In this paper, we focus on a novel knowledge reuse scenario where the knowledge in the source schema needs to be translated to a semantically heterogeneous target schema. We refer to this task as "knowledge translation" (KT). Unlike data translation and transfer learning, KT does not require any data from the source or target schema. We adopt a probabilistic approach to KT by representing the knowledge in the source schema, the mapping between the source and target schemas, and the resulting knowledge in the target schema all as probability distributions, specially using Markov random fields and Markov logic networks. Given the source knowledge and mappings, we use standard learning and inference algorithms for probabilistic graphical models to find an explicit probability distribution in the target schema that minimizes the Kullback-Leibler divergence from the implicit distribution. This gives us a compact probabilistic model that represents knowledge from the source schema as well as possible, respecting the uncertainty in both the source knowledge and the mapping. In experiments on both propositional and relational domains, we find that the knowledge obtained by KT is comparable to other approaches that require data, demonstrating that knowledge can be reused without data.
Deductive knowledge translation @cite_7 essentially tries to solve the same problem, but it only considers deterministic knowledge and mappings. Our KT work can handle knowledge and mappings with uncertainty, which is more general than the deterministic scenario deductive knowledge translation @cite_7 can handle.
@cite_7
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Many real-world knowledge-based systems must deal with information coming from different sources that invariably leads to incompleteness, overspecification, or inherently uncertain content. The presence of these varying levels of uncertainty doesn't mean that the information is worthless --- rather, these are hurdles that the knowledge engineer must learn to work with. In this paper, we continue work on an argumentation-based framework that extends the well-known Defeasible Logic Programming (DeLP) language with probabilistic uncertainty, giving rise to the Defeasible Logic Programming with Presumptions and Probabilistic Environments (DeLP3E) model. Our prior work focused on the problem of belief revision in DeLP3E, where we proposed a non-prioritized class of revision operators called AFO (Annotation Function-based Operators) to solve this problem. In this paper, we further study this class and argue that in some cases it may be desirable to define revision operators that take quantitative aspects into account, such as how the probabilities of certain literals or formulas of interest change after the revision takes place. To the best of our knowledge, this problem has not been addressed in the argumentation literature to date. We propose the QAFO (Quantitative Annotation Function-based Operators) class of operators, a subclass of AFO, and then go on to study the complexity of several problems related to their specification and application in revising knowledge bases. Finally, we present an algorithm for computing the probability that a literal is warranted in a DeLP3E knowledge base, and discuss how it could be applied towards implementing QAFO-style operators that compute approximations rather than exact operations. Considering data size and privacy concerns in a distributed setting, it is neither desirable nor feasible to translate data from one resource to another in data mining. Rather, it makes more sense to first mine knowledge from one data resource and then translate the discovered knowledge (models) to another for knowledge reuse. Although there have been successful research efforts in knowledge transfer, the knowledge translation problem in the semantically heterogenous scenario has not been addressed adequately. In this paper, we first propose to use Semantic Web ontologies to represent rule-based knowledge to make the knowledge computer "translatable". Instead of an inductive learning approach, we treat knowledge translation as a deductive inference. We elaborate a translation method with both the forward and backward chaining to address the asymmetry of translation. We show the effectiveness of our knowledge translation method in decision tree rules and association rules mined from sports and gene data respectively. In a more general context, this work illustrates the promise of a novel research which leverages ontologies and Semantic Web techniques to extend the knowledge transfer in data mining to the semantically heterogeneous scenario. The LF logical framework codifies a methodology for representing deductive systems, such as programming languages and logics, within a dependently typed λ-calculus. In this methodology, the syntactic and deductive apparatus of a system is encoded as the canonical forms of associated LF types; an encoding is correct (adequate) if and only if it defines a compositional bijection between the apparatus of the deductive system and the associated canonical forms. Given an adequate encoding, one may establish metatheoretic properties of a deductive system by reasoning about the associated LF representation. The Twelf implementation of the LF logical framework is a convenient and powerful tool for putting this methodology into practice. Twelf supports both the representation of a deductive system and the mechanical verification of proofs of metatheorems about it. The purpose of this article is to provide an up-to-date overview of the LF λ-calculus, the LF methodology for adequate representation, and the Twelf methodology for mechanizing metatheory. We begin by defining a variant of the original LF language, called Canonical LF, in which only canonical forms (long βη-normal forms) are permitted. This variant is parameterized by a subordination relation, which enables modular reasoning about LF representations. We