InfoSync: Information Synchronization across Multilingual Semi-structured Tables

Information Synchronization of semi-structured data across languages is challenging. For instance, Wikipedia tables in one language should be synchronized across languages. To address this problem, we introduce a new dataset InfoSyncC and a two-step method for tabular synchronization. InfoSync contains 100K entity-centric tables (Wikipedia Infoboxes) across 14 languages, of which a subset (3.5K pairs) are manually annotated. The proposed method includes 1) Information Alignment to map rows and 2) Information Update for updating missing/outdated information for aligned tables across multilingual tables. When evaluated on InfoSync, information alignment achieves an F1 score of 87.91 (en non-en). To evaluate information updation, we perform human-assisted Wikipedia edits on Infoboxes for 603 table pairs. Our approach obtains an acceptance rate of 77.28% on Wikipedia, showing the effectiveness of the proposed method.Comment: 22 pages, 7 figures, 20 tables, ACL 2023 (Toronto, Canada

Explicit diversification of event aspects for temporal summarization

During major events, such as emergencies and disasters, a large volume of information is reported on newswire and social media platforms. Temporal summarization (TS) approaches are used to automatically produce concise overviews of such events by extracting text snippets from related articles over time. Current TS approaches rely on a combination of event relevance and textual novelty for snippet selection. However, for events that span multiple days, textual novelty is often a poor criterion for selecting snippets, since many snippets are textually unique but are semantically redundant or non-informative. In this article, we propose a framework for the diversification of snippets using explicit event aspects, building on recent works in search result diversification. In particular, we first propose two techniques to identify explicit aspects that a user might want to see covered in a summary for different types of event. We then extend a state-of-the-art explicit diversification framework to maximize the coverage of these aspects when selecting summary snippets for unseen events. Through experimentation over the TREC TS 2013, 2014, and 2015 datasets, we show that explicit diversification for temporal summarization significantly outperforms classical novelty-based diversification, as the use of explicit event aspects reduces the amount of redundant and off-topic snippets returned, while also increasing summary timeliness

DocGenealogy: uma árvore genealógica de doutorados

Poster mostrado no EPCG sobre a árvore genealógica existente entre orientados e orientadores, extraído à partir da co-relação das infoboxes da Wikipedia.N/

The Currency of Wiki Articles – A Language Model-based Approach

Wikis are ubiquitous in organisational and private use and provide a wealth of textual data. Maintaining the currency of this textual data is important and difficult, requiring large manual efforts. Previous approaches from literature provide valuable contributions for assessing the currency of structured data or whole wiki articles but are unsuitable for textual wiki data like single sentences. Thus, we propose a novel approach supporting the assessment and improvement of the currency of textual wiki data in an automated manner. Grounded on a theoretical model, our approach makes use of data retrieved from recently published news articles and a language model to determine the currency of fact-based wiki sentences and suggest possible updates. Our evaluation conducted on 543 sentences from six wiki domains shows that the approach yields promising results with accuracies over 80% and thus is well-suited to support assessment and improvement of the currency of textual wiki data

Schema-agnostic entity retrieval in highly heterogeneous semi-structured environments

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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

Semantic Analysis of Wikipedia's Linked Data Graph for Entity Detection and Topic Identification Applications

Semantic Web and Linked Data community is now the reality of the future of the Web. The standards and technologies defined in this field have opened a strong pathway towards a new era of knowledge management and representation for the computing world. The data structures and the semantic formats introduced by the Semantic Web standards offer a platform for all the data and knowledge providers in the world to present their information in a free, publicly available, semantically tagged, inter-linked, and machine-readable structure. As a result, the adaptation of the Semantic Web standards by data providers creates numerous opportunities for development of new applications which were not possible or, at best, hardly achievable using the current state of Web which is mostly consisted of unstructured or semi-structured data with minimal semantic metadata attached tailored mainly for human-readability. This dissertation tries to introduce a framework for effective analysis of the Semantic Web data towards the development of solutions for a series of related applications. In order to achieve such framework, Wikipedia is chosen as the main knowledge resource largely due to the fact that it is the main and central dataset in Linked Data community. In this work, Wikipedia and its Semantic Web version DBpedia are used to create a semantic graph which constitutes the knowledgebase and the back-end foundation of the framework. The semantic graph introduced in this research consists of two main concepts: entities and topics. The entities act as the knowledge items while topics create the class hierarchy of the knowledge items. Therefore, by assigning entities to various topics, the semantic graph presents all the knowledge items in a categorized hierarchy ready for further processing. Furthermore, this dissertation introduces various analysis algorithms over entity and topic graphs which can be used in a variety of applications, especially in natural language understanding and knowledge management fields. After explaining the details of the analysis algorithms, a number of possible applications are presented and potential solutions to these applications are provided. The main themes of these applications are entity detection, topic identification, and context acquisition. To demonstrate the efficiency of the framework algorithms, some of the applications are developed and comprehensively studied by providing detailed experimental results which are compared with appropriate benchmarks. These results show how the framework can be used in different configurations and how different parameters affect the performance of the algorithms

Extending YAGO4 Knowledge Graph with Geospatial Knowledge

To YAGO είναι μία από τις μεγαλύτερες βάσεις γνώσης που διαθέτει τα δεδομένα της ως Ανοικτά Διασυνδεδεμένα Δεδομένα. Η τελευταία της έκδοση, YAGO4, συνδιάζει τους λεπτομερείς περιορισμούς με τις πλούσιες οντότητες που παρέχονται από το schema.org και το Wikidata, αντίστοιχα. Με αυτό τον τρόπο, προκύπτουν 2 δισεκατομμύρια "σταθερές" τριάδες που αντιστοιχούν σε 64 εκατομμύρια οντότητες, ενώ ταυτόχρονα δημιουργείται μια σταθερή οντολογία που επιτρέπει τη σημασιολογική συλλογιστική πορεία σύμφωνα με την λογική περιγραφή του OWL 2. Σε αυτή τη δουλειά, επεκτείνουμε αυτό τον γράφο γνώσης με ποιοτική γεωχωρική πληροφορία, η οποία παρέχεται από το σύνολο δεδομένων της Ελληνικής Διοικητικής Γεωγραφίας. Κύριος στόχος μας, είναι η επέκταση των ήδη υπάρχοντων οντοτήτων, καθώς και η δημιουργία εκείνων που λείπουν, χωρίς την εισαγωγή διπλότυπης πληροφορίας.YAGO is one of the largest knowledge bases in the Linked Open Data cloud. The latest version of YAGO, YAGO4, reconciles the rigorous typing and constraints of schema.org with the rich instance data of Wikidata. The resulting resource contains 2 billion type-consistent triples for 64 Million entities, and has a consistent ontology that allows semantic reasoning with OWL 2 description logics. In this work we present an extension of YAGO4 with qualitative geospatial information, extracted from Greek Administrative Geography (GAG) dataset. Our main goal is to extend and create preexisting and missing entities respectively, without introducing any duplication knowledge that already exists in the knowledge graph