Graph-based methods for large-scale multilingual knowledge integration

Given that much of our knowledge is expressed in textual form, information systems are increasingly dependent on knowledge about words and the entities they represent. This thesis investigates novel methods for automatically building large repositories of knowledge that capture semantic relationships between words, names, and entities, in many different languages. Three major contributions are made, each involving graph algorithms and statistical techniques that combine evidence from multiple sources of information. The lexical integration method involves learning models that disambiguate word meanings based on contextual information in a graph, thereby providing a means to connect words to the entities that they denote. The entity integration method combines semantic items from different sources into a single unified registry of entities by reconciling equivalence and distinctness information and solving a combinatorial optimization problem. Finally, the taxonomic integration method adds a comprehensive and coherent taxonomic hierarchy on top of this registry, capturing how different entities relate to each other. Together, these methods can be used to produce a large-scale multilingual knowledge base semantically describing over 5 million entities and over 16 million natural language words and names in more than 200 different languages.Da ein großer Teil unseres Wissens in textueller Form vorliegt, sind Informationssysteme in zunehmendem Maße auf Wissen über Wörter und den von ihnen repräsentierten Entitäten angewiesen. Gegenstand dieser Arbeit sind neue Methoden zur automatischen Erstellung großer multilingualer Wissensbanken, welche semantische Beziehungen zwischen Wörtern bzw. Namen und Konzepten bzw. Entitäten formal erfassen. In drei Hauptbeiträgen werden jeweils graphtheoretische bzw. statistische Verfahren zur Verknüpfung von Indizien aus mehreren Wissensquellen vorgestellt. Bei der lexikalischen Integration werden statistische Modelle zur Disambiguierung gebildet. Die Entitäten-Integration fasst semantische Einheiten unter Auflösung von Konflikten zwischen Äquivalenz- und Verschiedenheitsinformationen zusammen. Diese werden schließlich bei der taxonomischen Integration durch eine umfassende taxonomische Hierarchie ergänzt. Zusammen können diese Methoden zur Induzierung einer großen multilingualen Wissensbank eingesetzt werden, welche über 5 Millionen Entitäten und über 16 Millionen Wörter und Namen in mehr als 200 Sprachen semantisch beschreibt

Wiktionary: The Metalexicographic and the Natural Language Processing Perspective