then give an adequate representation of a simply typed λ-calculus in Canonical LF, both to illustrate adequacy and to serve as an object of analysis. Using this representation, we formalize and verify the proofs of some metatheoretic results, including preservation, determinacy, and strengthening. Each example illustrates a significant aspect of using LF and Twelf for formalized metatheory. We present a new approach for neural machine translation (NMT) using the morphological and grammatical decomposition of the words (factors) in the output side of the neural network. This architecture addresses two main problems occurring in MT, namely dealing with a large target language vocabulary and the out of vocabulary (OOV) words. By the means of factors, we are able to handle larger vocabulary and reduce the training time (for systems with equivalent target language vocabulary size). In addition, we can produce new words that are not in the vocabulary. We use a morphological analyser to get a factored representation of each word (lemmas, Part of Speech tag, tense, person, gender and number). We have extended the NMT approach with attention mechanism in order to have two different outputs, one for the lemmas and the other for the rest of the factors. The final translation is built using some linguistic information. We compare our extension with a word-based NMT system. The experiments, performed on the IWSLT'15 dataset translating from English to French, show that while the performance do not always increase, the system can manage a much larger vocabulary and consistently reduce the OOV rate. We observe up to 2 BLEU point improvement in a simulated out of domain translation setup. This paper proposes a novel translation-based knowledge graph embedding that preserves the logical properties of relations such as transitivity and symmetricity. The embedding space generated by existing translation-based embeddings do not represent transitive and symmetric relations precisely, because they ignore the role of entities in triples. Thus, we introduce a role-specific projection which maps an entity to distinct vectors according to its role in a triple. That is, a head entity is projected onto an embedding space by a head projection operator, and a tail entity is projected by a tail projection operator. This idea is applied to TransE, TransR, and TransD to produce lppTransE, lppTransR, and lppTransD, respectively. According to the experimental results on link prediction and triple classification, the proposed logical property preserving embeddings show the state-of-the-art performance at both tasks. These results prove that it is critical to preserve logical properties of relations while embedding knowledge graphs, and the proposed method does it effectively. One of the difficulties of neural machine translation (NMT) is the recall and appropriate translation of low-frequency words or phrases. In this paper, we propose a simple, fast, and effective method for recalling previously seen translation examples and incorporating them into the NMT decoding process. Specifically, for an input sentence, we use a search engine to retrieve sentence pairs whose source sides are similar with the input sentence, and then collect @math -grams that are both in the retrieved target sentences and aligned with words that match in the source sentences, which we call "translation pieces". We compute pseudo-probabilities for each retrieved sentence based on similarities between the input sentence and the retrieved source sentences, and use these to weight the retrieved translation pieces. Finally, an existing NMT model is used to translate the input sentence, with an additional bonus given to outputs that contain the collected translation pieces. We show our method improves NMT translation results up to 6 BLEU points on three narrow domain translation tasks where repetitiveness of the target sentences is particularly salient. It also causes little increase in the translation time, and compares favorably to another alternative retrieval-based method with respect to accuracy, speed, and simplicity of implementation. Many AI applications rely on knowledge about a relevant real-world domain that is encoded by means of some logical knowledge base (KB). The most essential benefit of logical KBs is the opportunity to perform automatic reasoning to derive implicit knowledge or to answer complex queries about the modeled domain. The feasibility of meaningful reasoning requires KBs to meet some minimal quality criteria such as logical consistency. Without adequate tool assistance, the task of resolving violated quality criteria in KBs can be extremely tough even for domain experts, especially when the problematic KB includes a large number of logical formulas or comprises complicated logical formalisms. Published non-interactive debugging systems often cannot localize all possible faults (incompleteness), suggest the deletion or modification of unnecessarily large parts of the KB (non-minimality), return incorrect solutions which lead to a repaired KB not satisfying the imposed quality requirements (unsoundness) or suffer from poor scalability due to the inherent complexity of the KB debugging problem. Even if a system is complete and sound and considers only minimal solutions, there are generally exponentially many solution candidates to select one from. However, any two repaired KBs obtained from these candidates differ in their semantics in terms of entailments and non-entailments. Selection of just any of these repaired KBs might result in unexpected entailments, the loss of desired entailments or unwanted changes to the KB. This work proposes complete, sound and optimal methods for the interactive debugging of KBs that suggest the one (minimally invasive) error correction of the faulty KB that yields a repaired KB with exactly the intended semantics. Users, e.g. domain experts, are involved in the debugging process by answering