Slot Filling

Slot filling (SF) is the task of automatically extracting facts about particular entities from unstructured text, and populating a knowledge base (KB) with these facts. These structured KBs enable applications such as structured web queries and question answering. SF is typically framed as a query-oriented setting of the related task of relation extraction. Throughout this thesis, we reflect on how SF is a task with many distinct problems. We demonstrate that recall is a major limiter on SF system performance. We contribute an analysis of typical SF recall loss, and find a substantial amount of loss occurs early in the SF pipeline. We confirm that accurate NER and coreference resolution are required for high-recall SF. We measure upper bounds using a naïve graph-based semi-supervised bootstrapping technique, and find that only 39% of results are reachable using a typical feature space. We expect that this graph-based technique will be directly useful for extraction, and this leads us to frame SF as a label propagation task. We focus on a detailed graph representation of the task which reflects the behaviour and assumptions we want to model based on our analysis, including modifying the label propagation process to model multiple types of label interaction. Analysing the graph, we find that a large number of errors occur in very close proximity to training data, and identify that this is of major concern for propagation. While there are some conflicts caused by a lack of sufficient disambiguating context—we explore adding additional contextual features to address this—many of these conflicts are caused by subtle annotation problems. We find that lack of a standard for how explicit expressions of relations must be in text makes consistent annotation difficult. Using a strict definition of explicitness results in 20% of correct annotations being removed from a standard dataset. We contribute several annotation-driven analyses of this problem, exploring the definition of slots and the effect of the lack of a concrete definition of explicitness: annotation schema do not detail how explicit expressions of relations need to be, and there is large scope for disagreement between annotators. Additionally, applications may require relatively strict or relaxed evidence for extractions, but this is not considered in annotation tasks. We demonstrate that annotators frequently disagree on instances, dependent on differences in annotator world knowledge and thresholds on making probabilistic inference. SF is fundamental to enabling many knowledge-based applications, and this work motivates modelling and evaluating SF to better target these tasks

Graph Deep Learning: Methods and Applications

The past few years have seen the growing prevalence of deep neural networks on various application domains including image processing, computer vision, speech recognition, machine translation, self-driving cars, game playing, social networks, bioinformatics, and healthcare etc. Due to the broad applications and strong performance, deep learning, a subfield of machine learning and artificial intelligence, is changing everyone\u27s life.Graph learning has been another hot field among the machine learning and data mining communities, which learns knowledge from graph-structured data. Examples of graph learning range from social network analysis such as community detection and link prediction, to relational machine learning such as knowledge graph completion and recommender systems, to mutli-graph tasks such as graph classification and graph generation etc.An emerging new field, graph deep learning, aims at applying deep learning to graphs. To deal with graph-structured data, graph neural networks (GNNs) are invented in recent years which directly take graphs as input and output graph/node representations. Although GNNs have shown superior performance than traditional methods in tasks such as semi-supervised node classification, there still exist a wide range of other important graph learning problems where either GNNs\u27 applicabilities have not been explored or GNNs only have less satisfying performance.In this dissertation, we dive deeper into the field of graph deep learning. By developing new algorithms, architectures and theories, we push graph neural networks\u27 boundaries to a much wider range of graph learning problems. The problems we have explored include: 1) graph classification; 2) medical ontology embedding; 3) link prediction; 4) recommender systems; 5) graph generation; and 6) graph structure optimization.We first focus on two graph representation learning problems: graph classification and medical ontology embedding.For graph classification, we develop a novel deep GNN architecture which aggregates node features through a novel SortPooling layer that replaces the simple summing used in previous works. We demonstrate its state-of-the-art graph classification performance on benchmark datasets. For medical ontology embedding, we propose a novel hierarchical attention propagation model, which uses attention mechanism to learn embeddings of medical concepts from hierarchically-structured medical ontologies such as ICD-9 and CCS. We validate the learned embeddings on sequential procedure/diagnosis prediction tasks with real patient data.Then we investigate GNNs\u27 potential for predicting relations, specifically link prediction and recommender systems. For link prediction, we first develop a theory unifying various traditional link prediction heuristics, and then design a framework to automatically learn suitable heuristics from a given network based on GNNs. Our model shows unprecedented strong link prediction performance, significantly outperforming all traditional methods. For recommender systems, we propose a novel graph-based matrix completion model, which uses a GNN to learn graph structure features from the bipartite graph formed by user and item interactions. Our model not only outperforms various matrix completion baselines, but also demonstrates excellent transfer learning ability -- a model trained on MovieLens can be directly used to predict Douban movie ratings with high performance.Finally, we explore GNNs\u27 applicability to graph generation and graph structure optimization. We focus on a specific type of graphs which usually carry computations on them, namely directed acyclic graphs (DAGs). We develop a variational autoencoder (VAE) for DAGs and prove that it can injectively map computations into a latent space. This injectivity allows us to perform optimization in the continuous latent space instead of the original discrete structure space. We then apply our VAE to two types of DAGs, neural network architectures and Bayesian networks. Experiments show that our model not only generates novel and valid DAGs, but also finds high-quality neural architectures and Bayesian networks through performing Bayesian optimization in its latent space

