (147 million tables), the knowledge base, as well as the gold standard are publicly available. The gold standard is used afterward to evaluate the performance of T2K Match, an iterative matching method which combines schema and instance matching. T2K Match is designed for the use case of matching large quantities of mostly small and narrow HTML tables against large cross-domain knowledge bases. The evaluation using the T2D gold standard shows that T2K Match discovers table-to-class correspondences with a precision of 94%, row-to-entity correspondences with a precision of 90%, and column-to-property correspondences with a precision of 77%.

Towards Evaluating Interactive Ontology Matching Tools

With a growing number of ontologies used in the semantic web, agents can fully make sense of different datasets only if correspondences between those ontologies are known. Ontology matching tools have been proposed to find such correspondences. While the current research focus is mainly on fully automatic matching tools, some approaches have been proposed that involve the user in the matching process. However, there are currently no benchmarks and test methods to compare such tools. In this paper, we introduce a number of quality measures for interactive ontology matching tools, and we discuss means to automatically run benchmark tests for such tools. To demonstrate how those evaluation can be designed, we show examples on assessing the quality of interactive matching tools which involve the user in matcher selection and matcher parametrization.

Profiling the Potential of Web Tables for Augmenting Cross-domain Knowledge Bases

Cross-domain knowledge bases such as DBpedia, YAGO, or the Google Knowledge Graph have gained increasing attention over the last years and are starting to be deployed within various use cases. However, the content of such knowledge bases is far from being complete, far from always being correct, and suffers from deprecation (i.e. population numbers become outdated after some time). Hence, there are efforts to leverage various types of Web data to complement, update and extend such knowledge bases. A source of Web data that potentially provides a very wide coverage are millions of relational HTML tables that are found on the Web. The existing work on using data from Web tables to augment cross-domain knowledge bases reports only aggregated performance numbers. The actual content of the Web tables and the topical areas of the knowledge bases that can be complemented using the tables remain unclear. In this paper, we match a large, publicly available Web table corpus to the DBpedia knowledge base. Based on the matching results, we profile the potential of Web tables for augmenting different parts of cross-domain knowledge bases and report detailed statistics about classes, properties, and instances for which missing values can be filled using Web table data as evidence. In order to estimate the potential quality of the new values, we empirically examine the Local Closed World Assumption and use it to determine the maximal number of correct facts that an ideal data fusion strategy could generate. Using this as ground truth, we compare three data fusion strategies and conclude that knowledge-based trust outperforms PageRank- and voting-based fusion.

The Mannheim Search Join Engine

A Search Join is a join operation which extends a user-provided table with additional attributes based on a large corpus of heterogeneous data originating from the Web or corporate intranets. Search Joins are useful 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 attributes containing the headquarters, turnover, and revenue 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. This article presents the Mannheim Search Join Engine which automatically performs such table extension operations based on a large corpus of Web data. Given a local table, the Mannheim Search Join Engine searches the corpus for additional data describing the entities contained in the input table. The discovered data are joined with the local table and are consolidated using schema matching and data fusion techniques. As a result, the user is presented with an extended table and given the opportunity to examine the provenance of the added data. We evaluate the Mannheim Search Join Engine using heterogeneous data originating from over one million different websites. The data corpus consists of HTML tables, as well as Linked Data and Microdata annotations which are converted into tabular form. Our experiments show that the Mannheim Search Join Engine achieves a coverage close to 100% and a precision of around 90% for the tasks of extending tables describing cities, companies, countries, drugs, books, films, and songs.

RODI: A Benchmark for Automatic Mapping Generation in Relational-to-Ontology Data Integration

A major challenge in information management today is the integration of huge amounts of data distributed across multiple data sources. A suggested approach to this problem is ontology-based data integration where legacy data systems are integrated via a common ontology that represents a unified global view over all data sources. However, data is often not natively born using these ontologies. Instead, much data resides in legacy relational databases. Therefore, mappings that relate the legacy relational data sources to the ontology need to be constructed. Recent techniques and systems that automatically construct such mappings have been developed. The quality metrics of these systems are, however, often only based on self-designed benchmarks. This paper introduces a new publicly available benchmarking suite called RODI, which is designed to cover a wide range of mapping challenges in Relational-to-Ontology Data Integration scenarios. RODI provides a set of different relational data sources and ontologies representing a wide range of mapping challenges as well as a scoring function with which the performance of relational-to-ontology mapping construction systems may be evaluated.

