Parameter Learning of Logic Programs for Symbolic-Statistical Modeling

We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. definite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distribution semantics, possible world semantics with a probability distribution which is unconditionally applicable to arbitrary logic programs including ones for HMMs, PCFGs and Bayesian networks. We also propose a new EM algorithm, the graphical EM algorithm, that runs for a class of parameterized logic programs representing sequential decision processes where each decision is exclusive and independent. It runs on a new data structure called support graphs describing the logical relationship between observations and their explanations, and learns parameters by computing inside and outside probability generalized for logic programs. The complexity analysis shows that when combined with OLDT search for all explanations for observations, the graphical EM algorithm, despite its generality, has the same time complexity as existing EM algorithms, i.e. the Baum-Welch algorithm for HMMs, the Inside-Outside algorithm for PCFGs, and the one for singly connected Bayesian networks that have been developed independently in each research field. Learning experiments with PCFGs using two corpora of moderate size indicate that the graphical EM algorithm can significantly outperform the Inside-Outside algorithm

Integrating prior knowledge into factorization approaches for relational learning

An efficient way to represent the domain knowledge is relational data, where information is recorded in form of relationships between entities. Relational data is becoming ubiquitous over the years for knowledge representation due to the fact that many real-word data is inherently interlinked. Some well-known examples of relational data are: the World Wide Web (WWW), a system of interlinked hypertext documents; the Linked Open Data (LOD) cloud of the Semantic Web, a collection of published data and their interlinks; and finally the Internet of Things (IoT), a network of physical objects with internal states and communications ability. Relational data has been addressed by many different machine learning approaches, the most promising ones are in the area of relational learning, which is the focus of this thesis. While conventional machine learning algorithms consider entities as being independent instances randomly sampled from some statistical distribution and being represented as data points in a vector space, relational learning takes into account the overall network environment when predicting the label of an entity, an attribute value of an entity or the existence of a relationship between entities. An important feature is that relational learning can exploit contextual information that is more distant in the relational network. As the volume and structural complexity of the relational data increase constantly in the era of Big Data, scalability and the modeling power become crucial for relational learning algorithms. Previous relational learning algorithms either provide an intuitive representation of the model, such as Inductive Logic Programming (ILP) and Markov Logic Networks (MLNs), or assume a set of latent variables to explain the observed data, such as the Infinite Hidden Relational Model (IHRM), the Infinite Relational Model (IRM) and factorization approaches. Models with intuitive representations often involve some form of structure learning which leads to scalability problems due to a typically large search space. Factorizations are among the best-performing approaches for large-scale relational learning since the algebraic computations can easily be parallelized and since they can exploit data sparsity. Previous factorization approaches exploit only patterns in the relational data itself and the focus of the thesis is to investigate how additional prior information (comprehensive information), either in form of unstructured data (e.g., texts) or structured patterns (e.g., in form of rules) can be considered in the factorization approaches. The goal is to enhance the predictive power of factorization approaches by involving prior knowledge for the learning, and on the other hand to reduce the model complexity for efficient learning. This thesis contains two main contributions: The first contribution presents a general and novel framework for predicting relationships in multirelational data using a set of matrices describing the various instantiated relations in the network. The instantiated relations, derived or learnt from prior knowledge, are integrated as entities' attributes or entity-pairs' attributes into different adjacency matrices for the learning. All the information available is then combined in an additive way. Efficient learning is achieved using an alternating least squares approach exploiting sparse matrix algebra and low-rank approximation. As an illustration, several algorithms are proposed to include information extraction, deductive reasoning and contextual information in matrix factorizations for the Semantic Web scenario and for recommendation systems. Experiments on various data sets are conducted for each proposed algorithm to show the improvement in predictive power by combining matrix factorizations with prior knowledge in a modular way. In contrast to a matrix, a 3-way tensor si a more natural representation for the multirelational data where entities are connected by different types of relations. A 3-way tensor is a three dimensional array which represents the multirelational data by using the first two dimensions for entities and using the third dimension for different types of relations. In the thesis, an analysis on the computational complexity of tensor models shows that the decomposition rank is key for the success of an efficient tensor decomposition algorithm, and that the factorization rank can be reduced by including observable patterns. Based on these theoretical considerations, a second contribution of this thesis develops a novel tensor decomposition approach - an Additive Relational Effects (ARE) model - which combines the strengths of factorization approaches and prior knowledge in an additive way to discover different relational effects from the relational data. As a result, ARE consists of a decomposition part which derives the strong relational leaning effects from a highly scalable tensor decomposition approach RESCAL and a Tucker 1 tensor which integrates the prior knowledge as instantiated relations. An efficient least squares approach is proposed to compute the combined model ARE. The additive model contains weights that reflect the degree of reliability of the prior knowledge, as evaluated by the data. Experiments on several benchmark data sets show that the inclusion of prior knowledge can lead to better performing models at a low tensor rank, with significant benefits for run-time and storage requirements. In particular, the results show that ARE outperforms state-of-the-art relational learning algorithms including intuitive models such as MRC, which is an approach based on Markov Logic with structure learning, factorization approaches such as Tucker, CP, Bayesian Clustered Tensor Factorization (BCTF), the Latent Factor Model (LFM), RESCAL, and other latent models such as the IRM. A final experiment on a Cora data set for paper topic classification shows the improvement of ARE over RESCAL in both predictive power and runtime performance, since ARE requires a significantly lower rank