Leveraging Graph Machine Learning for Social Network Analysis

As a ubiquitous complex system in quotidian life around everyone, online social networks (OSNs) provide a rich source of information about billions of users worldwide. To some extent, OSNs have mirrored our real society: people perform a multitude of different activities in OSNs as they do in the offline world, such as establishing social relations, sharing life moments, and expressing opinions about various topics. Therefore, understanding OSNs is of immense importance. One key characteristic of human social behaviour in OSNs is their inter-relational nature, which can be represented as graphs. Due to sparsity and complex structure, analysing these graphs is quite challenging and expensive. Over the past several decades, many expert-designed approaches to graphs have been proposed with elegant theoretical properties and successfully addressed numerous practical problems. Nevertheless, most of them are either not data-driven or do not benefit from the rapidly growing scale of data. Recently, in the light of remarkable achievements of artificial intelligence, especially deep neural networks techniques, graph machine learning (GML) has emerged to provide us with novel perspectives to understanding and analysing graphs. However, the current efforts of GML are relatively immature and lack attention to specific scenarios and characteristics of OSNs. Based on the pros and cons of GML, this thesis discusses several aspects of how to build advanced approaches to better simplify and ameliorate OSN analytic tasks. Specifically: 1) Overcoming flat message-passing graph neural networks. One of the most widely pursued branches in GML research, graph neural networks (GNNs), follows a similar flat message-passing principle for representation learning. Precisely, information is iteratively passed between adjacent nodes along observed edges via non-linear transformation and aggregation functions. Its effectiveness has been widely proved; however, two limitations need to be tackled: (i) they are costly in encoding long-range information spanning the graph structure; (ii) they are failing to encode features in the high-order neighbourhood in the graphs as they only perform information aggregation across the observed edges in the original graph. To fill up the gap, we propose a novel hierarchical message-passing framework to facilitate the existing GNN mechanism. Following this idea, we design two practical implementations, i.e., HC-GNN and AdamGNN, to demonstrate the framework's superiority. 2) Extending graph machine learning to heterophilous graphs. The existing GML approaches implicitly hold a homophily assumption that nodes of the same class tend to be connected. However, previous expert studies have shown the enormous importance of addressing the heterophily scenario, where ``opposites attract'', is essential for network analysis and fairness study. We demonstrate the possibility of extending GML to heterophilous graphs by simplifying supervised node classification models on heterophilous graphs (CLP) and designing an unsupervised heterophilous graph representation learning model (Selene). 3) Online social network analysis with graph machine learning. As GML approaches have demonstrated significant effectiveness over general graph analytic tasks, we perform two practical OSN analysis projects to illustrate the possibility of employing GML in practice. Specifically, we propose a semantic image graph embedding (SiGraph) to improve OSN image recognition task with the associated hashtags semantics and a simple GNN-based neural link prediction framework (NeuLP) to boost the performance with tiny change. Keywords: Graph machine learning, Social network analysis, Graph neural networks, Hierarchical structure, Homophily/Heterophily graphs, Link prediction, Online image content understanding

On the power of message passing for learning on graph-structured data

This thesis proposes novel approaches for machine learning on irregularly structured input data such as graphs, point clouds and manifolds. Specifically, we are breaking up with the regularity restriction of conventional deep learning techniques, and propose solutions in designing, implementing and scaling up deep end-to-end representation learning on graph-structured data, known as Graph Neural Networks (GNNs). GNNs capture local graph structure and feature information by following a neural message passing scheme, in which node representations are recursively updated in a trainable and purely local fashion. In this thesis, we demonstrate the generality of message passing through a unified framework suitable for a wide range of operators and learning tasks. Specifically, we analyze the limitations and inherent weaknesses of GNNs and propose efficient solutions to overcome them, both theoretically and in practice, e.g., by conditioning messages via continuous B-spline kernels, by utilizing hierarchical message passing, or by leveraging positional encodings. In addition, we ensure that our proposed methods scale naturally to large input domains. In particular, we propose novel methods to fully eliminate the exponentially increasing dependency of nodes over layers inherent to message passing GNNs. Lastly, we introduce PyTorch Geometric, a deep learning library for implementing and working with graph-based neural network building blocks, built upon PyTorch

