Entity Discovery and Annotation in Tables

International audienceThe Web is rich of tables (e.g., HTML tables, speadsheets, Google Fusion tables) that host a considerable wealth of high-quality relational data. Unlike unstructured texts, tables usually favour the automatic extraction of data because of their regular structure and properties. The data extraction is usually complemented by the annotation of the table, which determines its semantics by identifying a type for each column, the relations between columns, if any, and the entities that occur in each cell. In this paper, we focus on the problem of discovering and annotating entities intables. More specifically, we describe an algorithm that identifies the rows of a table that contain information on entities of specific types (e.g., restaurant, museum, theatre) derived from an ontology and determines the cells in which the names of those entities occur. We implemented this algorithm while developing a faceted browser over a repository of RDF data on points of interest of cities that we extracted from Google Fusion Tables. We claim that our algorithm complements the existing approaches, which annotate entities in a table based on a pre-compiled reference catalogue that lists the types of a finite set of entities; as a result, they are unable to discover and annotate entities that do not belong to the reference catalogue. Instead, we train our algorithm to look for information on previously unseen entities on the Web so as to annotate them with the correct type

Concept Expansion Using Web Tables

ABSTRACT We study the following problem: given the name of an ad-hoc concept as well as a few seed entities belonging to the concept, output all entities belonging to it. Since producing the exact set of entities is hard, we focus on returning a ranked list of entities. Previous approaches either use seed entities as the only input, or inherently require negative examples. They suffer from input ambiguity and semantic drift, or are not viable options for ad-hoc tail concepts. In this paper, we propose to leverage the millions of tables on the web for this problem. The core technical challenge is to identify the "exclusive" tables for a concept to prevent semantic drift; existing holistic ranking techniques like personalized PageRank are inadequate for this purpose. We develop novel probabilistic ranking methods that can model a new type of table-entity relationship. Experiments with real-life concepts show that our proposed solution is significantly more effective than applying state-of-the-art set expansion or holistic ranking techniques

Concept Expansion Using Web Tables

We study the following problem: given the name of an ad-hoc con-cept as well as a few seed entities belonging to the concept, output all entities belonging to it. Since producing the exact set of entities is hard, we focus on returning a ranked list of entities. Previous approaches either use seed entities as the only input, or inherently require negative examples. They suffer from input ambiguity and semantic drift, or are not viable options for ad-hoc tail concepts. In this paper, we propose to leverage the millions of tables on the web for this problem. The core technical challenge is to identify the “exclusive ” tables for a concept to prevent semantic drift; ex-isting holistic ranking techniques like personalized PageRank are inadequate for this purpose. We develop novel probabilistic rank-ing methods that can model a new type of table-entity relationship. Experiments with real-life concepts show that our proposed solu-tion is significantly more effective than applying state-of-the-art set expansion or holistic ranking techniques

Automated Structural and Spatial Comprehension of Data Tables

Data tables on the Web hold large quantities of information, but are difficult to search, browse, and merge using existing systems. This dissertation presents a collection of techniques for extracting, processing, and querying tables that contain geographic data, by harnessing the coherence of table structures for retrieval tasks. Data tables, including spreadsheets, HTML tables, and those found in rich document formats, are the standard way of communicating structured data for typical computer users. Notably, geographic tables (i.e., those containing names of locations) constitute a large fraction of publicly-available data tables and are ripe for exposure to Internet users who are increasingly comfortable interacting with geographic data using web-based maps. Of particular interest is the creation of a large repository of geographic data tables that would enable novel queries such as "find vacation itineraries geographically similar to mine" for use in trip planning or "find demographic datasets that cover regions X, Y, and Z" for sociological research. In support of these goals, this dissertation identifies several methods for using the structure and context of data tables to improve the interpretation of the contents, even in the presence of ambiguity. First, a method for identifying functional components of data tables is presented, capitalizing on techniques for sequence labeling that are used in natural language processing. Next, a novel automated method for converting place references to physical latitude/longitude values, a process known as geotagging, is applied to tables with high accuracy. A classification procedure for identifying a specific class of geographic table, the travel itinerary, is also described, which borrows inspiration from optimization techniques for the traveling salesman problem (TSP). Finally, methods for querying spatially similar tables are introduced and several mechanisms for visualizing and interacting with the extracted geographic data are explored

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

Entity Discovery and Annotation in Tables

International audienceThe Web is rich of tables (e.g., HTML tables, speadsheets, Google Fusion tables) that host a considerable wealth of high-quality relational data. Unlike unstructured texts, tables usually favour the automatic extraction of data because of their regular structure and properties. The data extraction is usually complemented by the annotation of the table, which determines its semantics by identifying a type for each column, the relations between columns, if any, and the entities that occur in each cell. In this paper, we focus on the problem of discovering and annotating entities intables. More specifically, we describe an algorithm that identifies the rows of a table that contain information on entities of specific types (e.g., restaurant, museum, theatre) derived from an ontology and determines the cells in which the names of those entities occur. We implemented this algorithm while developing a faceted browser over a repository of RDF data on points of interest of cities that we extracted from Google Fusion Tables. We claim that our algorithm complements the existing approaches, which annotate entities in a table based on a pre-compiled reference catalogue that lists the types of a finite set of entities; as a result, they are unable to discover and annotate entities that do not belong to the reference catalogue. Instead, we train our algorithm to look for information on previously unseen entities on the Web so as to annotate them with the correct type

Entity Discovery and Annotation in Tables

The Web is rich of tables (e.g., HTML tables, spreadsheets, Google Fusion Tables) that host a considerable wealth of high-quality relational data. Unlike unstructured texts, tables usually favour the automatic extraction of data because of their regular structure and properties. The data extraction is usually complemented by the annotation of the table, which determines its semantics by identifying a type for each column, the relations between columns, if any, and the entities that occur in each cell. In this paper, we focus on the problem of discovering and annotating entities in tables. More specifically, we describe an algorithm that identifies the rows of a table that contain information on entities of specific types (e.g., restaurant, museum, theatre) derived from an ontology and determines the cells in which the names of those entities occur. We implemented this algorithm while developing a faceted browser over a repository of RDF data on points of interest of cities that we extracted from Google Fusion Tables. We claim that our algorithm complements the existing approaches, which annotate entities in a table based on a pre-compiled reference catalogue that lists the types of a finite set of entities; as a result, they are unable to discover and annotate entities that do not belong to the reference catalogue. Instead, we train our algorithm to look for information on previously unseen entities on the Web so as to annotate them with the correct type. 1