Dictionaries are the main reference works for our understanding of language. They are used by humans and likewise by computational methods. So far, the compilation of dictionaries has almost exclusively been the profession of expert lexicographers. The ease of collaboration on the Web and the rising initiatives of collecting open-licensed knowledge, such as in Wikipedia, caused a new type of dictionary that is voluntarily created by large communities of Web users. This collaborative construction approach presents a new paradigm for lexicography that poses new research questions to dictionary research on the one hand and provides a very valuable knowledge source for natural language processing applications on the other hand. The subject of our research is Wiktionary, which is currently the largest collaboratively constructed dictionary project. In the first part of this thesis, we study Wiktionary from the metalexicographic perspective. Metalexicography is the scientific study of lexicography including the analysis and criticism of dictionaries and lexicographic processes. To this end, we discuss three contributions related to this area of research: (i) We first provide a detailed analysis of Wiktionary and its various language editions and dictionary structures. (ii) We then analyze the collaborative construction process of Wiktionary. Our results show that the traditional phases of the lexicographic process do not apply well to Wiktionary, which is why we propose a novel process description that is based on the frequent and continual revision and discussion of the dictionary articles and the lexicographic instructions. (iii) We perform a large-scale quantitative comparison of Wiktionary and a number of other dictionaries regarding the covered languages, lexical entries, word senses, pragmatic labels, lexical relations, and translations. We conclude the metalexicographic perspective by finding that the collaborative Wiktionary is not an appropriate replacement for expert-built dictionaries due to its inconsistencies, quality flaws, one-fits-all-approach, and strong dependence on expert-built dictionaries. However, Wiktionary's rapid and continual growth, its high coverage of languages, newly coined words, domain-specific vocabulary and non-standard language varieties, as well as the kind of evidence based on the authors' intuition provide promising opportunities for both lexicography and natural language processing. In particular, we find that Wiktionary and expert-built wordnets and thesauri contain largely complementary entries. In the second part of the thesis, we study Wiktionary from the natural language processing perspective with the aim of making available its linguistic knowledge for computational applications. Such applications require vast amounts of structured data with high quality. Expert-built resources have been found to suffer from insufficient coverage and high construction and maintenance cost, whereas fully automatic extraction from corpora or the Web often yields resources of limited quality. Collaboratively built encyclopedias present a viable solution, but do not cover well linguistically oriented knowledge as it is found in dictionaries. That is why we propose extracting linguistic knowledge from Wiktionary, which we achieve by the following three main contributions: (i) We propose the novel multilingual ontology OntoWiktionary that is created by extracting and harmonizing the weakly structured dictionary articles in Wiktionary. A particular challenge in this process is the ambiguity of semantic relations and translations, which we resolve by automatic word sense disambiguation methods. (ii) We automatically align Wiktionary with WordNet 3.0 at the word sense level. The largely complementary information from the two dictionaries yields an aligned resource with higher coverage and an enriched representation of word senses. (iii) We represent Wiktionary according to the ISO standard Lexical Markup Framework, which we adapt to the peculiarities of collaborative dictionaries. This standardized representation is of great importance for fostering the interoperability of resources and hence the dissemination of Wiktionary-based research. To this end, our work presents a foundational step towards the large-scale integrated resource UBY, which facilitates a unified access to a number of standardized dictionaries by means of a shared web interface for human users and an application programming interface for natural language processing applications. A user can, in particular, switch between and combine information from Wiktionary and other dictionaries without completely changing the software. Our final resource and the accompanying datasets and software are publicly available and can be employed for multiple different natural language processing applications. It particularly fills the gap between the small expert-built wordnets and the large amount of encyclopedic knowledge from Wikipedia. We provide a survey of previous works utilizing Wiktionary, and we exemplify the usefulness of our work in two case studies on measuring verb similarity and detecting cross-lingual marketing blunders, which make use of our Wiktionary-based resource and the results of our metalexicographic study. We conclude the thesis by emphasizing the usefulness of collaborative dictionaries when being combined with expert-built resources, which bears much unused potential

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

Reputation assessment in collaborative environments.

The popularity of open collaboration platforms is strongly related to the popularity of Internet: the growing of the latter (in technology and users) is a spring to the former. With the advent of Web 2.0, not only the Internet users became from passive receiver of published content to active producer of content, but also active reviewers and editors of content. With the increase of popularity of these platforms, some new interesting problems arise related on how to choose the best one, how to choose the collaborators and how evaluate the quality of the final work. This evolution has brought much benefit to the Internet community, especially related to the availability of free content, but also gave rise to the problem of how much this content, or these people, may be trusted. The purpose of this thesis is to present different reputation systems suitable for collaborative environments; to show that we must use very different techniques to obtain the best from the data we are dealing with and, eventually, to compare reputations systems and recommender systems and show that, under some strict circumstances, they become similar enough and we can just make minor adjustment to one to obtain the other

Reputation assessment in collaborative environments.

The popularity of open collaboration platforms is strongly related to the popularity of Internet: the growing of the latter (in technology and users) is a spring to the former. With the advent of Web 2.0, not only the Internet users became from passive receiver of published content to active producer of content, but also active reviewers and editors of content. With the increase of popularity of these platforms, some new interesting problems arise related on how to choose the best one, how to choose the collaborators and how evaluate the quality of the final work. This evolution has brought much benefit to the Internet community, especially related to the availability of free content, but also gave rise to the problem of how much this content, or these people, may be trusted. The purpose of this thesis is to present different reputation systems suitable for collaborative environments; to show that we must use very different techniques to obtain the best from the data we are dealing with and, eventually, to compare reputations systems and recommender systems and show that, under some strict circumstances, they become similar enough and we can just make minor adjustment to one to obtain the other

Reputation assessment in collaborative environments.