Pseudo-contractions as Gentle Repairs

Updating a knowledge base to remove an unwanted consequence is a challenging task. Some of the original sentences must be either deleted or weakened in such a way that the sentence to be removed is no longer entailed by the resulting set. On the other hand, it is desirable that the existing knowledge be preserved as much as possible, minimising the loss of information. Several approaches to this problem can be found in the literature. In particular, when the knowledge is represented by an ontology, two different families of frameworks have been developed in the literature in the past decades with numerous ideas in common but with little interaction between the communities: applications of AGM-like Belief Change and justification-based Ontology Repair. In this paper, we investigate the relationship between pseudo-contraction operations and gentle repairs. Both aim to avoid the complete deletion of sentences when replacing them with weaker versions is enough to prevent the entailment of the unwanted formula. We show the correspondence between concepts on both sides and investigate under which conditions they are equivalent. Furthermore, we propose a unified notation for the two approaches, which might contribute to the integration of the two areas

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Proceedings of the NASA Conference on Space Telerobotics, volume 2

These proceedings contain papers presented at the NASA Conference on Space Telerobotics held in Pasadena, January 31 to February 2, 1989. The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research

Technical Debt in Software Development : Examining Premises and Overcoming Implementation for Efficient Management

Software development is a unique field of engineering: all software constructs retain their modifiability — arguably, at least — until client release, no single project stakeholder has exhaustive knowledge about the project, and even this portion of the knowledge is generally acquired only at project completion. These characteristics imply that the field of software development is subject to design decisions that are known to be sub-optimal—either deliberately emphasizing interests of particular stakeholders or indeliberately harming the project due to lack of exhaustive knowledge. Technical debt is a concept that accounts for these decisions and their effects. The concept’s intention is to capture, track, and manage the decisions and their products: the affected software constructs. Reviewing the previous, it is vital for software development projects to acknowledge technical debt both as an enabler and as a hindrance. This thesis looks into facilitating efficient technical debt management for varying software development projects. In the thesis, examination of technical debt’s role in software development produces the premises on to which a management implementation approach is introduced. The thesis begins with a revision of motivations. Basing on prior research in the fields of technical debt management and software engineering in general, the five motivations establish the premises for technical debt in software development. These include notions of subjectivity in technical debt estimation, update frequency demands posed on technical debt information, and technical debt’s polymorphism. Three research questions are derived from the motivations. They ask for tooling support for technical debt management, capturing and modelling technical debt propagation, and characterizing software development environments and their technical debt instances. The questions imply consecutive completion as the first pursued tool would benefit from—possibly automatically assessable—propagation models, and finally the tool’s introduction to software development organizations could be assisted by tailoring it based on the software development environment and the technical debt instance characterizations. The thesis has seven included publications. In introducing them, the thesis maps their backgrounds to the motivations and their outcomes to the research questions. Amongst the outcomes are the DebtFlag tool for technical debt management, the procedures for retrospectively capturing technical debt from software repositories, a procedure for technical debt propagation model creation from these retrospectives, and a multi-national survey characterizing software development environments and their technical debt instances. The thesis concludes that the tooling support, the technical debt propagation modelling, and the software environment and technical debt instance characterization describe an implementation approach to further efficient technical debt management. Simultaneously, future work is implied as all previously described efforts need to be continued and extended. Challenges also remain in the introduced approach. An example of this is the combinatorial