A Large Public Corpus of Web Tables containing Time and Context Metadata

The Web contains vast amounts of HTML tables. Most of these tables are used for layout purposes, but a small subset of the tables is relational, meaning that they contain structured data describing a set of entities [2]. As these relational Web tables cover a very wide range of different topics, there is a growing body of research investigating the utility of Web table data for completing cross-domain knowledge bases [6], for extending arbitrary tables with additional attributes [7, 4], as well as for translating data values [5]. The existing research shows the potentials of Web tables. However, comparing the performance of the different systems is difficult as up till now each system is evaluated using a different corpus of Web tables and as most of the corpora are owned by large search engine companies and are thus not accessible to the public. In this poster, we present a large public corpus of Web tables which contains over 233 million tables and has been extracted from the July 2015 version of the CommonCrawl. By publishing the corpus as well as all tools that we used to extract it from the crawled data, we intend to provide a common ground for evaluating Web table systems. The main difference of the corpus compared to an earlier corpus that we extracted from the 2012 version of the CommonCrawl as well as the corpus extracted by Eberius et al. [3] from the 2014 version of the CommonCrawl is that the current corpus contains a richer set of metadata for each table. This metadata includes table-specific information such as table orientation, table caption, header row, and key column, but also context information such as the text before and after the table, the title of the HTML page, as well as timestamp information that was found before and after the table. The context information can be useful for recovering the semantics of a table [7]. The timestamp information is crucial for fusing time-depended data, such as alternative population numbers for a city [8].

Identifying references to datasets in publications

Research data and publications are usually stored in separate and structurally distinct information systems. Often, links between these resources are not explicitly available which complicates the search for previous research. In this paper, we propose a pattern induction method for the detection of study references in full texts. Since these references are not specified in a standardized way and may occur inside a variety of different contexts --- i.e., captions, footnotes, or continuous text --- our algorithm is required to induce very flexible patterns. To overcome the sparse distribution of training instances, we induce patterns iteratively using a bootstrapping approach. We show that our method achieves promising results for the automatic identification of data references and is a first step towards building an integrated information system.

State-of-the-Art in Multilingual and Cross-Lingual Ontology Matching

Ontology matching is one of the key solutions for solving the heterogeneity problem in the Semantic Web. Nowadays, the increasing amount of multilingual data on the Web and the consequent development of ontologies in different natural languages have pushed the need for multilingual and cross-lingual ontology matching. This chapter provides an overview of multilingual and cross-lingual ontology matching. We formally define the problem of matching multilingual and cross-lingual ontologies and provide a classification of different techniques and approaches. Systematic evaluations of these techniques are discussed with an emphasis on standard and freely available data sets and systems.

Multilingual Ontology Matching Evaluation – A First Report on Using MultiFarm

This paper reports on the first usage of the MultiFarm dataset for evaluating ontology matching systems. This dataset has been designed as a comprehensive benchmark for multilingual ontology matching. In a first set of experiments, we analyze how state-of-the-art matching systems – not particularly designed for the task of multilingual ontology matching – perform on this dataset. These experiments show the hardness of MultiFarm and result in baselines for any algorithm specifically designed for multilingual ontology matching. We continue with a second set of experiments, where we analyze three systems that have been extended with specific strategies to solve the multilingual matching problem. This paper allows us to draw relevant conclusions for both multilingual ontology matching and ontology matching evaluation in general.

RESTful open workflows for data provenance and reuse

In this paper, we present a workflow model together with an implementation following the Linked Data principles and the principles for RESTful web services. By means of RDF-based specifications of web services, workflows, and runtime information, we establish a full provenance chain for all resources created within these workflows.