Towards Dynamic Composition of Question Answering Pipelines

Question answering (QA) over knowledge graphs has gained significant momentum over the past five years due to the increasing availability of large knowledge graphs and the rising importance of question answering for user interaction. DBpedia has been the most prominently used knowledge graph in this setting. QA systems implement a pipeline connecting a sequence of QA components for translating an input question into its corresponding formal query (e.g. SPARQL); this query will be executed over a knowledge graph in order to produce the answer of the question. Recent empirical studies have revealed that albeit overall effective, the performance of QA systems and QA components depends heavily on the features of input questions, and not even the combination of the best performing QA systems or individual QA components retrieves complete and correct answers. Furthermore, these QA systems cannot be easily reused, extended, and results cannot be easily reproduced since the systems are mostly implemented in a monolithic fashion, lack standardised interfaces and are often not open source or available as Web services. All these drawbacks of the state of the art that prevents many of these approaches to be employed in real-world applications. In this thesis, we tackle the problem of QA over knowledge graph and propose a generic approach to promote reusability and build question answering systems in a collaborative effort. Firstly, we define qa vocabulary and Qanary methodology to develop an abstraction level on existing QA systems and components. Qanary relies on qa vocabulary to establish guidelines for semantically describing the knowledge exchange between the components of a QA system. We implement a component-based modular framework called "Qanary Ecosystem" utilising the Qanary methodology to integrate several heterogeneous QA components in a single platform. We further present Qaestro framework that provides an approach to semantically describing question answering components and effectively enumerates QA pipelines based on a QA developer requirements. Qaestro provides all valid combinations of available QA components respecting the input-output requirement of each component to build QA pipelines. Finally, we address the scalability of QA components within a framework and propose a novel approach that chooses the best component per task to automatically build QA pipeline for each input question. We implement this model within FRANKENSTEIN, a framework able to select QA components and compose pipelines. FRANKENSTEIN extends Qanary ecosystem and utilises qa vocabulary for data exchange. It has 29 independent QA components implementing five QA tasks resulting 360 unique QA pipelines. Each approach proposed in this thesis (Qanary methodology, Qaestro, and FRANKENSTEIN) is supported by extensive evaluation to demonstrate their effectiveness. Our contributions target a broader research agenda of offering the QA community an efficient way of applying their research to a research field which is driven by many different fields, consequently requiring a collaborative approach to achieve significant progress in the domain of question answering

Entities with quantities : extraction, search, and ranking

Quantities are more than numeric values. They denote measures of the world’s entities such as heights of buildings, running times of athletes, energy efficiency of car models or energy production of power plants, all expressed in numbers with associated units. Entity-centric search and question answering (QA) are well supported by modern search engines. However, they do not work well when the queries involve quantity filters, such as searching for athletes who ran 200m under 20 seconds or companies with quarterly revenue above $2 Billion. State-of-the-art systems fail to understand the quantities, including the condition (less than, above, etc.), the unit of interest (seconds, dollar, etc.), and the context of the quantity (200m race, quarterly revenue, etc.). QA systems based on structured knowledge bases (KBs) also fail as quantities are poorly covered by state-of-the-art KBs. In this dissertation, we developed new methods to advance the state-of-the-art on quantity knowledge extraction and search.Zahlen sind mehr als nur numerische Werte. Sie beschreiben Maße von Entitäten wie die Höhe von Gebäuden, die Laufzeit von Sportlern, die Energieeffizienz von Automodellen oder die Energieerzeugung von Kraftwerken - jeweils ausgedrückt durch Zahlen mit zugehörigen Einheiten. Entitätszentriete Anfragen und direktes Question-Answering werden von Suchmaschinen häufig gut unterstützt. Sie funktionieren jedoch nicht gut, wenn die Fragen Zahlenfilter beinhalten, wie z. B. die Suche nach Sportlern, die 200m unter 20 Sekunden gelaufen sind, oder nach Unternehmen mit einem Quartalsumsatz von über 2 Milliarden US-Dollar. Selbst moderne Systeme schaffen es nicht, Quantitäten, einschließlich der genannten Bedingungen (weniger als, über, etc.), der Maßeinheiten (Sekunden, Dollar, etc.) und des Kontexts (200-Meter-Rennen, Quartalsumsatz usw.), zu verstehen. Auch QA-Systeme, die auf strukturierten Wissensbanken (“Knowledge Bases”, KBs) aufgebaut sind, versagen, da quantitative Eigenschaften von modernen KBs kaum erfasst werden. In dieser Dissertation werden neue Methoden entwickelt, um den Stand der Technik zur Wissensextraktion und -suche von Quantitäten voranzutreiben. Unsere Hauptbeiträge sind die folgenden: • Zunächst präsentieren wir Qsearch [Ho et al., 2019, Ho et al., 2020] – ein System, das mit erweiterten Fragen mit Quantitätsfiltern umgehen kann, indem es Hinweise verwendet, die sowohl in der Frage als auch in den Textquellen vorhanden sind. Qsearch umfasst zwei Hauptbeiträge. Der erste Beitrag ist ein tiefes neuronales Netzwerkmodell, das für die Extraktion quantitätszentrierter Tupel aus Textquellen entwickelt wurde. Der zweite Beitrag ist ein neuartiges Query-Matching-Modell zum Finden und zur Reihung passender Tupel. • Zweitens, um beim Vorgang heterogene Tabellen einzubinden, stellen wir QuTE [Ho et al., 2021a, Ho et al., 2021b] vor – ein System zum Extrahieren von Quantitätsinformationen aus Webquellen, insbesondere Ad-hoc Webtabellen in HTML-Seiten. Der Beitrag von QuTE umfasst eine Methode zur Verknüpfung von Quantitäts- und Entitätsspalten, für die externe Textquellen genutzt werden. Zur Beantwortung von Fragen kontextualisieren wir die extrahierten Entitäts-Quantitäts-Paare mit informativen Hinweisen aus der Tabelle und stellen eine neue Methode zur Konsolidierung und verbesserteer Reihung von Antwortkandidaten durch Inter-Fakten-Konsistenz vor. • Drittens stellen wir QL [Ho et al., 2022] vor – eine Recall-orientierte Methode zur Anreicherung von Knowledge Bases (KBs) mit quantitativen Fakten. Moderne KBs wie Wikidata oder YAGO decken viele Entitäten und ihre relevanten Informationen ab, übersehen aber oft wichtige quantitative Eigenschaften. QL ist frage-gesteuert und basiert auf iterativem Lernen mit zwei Hauptbeiträgen, um die KB-Abdeckung zu verbessern. Der erste Beitrag ist eine Methode zur Expansion von Fragen, um einen größeren Pool an Faktenkandidaten zu erfassen. Der zweite Beitrag ist eine Technik zur Selbstkonsistenz durch Berücksichtigung der Werteverteilungen von Quantitäten