explosion of technology-development-context-combinations that technical debt propagation modelling needs to consider. All combinations have to be managed if exhaustive modelling is desired. There is, however, a great deal of motivation to pursue these efforts when one re-notes that technical debt is a permanent component of software development that, when correctly managed, is a development efficiency mechanism comparable to a financial loan investment.Ohjelmistokehitys on uniikki tekniikan ala: kaikki ohjelmistorakenteet säilyttävät muokattavuutensa — otaksuttavasti ainakin — asiakasjulkaisuun asti. Yhdenkään projektiosakkaan tietämys ei kata koko projektia ja merkittävä osa tästäkin tiedosta karttuu vasta projektin suorittamisen aikana. Nämä ominaisuudet antavat ymmärtää, että ohjelmistokehitysala on sellaisten suunnitelupäätösten kohde, joiden tiedetään olevan epätäydellisiä—joko tarkoituksella tiettyjen projektiosakkaiden intressejä painottavia tai tahattomasti projektia vahingoittavia puutteelliseen tietoon perustuvia. Tekninen velka on konsepti, joka huomioi nämä päätökset sekä niiden vaikutukset. Konseptin tarkoitus on havaita, seurata ja hallita näitä päätöksiä sekä tuloksena syntyviä teknisen velan vaikutuksen alla olevia ohjelmistorakenteita. Edellisen kuvauksen valossa ohjelmistokehitysprojekteille on erityisen tärkeää huomioida tekninen velka sekä mahdollistajana että hidasteena. Tämän vuoksi kyseinen väitöskirja perehtyy tehokkaan teknisen velan hallinnan fasilitointiin moninaisille ohjelmistokehitysprojekteille. Väitöskirjassa tarkastellaan teknisen velan roolia osana ohjelmistokehitystä. Tarkastelu tuottaa joukon premissejä, joihin perustuen esitellään lähestymistapa teknisen velan hallinnan toteuttamiselle. Viisi väitöskirjan alussa esitettyä motivaatiota kiinnittävät ne premissit,joille ratkaisu esitetään. Motivaatiot rakennetaan olemassa olevaan teknisen velan sekä ohjelmistotekniikan tutkimustietoon perustuen. Näihin lukeutuvat muun muassa subjektiivisuus teknisen velan estimoinnissa, teknisen velan informaatiolle nähdyt päivitystaajuusvaatimukset sekä teknisen velan polymorfismi. Havainnoista johdetaan kolme tutkimuskysymystä. Ne tavoittelevat työkalutukea teknisen velan hallinnalle, velan propagoitumisen havainnointia sekä mallinnusta kuin myös ohjelmistotuotantoympäristöjen ja niiden velka instanssien kuvaamista. Tutkimuskysymykset implikoivat peräkkäistä suoritusta: tavoiteltu työkalu hyötyy—mahdollisesti automaattisesti arvoitavista—teknisen velan propagaatiomalleista. Valmiin työkalun käyttöönottoa voidaan taas edistää jos kuvaukset kehitysympäristöistä sekä niiden velkainstansseista ovat käytettävissä työkalun räätälöintiin. Väitöskirjaaan sisältyy seitsemän julkaisua. Väitöskirja esittelee ne kiinnittämällä julkaisujen taustatyön aikaisemmin mainittuihin motivaatioihin sekä niiden tulokset edellisiin tutkimuskysymyksiin. Tuloksista huomioidaan esimerkiksi DebtFlag-työkalu teknisen velan hallintaan, retrospektiivinen prosessi teknisen velan kartoittamiselle versionhallintajärjestelmistä, prosessi teknisen velan mallien rakentamiselle näistä kartoituksista ja monikansallinen kyselytutkimus ohjelmistokehitysympäristöjen sekä näiden teknisen velan instanssien luonnehtimiseksi. Väitöskirjan yhteenvetona huomioidaan, että teknisen velan hallinnan työkalutuki, teknisen velan propagaatiomallinnus ja ohjelmistokehitysympäristöjen sekä niiden teknisen velan instanssien luonnehdinta muodostavat toteutustavan, jolla teknisen velan tehokasta hallintaa voidaan kehittää. Samalla implikoidaan jatkotoimia, sillä kaikkia edellä kuvattuja työn osia tulee jatkaa ja laajentaa. Toteutustavalle nähdään myös haasteita. Eräs näistä on kombinatorinen räjähdys teknologia- ja kehityskontekstikombinaatioille. Kaikki kombinaatiot tulee huomioida mikäli teknisen velan propagaatiomallinnuksesta halutaan kattavaa. Motivaatio väitöskirjassa esitetyn työn jatkamiselle on huomattavaa ja sitä kasvattaa entuudestaan edellä tehty huomio siitä, että tekninen velka on pysyvä komponentti ohjelmistokehityksessä, joka oikein hallittuna on kehitystehokkuutta edistävänä komponenttina verrattavissa finanssialan lainainvestointiin.Siirretty Doriast

Examining the Application of Modular and Contextualised Ontology in Query Expansions for Information Retrieval

This research considers the ongoing challenge of semantics-based search from the perspective of how to exploit Semantic Web languages for search in the current Web environment. The purpose of the PhD was to use ontology-based query expansion (OQE) to improve search effectiveness by increasing search precision, i.e. retrieving relevant documents in the topmost ranked positions in a returned document list. Query experiments have required a novel search tool that can combine Semantic Web technologies in an otherwise traditional IR process using a Web document collection

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications