1,264 research outputs found
Importance sampling on relational Bayesian networks
No full textWe present techniques for importance sampling from distributions defined representation language, and therefore can be applied in situations where sampling from a standard Bayesian Network representation is infeasible. We describe experimental results from using standard, adaptive and backward sampling strategies. Furthermore, we use in our experiments a model that illustrates a fully general way of translating the recent framework of Markov Logic Networks into Relational Bayesian Networks
Learning and Reasoning with Graph Data: Neural and Statistical-Relational Approaches (Invited Paper)
No full textGraph neural networks (GNNs) have emerged in recent years as a very powerful and popular modeling tool for graph and network data. Though much of the work on GNNs has focused on graphs with a single edge relation, they have also been adapted to multi-relational graphs, including knowledge graphs. In such multi-relational domains, the objectives and possible applications of GNNs become quite similar to what for many years has been investigated and developed in the field of statistical relational learning (SRL). This article first gives a brief overview of the main features of GNN and SRL approaches to learning and reasoning with graph data. It analyzes then in more detail their commonalities and differences with respect to semantics, representation, parameterization, interpretability, and flexibility. A particular focus will be on relational Bayesian networks (RBNs) as the SRL framework that is most closely related to GNNs. We show how common GNN architectures can be directly encoded as RBNs, thus enabling the direct integration of "low level" neural model components with the "high level" symbolic representation and flexible inference capabilities of SRL
Relational Information Gain
No full textWe introduce relational information gain, a refinement scoring function measuring the informativeness of newly introduced variables. The gain can be interpreted as a conditional entropy in a well-defined sense and can be efficiently approximately computed. In conjunction with simple greedy general-to-specific search algorithms such as FOIL, it yields an efficient and competitive algorithm in terms of predictive accuracy and compactness of the learned theory. In conjunction with the decision tree learner TILDE, it offers a beneficial alternative to lookahead, achieving similar performance while significantly reducing the number of evaluated literal
Type Extension Trees for Feature Construction and Learning in Relational Domains
No full textType Extension Trees are a powerful representation language for "count-of-count" features characterizing the combinatorial structure of neighborhoods of entities in relational domains. In this paper we present a learning algorithm for Type Extension Trees (TET) that discovers informative count-of-count features in the supervised learning setting. Experiments on bibliographic data show that TET-learning is able to discover the count-of-count feature underlying the definition of the h-index, and the inverse document frequency feature commonly used in information retrieval. We also introduce a metric on TET feature values. This metric is defined as a recursive application of the Wasserstein-Kantorovich metric. Experiments with a k-NN classifier show that exploiting the recursive count-of-count statistics encoded in TET values improves classification accuracy over alternative methods based on simple count statistics. © 2013 Elsevier B.V
Counts-of-counts similarity for prediction and search in relational data
No full textDefining appropriate distance functions is a crucial aspect of effective and efficient similarity-based prediction and retrieval. Relational data are especially challenging in this regard. By viewing relational data as multi-relational graphs, one can easily see that a distance between a pair of nodes can be defined in terms of a virtually unlimited class of features, including node attributes, attributes of node neighbors, structural aspects of the node neighborhood and arbitrary combinations of these properties. In this paper we propose a rich and flexible class of metrics on graph entities based on earth mover’s distance applied to a hierarchy of complex counts-of-counts statistics. We further propose an approximate version of the distance using sums of marginal earth mover’s distances. We show that the approximation is correct for many cases of practical interest and allows efficient nearest-neighbor retrieval when combined with a simple metric tree data structure. An experimental evaluation on two real-world scenarios highlights the flexibility of our framework for designing metrics representing different notions of similarity. Substantial improvements in similarity-based prediction are reported when compared to solutions based on state-of-the-art graph kernels
Lower complexity bounds for lifted inference
No full textOne of the big challenges in the development of probabilistic relational (or probabilistic logical) modeling and learning frameworks is the design of inference techniques that operate on the level of the abstract model representation language, rather than on the level of ground, propositional instances of the model. Numerous approaches for such “lifted inference” techniques have been proposed. While it has been demonstrated that these techniques will lead to significantly more efficient inference on some specific models, there are only very recent and still quite restricted results that show the feasibility of lifted inference on certain syntactically defined classes of models. Lower complexity bounds that imply some limitations for the feasibility of lifted inference on more expressive model classes were established earlier in Jaeger (2000; Jaeger, M. 2000. On the complexity of inference about probabilistic relational models. Artificial Intelligence 117, 297–308). However, it is not immediate that these results also apply to the type of modeling languages that currently receive the most attention, i.e., weighted, quantifier-free formulas. In this paper we extend these earlier results, and show that under the assumption that NETIME≠ETIME, there is no polynomial lifted inference algorithm for knowledge bases of weighted, quantifier-, and function-free formulas. Further strengthening earlier results, this is also shown to hold for approximate inference and for knowledge bases not containing the equality predicate
Constraints as data: A new perspective on inferring probabilities
No full textWe present a new approach to inferring a probability distribution which is incompletely specified by a number of linear constraints. We argue that the currently most popular approach of entropy maximization depends on a “constraints as knowledge” interpretation of the constraints, and that a different “constraints as data ” perspective leads to a completely different type of inference procedures by statistical methods. With statistical methods some of the counterintuitive results of entropy maximization can be avoided, and inconsistent sets of constraints can be handled just like consistent ones. A particular statistical inference method is developed and shown to have a nice robustness property.
