Facilitating Information Access for Heterogeneous Data Across Many Languages

Information access, which enables people to identify, retrieve, and use information freely and effectively, has attracted interest from academia and industry. Systems for document retrieval and question answering have helped people access information in powerful and useful ways. Recently, natural language technologies based on neural network have been applied to various tasks for information access. Specifically, transformer-based pre-trained models have pushed tasks such as document and passage retrieval to new state-of-the-art effectiveness. (1) Most of the research has focused on helping people access passages and documents on the web. However, there is abundant information stored in other formats such as semi-structured tables and domain-specific relational databases in companies. Development of the models and frameworks that support access information from these data formats is also essential. (2) Moreover, most of the advances in information access research are based on English, leaving other languages less explored. It is insufficient and inequitable in our globalized and connected world to serve only speakers of English. In this thesis, we explore and develop models and frameworks that could alleviate the aforementioned challenges. This dissertation consists of three parts. We begin with a discussion on developing models designed for accessing data in formats other than passages and documents. We mainly focus on two data formats, namely semi-structured tables and relational databases. In the second part, we discuss methods that can enhance the user experience for non-English speakers when using information access systems. Specifically, we first introduce model development for multilingual knowledge graph integration, which can benefit many information access applications such as cross-lingual question answering systems and other knowledge-driven cross-lingual NLP applications. We further focus on multilingual document dense retrieval and reranking that boost the effectiveness of search engines for non-English information access. Last but not least, we take a step further based on the aforementioned two parts by investigating models and frameworks that can facilitate non-English speakers to access structured data. In detail, we present cross-lingual Text-to-SQL semantic parsing systems that enable non-English speakers to query relational databases with queries in their languages

KNIT: Ontology reusability through knowledge graph exploration

Ontologies have become a standard for knowledge representation across several domains. In Life Sciences, numerous ontologies have been introduced to represent human knowledge, often providing overlapping or conflicting perspectives. These ontologies are usually published as OWL or OBO, and are often registered in open repositories, e.g., BioPortal. However, the task of finding the concepts (classes and their properties) defined in the existing ontologies and the relationships between these concepts across different ontologies – for example, for developing a new ontology aligned with the existing ones – requires a great deal of manual effort in searching through the public repositories for candidate ontologies and their entities. In this work, we develop a new tool, KNIT, to automatically explore open repositories to help users fetch the previously designed concepts using keywords. User-specified keywords are then used to retrieve matching names of classes or properties. KNIT then creates a draft knowledge graph populated with the concepts and relationships retrieved from the existing ontologies. Furthermore, following the process of ontology learning, our tool refines this first draft of an ontology. We present three BioPortal-specific use cases for our tool. These use cases outline the development of new knowledge graphs and ontologies in the sub-domains of biology: genes and diseases, virome and drugs.This work has been funded by grant PID2020-112540RB-C4121, AETHER-UMA (A smart data holistic approach for context-aware data analytics: semantics and context exploitation). Funding for open access charge: Universidad de Málaga / CBUA

Factoid question answering for spoken documents

In this dissertation, we present a factoid question answering system, specifically tailored for Question Answering (QA) on spoken documents. This work explores, for the first time, which techniques can be robustly adapted from the usual QA on written documents to the more difficult spoken documents scenario. More specifically, we study new information retrieval (IR) techniques designed for speech, and utilize several levels of linguistic information for the speech-based QA task. These include named-entity detection with phonetic information, syntactic parsing applied to speech transcripts, and the use of coreference resolution. Our approach is largely based on supervised machine learning techniques, with special focus on the answer extraction step, and makes little use of handcrafted knowledge. Consequently, it should be easily adaptable to other domains and languages. In the work resulting of this Thesis, we have impulsed and coordinated the creation of an evaluation framework for the task of QA on spoken documents. The framework, named QAst, provides multi-lingual corpora, evaluation questions, and answers key. These corpora have been used in the QAst evaluation that was held in the CLEF workshop for the years 2007, 2008 and 2009, thus helping the developing of state-of-the-art techniques for this particular topic. The presentend QA system and all its modules are extensively evaluated on the European Parliament Plenary Sessions English corpus composed of manual transcripts and automatic transcripts obtained by three different Automatic Speech Recognition (ASR) systems that exhibit significantly different word error rates. This data belongs to the CLEF 2009 track for QA on speech transcripts. The main results confirm that syntactic information is very useful for learning to rank question candidates, improving results on both manual and automatic transcripts unless the ASR quality is very low. Overall, the performance of our system is comparable or better than the state-of-the-art on this corpus, confirming the validity of our approach.En aquesta Tesi, presentem un sistema de Question Answering (QA) factual, especialment ajustat per treballar amb documents orals. En el desenvolupament explorem, per primera vegada, quines tècniques de les habitualment emprades en QA per documents escrit són suficientment robustes per funcionar en l'escenari més difícil de documents orals. Amb més especificitat, estudiem nous mètodes de Information Retrieval (IR) dissenyats per tractar amb la veu, i utilitzem diversos nivells d'informació linqüística. Entre aquests s'inclouen, a saber: detecció de Named Entities utilitzant informació fonètica, "parsing" sintàctic aplicat a transcripcions de veu, i també l'ús d'un sub-sistema de detecció i resolució de la correferència. La nostra aproximació al problema es recolza en gran part en tècniques supervisades de Machine Learning, estant aquestes enfocades especialment cap a la part d'extracció de la resposta, i fa servir la menor quantitat possible de coneixement creat per humans. En conseqüència, tot el procés de QA pot ser adaptat a altres dominis o altres llengües amb relativa facilitat. Un dels resultats addicionals de la feina darrere d'aquesta Tesis ha estat que hem impulsat i coordinat la creació d'un marc d'avaluació de la taska de QA en documents orals. Aquest marc de treball, anomenat QAst (Question Answering on Speech Transcripts), proporciona un corpus de documents orals multi-lingüe, uns conjunts de preguntes d'avaluació, i les respostes correctes d'aquestes. Aquestes dades han estat utilitzades en les evaluacionis QAst que han tingut lloc en el si de les conferències CLEF en els anys 2007, 2008 i 2009; d'aquesta manera s'ha promogut i ajudat a la creació d'un estat-de-l'art de tècniques adreçades a aquest problema en particular. El sistema de QA que presentem i tots els seus particulars sumbòduls, han estat avaluats extensivament utilitzant el corpus EPPS (transcripcions de les Sessions Plenaries del Parlament Europeu) en anglès, que cónté transcripcions manuals de tots els discursos i també transcripcions automàtiques obtingudes mitjançant tres reconeixedors automàtics de la parla (ASR) diferents. Els reconeixedors tenen característiques i resultats diferents que permetes una avaluació quantitativa i qualitativa de la tasca. Aquestes dades pertanyen a l'avaluació QAst del 2009. Els resultats principals de la nostra feina confirmen que la informació sintàctica és mol útil per aprendre automàticament a valorar la plausibilitat de les respostes candidates, millorant els resultats previs tan en transcripcions manuals com transcripcions automàtiques, descomptat que la qualitat de l'ASR sigui molt baixa. En general, el rendiment del nostre sistema és comparable o millor que els altres sistemes pertanyents a l'estat-del'art, confirmant així la validesa de la nostra aproximació