Integrating prior knowledge into factorization approaches for relational learning

An efficient way to represent the domain knowledge is relational data, where information is recorded in form of relationships between entities. Relational data is becoming ubiquitous over the years for knowledge representation due to the fact that many real-word data is inherently interlinked. Some well-known examples of relational data are: the World Wide Web (WWW), a system of interlinked hypertext documents; the Linked Open Data (LOD) cloud of the Semantic Web, a collection of published data and their interlinks; and finally the Internet of Things (IoT), a network of physical objects with internal states and communications ability. Relational data has been addressed by many different machine learning approaches, the most promising ones are in the area of relational learning, which is the focus of this thesis. While conventional machine learning algorithms consider entities as being independent instances randomly sampled from some statistical distribution and being represented as data points in a vector space, relational learning takes into account the overall network environment when predicting the label of an entity, an attribute value of an entity or the existence of a relationship between entities. An important feature is that relational learning can exploit contextual information that is more distant in the relational network. As the volume and structural complexity of the relational data increase constantly in the era of Big Data, scalability and the modeling power become crucial for relational learning algorithms. Previous relational learning algorithms either provide an intuitive representation of the model, such as Inductive Logic Programming (ILP) and Markov Logic Networks (MLNs), or assume a set of latent variables to explain the observed data, such as the Infinite Hidden Relational Model (IHRM), the Infinite Relational Model (IRM) and factorization approaches. Models with intuitive representations often involve some form of structure learning which leads to scalability problems due to a typically large search space. Factorizations are among the best-performing approaches for large-scale relational learning since the algebraic computations can easily be parallelized and since they can exploit data sparsity. Previous factorization approaches exploit only patterns in the relational data itself and the focus of the thesis is to investigate how additional prior information (comprehensive information), either in form of unstructured data (e.g., texts) or structured patterns (e.g., in form of rules) can be considered in the factorization approaches. The goal is to enhance the predictive power of factorization approaches by involving prior knowledge for the learning, and on the other hand to reduce the model complexity for efficient learning. This thesis contains two main contributions: The first contribution presents a general and novel framework for predicting relationships in multirelational data using a set of matrices describing the various instantiated relations in the network. The instantiated relations, derived or learnt from prior knowledge, are integrated as entities' attributes or entity-pairs' attributes into different adjacency matrices for the learning. All the information available is then combined in an additive way. Efficient learning is achieved using an alternating least squares approach exploiting sparse matrix algebra and low-rank approximation. As an illustration, several algorithms are proposed to include information extraction, deductive reasoning and contextual information in matrix factorizations for the Semantic Web scenario and for recommendation systems. Experiments on various data sets are conducted for each proposed algorithm to show the improvement in predictive power by combining matrix factorizations with prior knowledge in a modular way. In contrast to a matrix, a 3-way tensor si a more natural representation for the multirelational data where entities are connected by different types of relations. A 3-way tensor is a three dimensional array which represents the multirelational data by using the first two dimensions for entities and using the third dimension for different types of relations. In the thesis, an analysis on the computational complexity of tensor models shows that the decomposition rank is key for the success of an efficient tensor decomposition algorithm, and that the factorization rank can be reduced by including observable patterns. Based on these theoretical considerations, a second contribution of this thesis develops a novel tensor decomposition approach - an Additive Relational Effects (ARE) model - which combines the strengths of factorization approaches and prior knowledge in an additive way to discover different relational effects from the relational data. As a result, ARE consists of a decomposition part which derives the strong relational leaning effects from a highly scalable tensor decomposition approach RESCAL and a Tucker 1 tensor which integrates the prior knowledge as instantiated relations. An efficient least squares approach is proposed to compute the combined model ARE. The additive model contains weights that reflect the degree of reliability of the prior knowledge, as evaluated by the data. Experiments on several benchmark data sets show that the inclusion of prior knowledge can lead to better performing models at a low tensor rank, with significant benefits for run-time and storage requirements. In particular, the results show that ARE outperforms state-of-the-art relational learning algorithms including intuitive models such as MRC, which is an approach based on Markov Logic with structure learning, factorization approaches such as Tucker, CP, Bayesian Clustered Tensor Factorization (BCTF), the Latent Factor Model (LFM), RESCAL, and other latent models such as the IRM. A final experiment on a Cora data set for paper topic classification shows the improvement of ARE over RESCAL in both predictive power and runtime performance, since ARE requires a significantly lower rank