The popularity of open collaboration platforms is strongly related to the popularity of Internet: the growing of the latter (in technology and users) is a spring to the former. With the advent of Web 2.0, not only the Internet users became from passive receiver of published content to active producer of content, but also active reviewers and editors of content. With the increase of popularity of these platforms, some new interesting problems arise related on how to choose the best one, how to choose the collaborators and how evaluate the quality of the final work. This evolution has brought much benefit to the Internet community, especially related to the availability of free content, but also gave rise to the problem of how much this content, or these people, may be trusted. The purpose of this thesis is to present different reputation systems suitable for collaborative environments; to show that we must use very different techniques to obtain the best from the data we are dealing with and, eventually, to compare reputations systems and recommender systems and show that, under some strict circumstances, they become similar enough and we can just make minor adjustment to one to obtain the other

Understanding the topics and opinions from social media content

Social media has become one indispensable part of people’s daily life, as it records and reflects people’s opinions and events of interest, as well as influences people’s perceptions. As the most commonly employed and easily accessed data format on social media, a great deal of the social media textual content is not only factual and objective, but also rich in opinionated information. Thus, besides the topics Internet users are talking about in social media textual content, it is also of great importance to understand the opinions they are expressing. In this thesis, I present my broadly applicable text mining approaches, in order to understand the topics and opinions of user-generated texts on social media, to provide insights about the thoughts of Internet users on entities, events, etc. Specifically, I develop approaches to understand the semantic differences between language-specific editions of Wikipedia, when discussing certain entities from the related topical aspects perspective and the aggregated sentiment bias perspective. Moreover, I employ effective features to detect the reputation-influential sentences for person and company entities in Wikipedia articles, which lead to the detected sentiment bias. Furthermore, I propose neural network models with different levels of attention mechanism, to detect the stances of tweets towards any given target. I also introduce an online timeline generation approach, to detect and summarise the relevant sub-topics in the tweet stream, in order to provide Internet users with some insights about the evolution of major events they are interested in

Efficient Extraction and Query Benchmarking of Wikipedia Data

Knowledge bases are playing an increasingly important role for integrating information between systems and over the Web. Today, most knowledge bases cover only specific domains, they are created by relatively small groups of knowledge engineers, and it is very cost intensive to keep them up-to-date as domains change. In parallel, Wikipedia has grown into one of the central knowledge sources of mankind and is maintained by thousands of contributors. The DBpedia (http://dbpedia.org) project makes use of this large collaboratively edited knowledge source by extracting structured content from it, interlinking it with other knowledge bases, and making the result publicly available. DBpedia had and has a great effect on the Web of Data and became a crystallization point for it. Furthermore, many companies and researchers use DBpedia and its public services to improve their applications and research approaches. However, the DBpedia release process is heavy-weight and the releases are sometimes based on several months old data. Hence, a strategy to keep DBpedia always in synchronization with Wikipedia is highly required. In this thesis we propose the DBpedia Live framework, which reads a continuous stream of updated Wikipedia articles, and processes it. DBpedia Live processes that stream on-the-fly to obtain RDF data and updates the DBpedia knowledge base with the newly extracted data. DBpedia Live also publishes the newly added/deleted facts in files, in order to enable synchronization between our DBpedia endpoint and other DBpedia mirrors. Moreover, the new DBpedia Live framework incorporates several significant features, e.g. abstract extraction, ontology changes, and changesets publication. Basically, knowledge bases, including DBpedia, are stored in triplestores in order to facilitate accessing and querying their respective data. Furthermore, the triplestores constitute the backbone of increasingly many Data Web applications. It is thus evident that the performance of those stores is mission critical for individual projects as well as for data integration on the Data Web in general. Consequently, it is of central importance during the implementation of any of these applications to have a clear picture of the weaknesses and strengths of current triplestore implementations. We introduce a generic SPARQL benchmark creation procedure, which we apply to the DBpedia knowledge base. Previous approaches often compared relational and triplestores and, thus, settled on measuring performance against a relational database which had been converted to RDF by using SQL-like queries. In contrast to those approaches, our benchmark is based on queries that were actually issued by humans and applications against existing RDF data not resembling a relational schema. Our generic procedure for benchmark creation is based on query-log mining, clustering and SPARQL feature analysis. We argue that a pure SPARQL benchmark is more useful to compare existing triplestores and provide results for the popular triplestore implementations Virtuoso, Sesame, Apache Jena-TDB, and BigOWLIM. The subsequent comparison of our results with other benchmark results indicates that the performance of triplestores is by far less homogeneous than suggested by previous benchmarks. Further, one of the crucial tasks when creating and maintaining knowledge bases is validating their facts and maintaining the quality of their inherent data. This task include several subtasks, and in thesis we address two of those major subtasks, specifically fact validation and provenance, and data quality The subtask fact validation and provenance aim at providing sources for these facts in order to ensure correctness and traceability of the provided knowledge This subtask is often addressed by human curators in a three-step process: issuing appropriate keyword queries for the statement to check using standard search engines, retrieving potentially relevant documents and screening those documents for relevant content. The drawbacks of this process are manifold. Most importantly, it is very time-consuming as the experts have to carry out several search processes and must often read several documents. We present DeFacto (Deep Fact Validation), which is an algorithm for validating facts by finding trustworthy sources for it on the Web. DeFacto aims to provide an effective way of validating facts by supplying the user with relevant excerpts of webpages as well as useful additional information including a score for the confidence DeFacto has in the correctness of the input fact. On the other hand the subtask of data quality maintenance aims at evaluating and continuously improving the quality of data of the knowledge bases. We present a methodology for assessing the quality of knowledge bases’ data, which comprises of a manual and a semi-automatic process. The first phase includes the detection of common quality problems and their representation in a quality problem taxonomy. In the manual process, the second phase comprises of the evaluation of a large number of individual resources, according to the quality problem taxonomy via crowdsourcing. This process is accompanied by a tool wherein a user assesses an individual resource and evaluates each fact for correctness. The semi-automatic process involves the generation and verification of schema axioms. We report the results obtained by applying this methodology to DBpedia