Interpreting and Answering Keyword Queries using Web Knowledge Bases

Many keyword queries issued to Web search engines target information about real world entities, and interpreting these queries over Web knowledge bases can allow a search system to provide exact answers to keyword queries. Such an ability provides a useful service to end users, as their information need can be directly addressed and they need not scour textual results for the desired information. However, not all keyword queries can be addressed by even the most comprehensive knowledge base, and therefore equally important is the problem of recognizing when a reference knowledge base is not capable of modelling the keyword query's intention. This may be due to lack of coverage of the knowledge base or lack of expressiveness in the underlying query representation formalism. This thesis presents an approach to computing structured representations of keyword queries over a reference knowledge base. Keyword queries are annotated with occurrences of semantic constructs by learning a sequential labelling model from an annotated Web query log. Frequent query structures are then mined from the query log and are used along with the annotations to map keyword queries into a structured representation over the vocabulary of a reference knowledge base. The proposed approach exploits coarse linguistic structure in keyword queries, and combines it with rich structured query representations of information needs. As an intermediate representation formalism, a novel query language is proposed that blends keyword search with structured query processing over large Web knowledge bases. The formalism for structured keyword queries combines the flexibility of keyword search with the expressiveness of structures queries. A solution to the resulting disambiguation problem caused by introducing keywords as primitives in a structured query language is presented. Expressions in our proposed language are rewritten using the vocabulary of the knowledge base, and different possible rewritings are ranked based on their syntactic relationship to the keywords in the query as well as their semantic coherence in the underlying knowledge base. The problem of ranking knowledge base entities returned as a query result is also explored from the perspective of personalized result ranking. User interest models based on entity types are learned from a Web search session by cross referencing clicks on URLs with known entity homepages. The user interest model is then used to effectively rerank answer lists for a given user. A methodology for evaluating entity-based search engines is also proposed and empirically evaluated

Knowledge Reasoning with Graph Neural Networks

Knowledge reasoning is the process of drawing conclusions from existing facts and rules, which requires a range of capabilities including but not limited to understanding concepts, applying logic, and calibrating or validating architecture based on existing knowledge. With the explosive growth of communication techniques and mobile devices, much of collective human knowledge resides on the Internet today, in unstructured and semi-structured forms such as text, tables, images, videos, etc. It is overwhelmingly difficult for human to navigate the gigantic Internet knowledge without the help of intelligent systems such as search engines and question answering systems. To serve various information needs, in this thesis, we develop methods to perform knowledge reasoning over both structured and unstructured data. This thesis attempts to answer the following research questions on the topic of knowledge reasoning: (1) How to perform multi-hop reasoning over knowledge graphs? How should we leverage graph neural networks to learn graph-aware representations efficiently? And, how to systematically handle the noise in human questions? (2) How to combine deep learning and symbolic reasoning in a consistent probabilistic framework? How to make the inference efficient and scalable for large-scale knowledge graphs? Can we strike a balance between the representation power and the simplicity of the model? (3) What is the reasoning pattern of graph neural networks for knowledge-aware QA tasks? Can those elaborately designed GNN modules really perform complex reasoning process? Are they under- or over-complicated? Can we design a much simpler yet effective model to achieve comparable performance? (4) How to build an open-domain question answering system that can reason over multiple retrieved documents? How to efficiently rank and filter the retrieved documents to reduce the noise for the downstream answer prediction module? How to propagate and assemble the information among multiple retrieved documents? (5) How to answer the questions that require numerical reasoning over textual passages? How to enable pre-trained language models to perform numerical reasoning? We explored the research questions above and discovered that graph neural networks can be leveraged as a powerful tool for various knowledge reasoning tasks over both structured and unstructured knowledge sources. On structured graph-based knowledge source, we build graph neural networks on top of the graph structure to capture the topology information for downstream reasoning tasks. On unstructured text-based knowledge source, we first identify graph-structured information such as entity co-occurrence and entity-number binding, and then employ graph neural networks to reason over the constructed graphs, working together with pre-trained language models to handle unstructured part of the knowledge source.Ph.D