Love in the First Degree: Manfred, Byron, and Incest
No full textThis is the author accepted manuscript. The final version is freely available from the University of Colorado via the link in this recordNote that the text of the manuscript varies considerably from the final published versionThis essay suggests that Byron’s Manfred contains not an expression of Byron’s guilt about his incest with his half-sister Augusta Leigh, as previous critics have suggested, but rather considerable evidence of his lack of guilt. It argues that the play displays incest and torment, but in fact does not link the two, instead displaying Manfred’s love for Astarte as deeply felt without regrets. The essay then argues that one finds the same combination of deep love and lack of regret in Byron’s remarks about his relationship with his half-sister, as well as in the representations of incest in his other works. It suggests that this acceptance of incest links to Byron’s commitment to rational thinking and personal freedom, and it invites future criticism to explore this connection in more detail
Manfred Macmillan
No full textDecadence meets gothic in Manfred Macmillan (1907), a carefully constructed tale of doppelgangers, magical intrigue, and the rootless scion of a noble house. This annotated, first-ever English translation presents an early queer novel long unavailable except in the original Czech. Author Jiří Karásek ze Lvovic (1871–1951) was a major cultural figure in his native Bohemia and cultivated ties with fellow artists from across Central Europe. In their extensive scholarly introduction, translator Carleton Bulkin and translation scholar Brian James Baer situate the novel within longer histories of gay literature, fascinations with the occult, and the cultural and linguistic politics of so-called peripheral European nations. They persuasively frame Karásek as a queer author and cultural disruptor in the fin de siècle Habsburg space.
Karasék rejected Czech translations of ancient Greek writers that bowdlerized gay themes, and he personally and vigorously defended Oscar Wilde in print, both on the grounds of artistic freedom and of private morality. He also published a cycle of homoerotic poems under the title Sodom, confiscated by the Austrian authorities but republished in 1905 and repeatedly afterward. A colonized subject, a literary decadent, and a sexual outlaw, Karasék’s complex responses to his own marginalization can be traced through his fantastically strange novel trilogy Three Magicians. As the first volume in that series, Manfred Macmillan is a gorgeous, compelling, and important addition to expanding canons of LGBTQI+ literature
Manfred Macmillan
No full textDecadence meets gothic in Manfred Macmillan (1907), a carefully constructed tale of doppelgangers, magical intrigue, and the rootless scion of a noble house. This annotated, first-ever English translation presents an early queer novel long unavailable except in the original Czech. Author Jiří Karásek ze Lvovic (1871–1951) was a major cultural figure in his native Bohemia and cultivated ties with fellow artists from across Central Europe. In their extensive scholarly introduction, translator Carleton Bulkin and translation scholar Brian James Baer situate the novel within longer histories of gay literature, fascinations with the occult, and the cultural and linguistic politics of so-called peripheral European nations. They persuasively frame Karásek as a queer author and cultural disruptor in the fin de siècle Habsburg space.
Karasék rejected Czech translations of ancient Greek writers that bowdlerized gay themes, and he personally and vigorously defended Oscar Wilde in print, both on the grounds of artistic freedom and of private morality. He also published a cycle of homoerotic poems under the title Sodom, confiscated by the Austrian authorities but republished in 1905 and repeatedly afterward. A colonized subject, a literary decadent, and a sexual outlaw, Karasék’s complex responses to his own marginalization can be traced through his fantastically strange novel trilogy Three Magicians. As the first volume in that series, Manfred Macmillan is a gorgeous, compelling, and important addition to expanding canons of LGBTQI+ literature
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