Distributed Semantic Social Networks: Architecture, Protocols and Applications

Online social networking has become one of the most popular services on the Web. Especially Facebook with its 845Mio+ monthly active users and 100Mrd+ friendship relations creates a Web inside the Web. Drawing on the metaphor of islands, Facebook is becoming more like a continent. However, users are locked up on this continent with hardly any opportunity to communicate easily with users on other islands and continents or even to relocate trans-continentally. In addition to that, privacy, data ownership and freedom of communication issues are problematically in centralized environments. The idea of distributed social networking enables users to overcome the drawbacks of centralized social networks. The goal of this thesis is to provide an architecture for distributed social networking based on semantic technologies. This architecture consists of semantic artifacts, protocols and services which enable social network applications to work in a distributed environment and with semantic interoperability. Furthermore, this thesis presents applications for distributed semantic social networking and discusses user interfaces, architecture and communication strategies for this application category.Soziale Netzwerke gehören zu den beliebtesten Online Diensten im World Wide Web. Insbesondere Facebook mit seinen mehr als 845 Mio. aktiven Nutzern im Monat und mehr als 100 Mrd. Nutzer- Beziehungen erzeugt ein eigenständiges Web im Web. Den Nutzern dieser Sozialen Netzwerke ist es jedoch schwer möglich mit Nutzern in anderen Sozialen Netzwerken zu kommunizieren oder aber mit ihren Daten in ein anderes Netzwerk zu ziehen. Zusätzlich dazu werden u.a. Privatsphäre, Eigentumsrechte an den eigenen Daten und uneingeschränkte Freiheit in der Kommunikation als problematisch empfunden. Die Idee verteilter Soziale Netzwerke ermöglicht es, diese Probleme zentralisierter Sozialer Netzwerke zu überwinden. Das Ziel dieser Arbeit ist die Darstellung einer Architektur verteilter Soziale Netzwerke welche auf semantischen Technologien basiert. Diese Architektur besteht aus semantischen Artefakten, Protokollen und Diensten und ermöglicht die Kommunikation von Sozialen Anwendungen in einer verteilten Infrastruktur. Darüber hinaus präsentiert diese Arbeit mehrere Applikationen für verteilte semantische Soziale Netzwerke und diskutiert deren Nutzer-Schnittstellen, Architektur und Kommunikationsstrategien.