Atti del IX Convegno Annuale dell'Associazione per l'Informatica Umanistica e la Cultura Digitale (AIUCD). La svolta inevitabile: sfide e prospettive per l'Informatica Umanistica

Proceedings of the IX edition of the annual AIUCD conferenc

Atti del IX Convegno Annuale AIUCD. La svolta inevitabile: sfide e prospettive per l'Informatica Umanistica.

La nona edizione del convegno annuale dell'Associazione per l'Informatica Umanistica e la Cultura Digitale (AIUCD 2020; Milano, 15-17 gennaio 2020) ha come tema “La svolta inevitabile: sfide e prospettive per l'Informatica Umanistica”, con lo specifico obiettivo di fornire un'occasione per riflettere sulle conseguenze della crescente diffusione dell’approccio computazionale al trattamento dei dati connessi all’ambito umanistico. Questo volume raccoglie gli articoli i cui contenuti sono stati presentati al convegno. A diversa stregua, essi affrontano il tema proposto da un punto di vista ora più teorico-metodologico, ora più empirico-pratico, presentando i risultati di lavori e progetti (conclusi o in corso) che considerino centrale il trattamento computazionale dei dati

Tune your brown clustering, please

Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal

B!SON: A Tool for Open Access Journal Recommendation

Finding a suitable open access journal to publish scientific work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders’ conditions and the risk of Predatory Publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. It is developed based on a systematic requirements analysis, built on open data, gives publisher-independent recommendations and works across domains. It suggests open access journals based on title, abstract and references provided by the user. The recommendation quality has been evaluated using a large test set of 10,000 articles. Development by two German scientific libraries ensures the longevity of the project

The Language XChange

The research topic investigated by this thesis is reactivity on the Web. Reactivity on the Web is an emerging research issue covering: updating data on the Web, exchanging information about events (such as executed updates) between Web sites, and reacting to combinations of such events. Following a declarative approach to reactivity on the Web, a novel reactive language called XChange is proposed. Novelties of the language are represented by the proposed data metaphor intended to ease the language understanding and the supported reactive features tailored to the characteristics of the Web. Realising this pressuposed refining, extending, and adapting to a new medium some of the concepts on which active database systems are built upon. Reactivity is specified in XChange by means of reactive rules (or event-condition-action rules) having the following components: the event part is a query against events that occurred on the Web, the condition part is a query against Web resources (expressed in the Web query language Xcerpt), and the action part is a transaction specification (specifying updates to be executed and events to be raised in an all-or-nothing manner). Novel in XChange is its ability to detect composite events on the Web, i.e. possibly time related combinations of events that have occurred at (same or different) Web sites. XChange introduces a novel view over the Web data by stressing a clear separation between persistent data (data of Web resources, such as XML or HTML documents) and volatile data (event data communicated on the Web between XChange programs). Based on the differences between these kinds of data, the data metaphor is that of written text vs. speech. XChange's language design enforces this clear separation and entails new characteristics of event processing on the Web. After motivating the need for a solution to reactivity on the Web, this thesis introduces the design principles and syntax of the language XChange accompanied by use cases for demonstrating the practical applicability of its constructs. Important contributions of the thesis are the specification of the language semantics and the description of an algortihm for evaluating XChange programs