Thirsting for recognition : a comparative ethnographic case study of water governance and security in the highlands of Kalinga, Philippines

Basé sur une recherche ethnographique de neuf mois, ce mémoire présente une étude comparative des incertitudes, risques et vulnérabilités vécues à l’ère d’instabilités environnementales et climatiques aux Philippines, ainsi qu’aux enjeux contemporains liés à la sécurité ainsi qu’à la gouvernance de l’eau dans les hautes terres de Kalinga, une province située dans la région administrative de la Cordillère au nord de l’île de Luçon. Divulguant, pour ce faire, les fondements et les opérations du gouvernement coutumier de l’eau d’irrigation, cette étude souligne les fondements intrinsèquement politiques de la disponibilité et de l’accessibilité de l’eau comme ressource, ainsi que pour la protection des droits autochtones et le développement des ressources naturelles. Mots-clés : gouvernance et sécurité de l’eau, modalités et processus de gestion coutumière de l’eau d’irrigation, droits et savoirs autochtones, Kalinga, Philippines.Based on a nine-month ethnographic research conducted in 2015 and 2016 amongst three indigenous communities of the Kalinga highlands, a province and ancestral domain located in the Cordillera Administrative Region of Northern Luzon (Philippines), this comparative academic study examines the local experiences and responses to contemporary threats to safe and sufficient supplies of irrigation water. It further provides a detailed account of the constitution and functions of prevailing customary water governance systems and practices. This study, thus, defends the need to correlate water security to governance, whilst insisting upon the importance of articulating preventive and responsive policies and interventions with local contexts and conditions. Keywords : water governance, water security, customary water governance systems and practices, indigenous knowledge, Kalinga, Philippines

Does faith develop universally through stable and hierarchical stages?