automatically generated queries about the intended domain. Corpus-based approaches to machine translation (MT) rely on the availability of parallel corpora. To produce user-acceptable translation outputs, such systems need high quality data to be efficiently trained, optimized and evaluated. However, building high quality dataset is a relatively expensive task. In this paper, we describe the data collection and analysis of a large database of 10.881 SMT translation output hypotheses manually corrected. These post-editions were collected using Amazon’s Mechanical Turk, following some ethical guidelines. A complete analysis of the collected data pointed out a high quality of the corrections with more than 87 of the collected post-editions that improve hypotheses and more than 94 of the crowdsourced post-editions which are at least of professional quality. We also post-edited 1,500 gold-standard reference translations (of bilingual parallel corpora generated by professional) and noticed that 72 of these translations needed to be corrected during post-edition. We computed a proximity measure between the different kind of translations and pointed out that reference translations are as far from the hypotheses as from the corrected hypotheses (i.e. the post-editions). In light of these last findings, we discuss the adequation of text-based generated reference translations to train setence-to-sentence based SMT systems. In this paper, we study a new learning paradigm for Neural Machine Translation (NMT). Instead of maximizing the likelihood of the human translation as in previous works, we minimize the distinction between human translation and the translation given by an NMT model. To achieve this goal, inspired by the recent success of generative adversarial networks (GANs), we employ an adversarial training architecture and name it as Adversarial-NMT. In Adversarial-NMT, the training of the NMT model is assisted by an adversary, which is an elaborately designed Convolutional Neural Network (CNN). The goal of the adversary is to differentiate the translation result generated by the NMT model from that by human. The goal of the NMT model is to produce high quality translations so as to cheat the adversary. A policy gradient method is leveraged to co-train the NMT model and the adversary. Experimental results on English @math French and German @math English translation tasks show that Adversarial-NMT can achieve significantly better translation quality than several strong baselines. The Systeme d'Acquisition des Connaissances Deontique (SACD), part of the Acquisition des Connaissances et Analyse de Textes project, which can generate a knowledge base from the logical structure of regulatory texts, is described. SACD can be used by domain specialists who know the text, characteristics and the intricacies of the legislation, or by knowledge engineers who know SACD and can modify the grammars. SACD performs all syntactic text processing and automatically manipulates the knowledge structures. The specialist interprets all semantic knowledge and performs any tasks requiring domain experience. The knowledge engineer adapts SACD's knowledge structures when the system behaves inaccurately. The logical context of regulatory texts is reviewed, and the text analysis, knowledge base operation, and knowledge base adaptation procedures of SACD are discussed. > Many models in computer education and assessment take into account difficulty. However, despite the positive results of models that take difficulty in to account, knowledge tracing is still used in its basic form due to its skill level diagnostic abilities that are very useful to teachers. This leads to the research question we address in this work: Can KT be effectively extended to capture item difficulty and improve prediction accuracy? There have been a variety of extensions to KT in recent years. One such extension was Baker's contextual guess and slip model. While this model has shown positive gains over KT in internal validation testing, it has not performed well relative to KT on unseen in-tutor data or post-test data, however, it has proven a valuable model to use alongside other models. The contextual guess and slip model increases the complexity of KT by adding regression steps and feature generation. The added complexity of feature generation across datasets may have hindered the performance of this model. Therefore, one of the aims of our work here is to make the most minimal of modifications to the KT model in order to add item difficulty and keep the modification limited to changing the topology of the model. We analyze datasets from two intelligent tutoring systems with KT and a model we have called KT-IDEM (Item Difficulty Effect Model) and show that substantial performance gains can be achieved with this minor modification that incorporates item difficulty. Motivated by large-scale multimedia applications we propose to learn mappings from high-dimensional data to binary codes that preserve semantic similarity. Binary codes are well suited to large-scale applications as they are storage efficient and permit exact sub-linear kNN search. The framework is applicable to broad families of mappings, and uses a flexible form of triplet ranking loss. We overcome discontinuous optimization of the discrete mappings by minimizing a piecewise-smooth upper bound on empirical loss, inspired by latent structural SVMs. We develop a new loss-augmented inference algorithm that is quadratic in the code length. We show strong retrieval performance on CIFAR-10 and MNIST, with promising classification results using no more than kNN on the binary codes. Several recent works on relation extraction have been applying the distant supervision paradigm: instead of relying on annotated text to learn how to predict relations, they employ existing knowledge bases (KBs) as source of supervision. Crucially, these approaches are trained based on the assumption that each