Multilingual Schema Matching for Wikipedia Infoboxes

Recent research has taken advantage of Wikipedia's multilingualism as a resource for cross-language information retrieval and machine translation, as well as proposed techniques for enriching its cross-language structure. The availability of documents in multiple languages also opens up new opportunities for querying structured Wikipedia content, and in particular, to enable answers that straddle different languages. As a step towards supporting such queries, in this paper, we propose a method for identifying mappings between attributes from infoboxes that come from pages in different languages. Our approach finds mappings in a completely automated fashion. Because it does not require training data, it is scalable: not only can it be used to find mappings between many language pairs, but it is also effective for languages that are under-represented and lack sufficient training samples. Another important benefit of our approach is that it does not depend on syntactic similarity between attribute names, and thus, it can be applied to language pairs that have distinct morphologies. We have performed an extensive experimental evaluation using a corpus consisting of pages in Portuguese, Vietnamese, and English. The results show that not only does our approach obtain high precision and recall, but it also outperforms state-of-the-art techniques. We also present a case study which demonstrates that the multilingual mappings we derive lead to substantial improvements in answer quality and coverage for structured queries over Wikipedia content.Comment: VLDB201

CÁC PHƯƠNG PHÁP ÁNH XẠ THUỘC TÍNH HỘP THÔNG TIN WIKIPEDIA ĐẾN WIKIDATA

Wikidata is an open, online database which stores the common resources of other Wikimedia projects. Unifying Wikipedia infoboxes was described in Phase II of Wikidata plan which aims to augment auto-translation to Wikipedia infobox templates and deals with the diversity of Infobox data in all languages. In this paper, we offer some approaches to map Infobox properties to Wikidata for improving our enrichment model. Our results can be a valuable resource for Wikidata to alignInfobox properties. We mainly focus on how to map Vietnamese and English properties to Wikidata.Wikidata là một cơ sở dữ liệu trực tuyến mở lưu trữ các tài nguyên chung của các dự án liên quan do tổ chức Wikimedia quản lý. Việc đồng nhất hóa các hộp thông tin (infobox) của Wikipedia được nêu trong kế hoạch giai đoạn 2 của Wikidata. Theo đó, các hộp thông tin sẽ được đồng nhất hóa để tránh tình trạng đa dạng dữ liệu giữa các dự án ngôn ngữ. Đồng thời, nhóm phát triển Wikidata cũng lên kế hoạch phát triển hệ thống tự động diễn dịch các thuộc tính của các hộp thông tin Wikipedia. Bài báo này nhắm đến việc đưa ra một vài phương pháp tiếp cận để ánh xạ các thuộc tính của hộp thông tin đến Wikidata, từ đó nâng cao khả năng phát triển làm giàu nội dung cho các bài viết Wikipedia. Chúng tôi tập trung chủ yếu vào việc ánh xạ các thuộc tính ở Wikipedia Tiếng Việt và Wikipedia Tiếng Anh

Doctor of Philosophy

dissertationThe explosion of structured Web data (e.g., online databases, Wikipedia infoboxes) creates many opportunities for integrating and querying these data that go far beyond the simple search capabilities provided by search engines. Although much work has been devoted to data integration in the database community, the Web brings new challenges: the Web-scale (e.g., the large and growing volume of data) and the heterogeneity in Web data. Because there are so much data, scalable techniques that require little or no manual intervention and that are robust to noisy data are needed. In this dissertation, we propose a new and effective approach for matching Web-form interfaces and for matching multilingual Wikipedia infoboxes. As a further step toward these problems, we propose a general prudent schema-matching framework that matches a large number of schemas effectively. Our comprehensive experiments for Web-form interfaces and Wikipedia infoboxes show that it can enable on-the-fly, automatic integration of large collections of structured Web data. Another problem we address in this dissertation is schema discovery. While existing integration approaches assume that the relevant data sources and their schemas have been identified in advance, schemas are not always available for structured Web data. Approaches exist that exploit information in Wikipedia to discover the entity types and their associate schemas. However, due to inconsistencies, sparseness, and noise from the community contribution, these approaches are error prone and require substantial human intervention. Given the schema heterogeneity in Wikipedia infoboxes, we developed a new approach that uses the structured information available in infoboxes to cluster similar infoboxes and infer the schemata for entity types. Our approach is unsupervised and resilient to the unpredictable skew in the entity class distribution. Our experiments, using over one hundred thousand infoboxes extracted from Wikipedia, indicate that our approach is effective and produces accurate schemata for Wikipedia entities

Alignment of multi-cultural knowledge repositories

The ability to interconnect multiple knowledge repositories within a single framework is a key asset for various use cases such as document retrieval and question answering. However, independently created repositories are inherently heterogeneous, reflecting their diverse origins. Thus, there is a need to align concepts and entities across knowledge repositories. A limitation of prior work is the assumption of high afinity between the repositories at hand, in terms of structure and terminology. The goal of this dissertation is to develop methods for constructing and curating alignments between multi-cultural knowledge repositories. The first contribution is a system, ACROSS, for reducing the terminological gap between repositories. The second contribution is two alignment methods, LILIANA and SESAME, that cope with structural diversity. The third contribution, LAIKA, is an approach to compute alignments between dynamic repositories. Experiments with a suite ofWeb-scale knowledge repositories show high quality alignments. In addition, the application benefits of LILIANA and SESAME are demonstrated by use cases in search and exploration.Die Fähigkeit mehrere Wissensquellen in einer Anwendung miteinander zu verbinden ist ein wichtiger Bestandteil für verschiedene Anwendungsszenarien wie z.B. dem Auffinden von Dokumenten und der Beantwortung von Fragen. Unabhängig erstellte Datenquellen sind allerdings von Natur aus heterogen, was ihre unterschiedlichen Herkünfte widerspiegelt. Somit besteht ein Bedarf darin, die Konzepte und Entitäten zwischen den Wissensquellen anzugleichen. Frühere Arbeiten sind jedoch auf Datenquellen limitiert, die eine hohe Ähnlichkeit im Sinne von Struktur und Terminologie aufweisen. Das Ziel dieser Dissertation ist, Methoden für Aufbau und Pflege zum Angleich zwischen multikulturellen Wissensquellen zu entwickeln. Der erste Beitrag ist ein System names ACROSS, das auf die Reduzierung der terminologischen Kluft zwischen den Datenquellen abzielt. Der zweite Beitrag sind die Systeme LILIANA und SESAME, welche zum Angleich eben dieser Datenquellen unter Berücksichtigung deren struktureller Unterschiede dienen. Der dritte Beitrag ist ein Verfahren names LAIKA, das den Angleich dynamischer Quellen unterstützt. Unsere Experimente mit einer Reihe von Wissensquellen in Größenordnung des Web zeigen eine hohe Qualität unserer Verfahren. Zudem werden die Vorteile in der Verwendung von LILIANA und SESAME in Anwendungsszenarien für Suche und Exploration dargelegt