Faith Development Theories are concerned with the way faith, as a way of knowing, develops universally through stable and hierarchical stages. This thesis questions the assumptions underlying these theories. The spiritual tradition of Western Christianity is examined for evidence of stable stages. Piaget's stage theory is reviewed critically. This is followed by a critical look at language development studies. The assumption of development as a naturally occurring phenomenon is questioned. Some thoughts are gathered about self and mind. Finally, these chapters are woven together to propose that faith may indeed change but that these changes are not universal, stable and hierarchical. A theory to explain faith change is developed throughout the thesis

Homogeneity and heterogeneity in disciplinary discourse: tracking the management of intertextuality in undergraduate academic lectures

Using a corpus of twenty-four lectures drawn from The BASE corpus*, this study is an analysis and inter-disciplinary comparison of the management of Intertextuality in the genre of the undergraduate lecture. Theorising Intertextuality as central within the discursive (re-)construction of disciplinary knowledge, the investigation of Intertextuality is viewed as the investigation of the discursively-mediated interaction(s) of a current lecturer with original knowledge-constituting discourses, and with their agents too, of an academic community. As there is no holistic and comprehensive methodology for assessing the management of Intertextuality in academic discourse both qualitatively and quantitatively, this study uses two further lectures to devise such a methodology. This involves segregating lecture discourse into consistent independent units and then coding each unit according both to its function in the discourse and the participant voice(s) behind it. Applying this comprehensive scheme shows that independent units in lecture discourse are classifiable under three broad functional areas, Intertextuality (units realising propositional input), Intratextuality (units realising the mechanics of text and discursive interaction), and Metatextuality (units realising unit-length evaluation of emerging discourse). These functional areas and the functions within them are manageable via different participant voice(s), the manifestations and pragmatic effects of which in discourse vary, meaning the management of Intertextuality can be assessed qualitatively and quantitatively using the coherent, consistent and data-driven coding scheme derived from these analyses. This methodology, applied qualitatively and quantitatively to the corpus, reveals management similarities broadly between Arts & Humanities and Social Sciences lectures, typically a dialogic management, and management differences broadly between these two groupings and Physical Sciences lectures, typically a monophonic management. These management choices are understood as both constituted by and as reconstitutive of the social and epistemological landscapes behind lectures, meaning the management of Intertextuality is viewed as the dominant influence in shaping disciplinary discourse. * The BASE (British Academic Spoken English) corpus is a corpus of authentic academic speech events currently being developed at the universities of Warwick and Reading in The UK with funding from the Arts and Humanities Research Board

Homogeneity and heterogeneity in disciplinary discourse : tracking the management of intertextuality in undergraduate academic lectures

Using a corpus of twenty-four lectures drawn from The BASE corpus, this study is an analysis and inter-disciplinary comparison of the management of Intertextuality in the genre of the undergraduate lecture. Theorising Intertextuality as central within the discursive (re-)construction of disciplinary knowledge, the investigation of Intertextuality is viewed as the investigation of the discursively-mediated interaction(s) of a current lecturer with original knowledge-constituting discourses, and with their agents too, of an academic community. As there is no holistic and comprehensive methodology for assessing the management of Intertextuality in academic discourse both qualitatively and quantitatively, this study uses two further lectures to devise such a methodology. This involves segregating lecture discourse into consistent independent units and then coding each unit according both to its function in the discourse and the participant voice(s) behind it. Applying this comprehensive scheme shows that independent units in lecture discourse are classifiable under three broad functional areas, Intertextuality (units realising propositional input), Intratextuality (units realising the mechanics of text and discursive interaction), and Metatextuality (units realising unit-length evaluation of emerging discourse). These functional areas and the functions within them are manageable via different participant voice(s), the manifestations and pragmatic effects of which in discourse vary, meaning the management of Intertextuality can be assessed qualitatively and quantitatively using the coherent, consistent and data-driven coding scheme derived from these analyses. This methodology, applied qualitatively and quantitatively to the corpus, reveals management similarities broadly between Arts & Humanities and Social Sciences lectures, typically a dialogic management, and management differences broadly between these two groupings and Physical Sciences lectures, typically a monophonic management. These management choices are understood as both constituted by and as reconstitutive of the social and epistemological landscapes behind lectures, meaning the management of Intertextuality is viewed as the dominant influence in shaping disciplinary discourse

The Falcon 2016-2017

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