sentence which mentions the two related entities is an expression of the given relation. Here we argue that this leads to noisy patterns that hurt precision, in particular if the knowledge base is not directly related to the text we are working with. We present a novel approach to distant supervision that can alleviate this problem based on the following two ideas: First, we use a factor graph to explicitly model the decision whether two entities are related, and the decision whether this relation is mentioned in a given sentence; second, we apply constraint-driven semi-supervision to train this model without any knowledge about which sentences express the relations in our training KB. We apply our approach to extract relations from the New York Times corpus and use Freebase as knowledge base. When compared to a state-of-the-art approach for relation extraction under distant supervision, we achieve 31 error reduction. In multi-instance multi-label learning (i.e. MIML), each example is not only represented by multiple instances but also associated with multiple labels. Most existing algorithms solve MIML problem via the intuitive way of identifying its equivalence in degenerated version of MIML. However, this identification process may lose useful information encoded in training examples and therefore be harmful to the learning algorithm's performance. In this paper, a novel algorithm named MIML-kNN is proposed for MIML by utilizing the popular k-nearest neighbor techniques. Given a test example, MIML-kNN not only considers its neighbors, but also considers its citers which regard it as their own neighbors. The label set of the test example is determined by exploiting the labeling information conveyed by its neighbors and citers. Experiments on two real-world MIML tasks, i.e. scene classification and text categorization, show that MIML-kNN achieves superior performance than some existing MIML algorithms. The paper proposes a new knowledge representation language, called DLP<, which extends disjunctive logic programming (with strong negation) by inheritance. The addition of inheritance enhances the knowledge modeling features of the language providing a natural representation of default reasoning with exceptions. A declarative model-theoretic semantics of DLP< is provided, which is shown to generalize the Answer Set Semantics of disjunctive logic programs. The knowledge modeling features of the language are illustrated by encoding classical nonmonotonic problems in DLP<. The complexity of DLP< is analyzed, proving that inheritance does not cause any computational overhead, as reasoning in DLP< has exactly the same complexity as reasoning in disjunctive logic programming. This is confirmed by the existence of an efficient translation from DLP< to plain disjunctive logic programming. Using this translation, an advanced KR system supporting the DLP< language has been implemented on top of the DLV system and has subsequently been integrated into DLV. For machine translation to tackle discourse phenomena, models must have access to extra-sentential linguistic context. There has been recent interest in modelling context in neural machine translation (NMT), but models have been principally evaluated with standard automatic metrics, poorly adapted to evaluating discourse phenomena. In this article, we present hand-crafted, discourse test sets, designed to test the models' ability to exploit previous source and target sentences. We investigate the performance of recently proposed multi-encoder NMT models trained on subtitles for English to French. We also explore a novel way of exploiting context from the previous sentence. Despite gains using BLEU, multi-encoder models give limited improvement in the handling of discourse phenomena: 50 accuracy on our coreference test set and 53.5 for coherence cohesion (compared to a non-contextual baseline of 50 ). A simple strategy of decoding the concatenation of the previous and current sentence leads to good performance, and our novel strategy of multi-encoding and decoding of two sentences leads to the best performance (72.5 for coreference and 57 for coherence cohesion), highlighting the importance of target-side context. This paper is concerned with the page migration (or file migration) problem (Black and Sleator, Technical Report CMU-CS-89-201, Department of Computer Science, Carnegie-Mellon University, 1989) as part of a large class of on-line problems. The page migration problem deals with the management of pages residing in a network of processors. In the classical problem there is only one copy of each page which is accessed by different processors over time. The page is allowed to be migrated between processors. However a migration incurs higher communication cost than an access (proportionally to the page size). The problem is that of deciding when and where to migrate the page in order to lower access costs. A more general setting is the k-page migration problem where we wish to maintain k copies of the page. The page migration problems are concerned with a dilemma common to many on-line problems: determining when it is beneficial to make configuration changes. We deal with the relaxed task systems model which captures a large class of problems of this type, that can be described as the generalization of some original task system problem (, J. ACM 39(4) (1992) 745-763). Given a c-competitive algorithm for a task system we show how to obtain a deterministic O(c2) and randomized O(c) competitive algorithms for the corresponding relaxed task system. The result implies deterministic algorithms for k-page migration by using k-server (, J. Algorithms 11(2) (1990) 208-230) algorithms, and for network leasing by using generalized Steiner tree algorithms (, Proc 7th Ann. ACM-SIAM Symp. on Discrete Algorithms, January 1996, pp. 68-74), as well as providing solutions for natural generalizations of other problems (e.g. storage rearrangement (, Proc. 36th Ann. IEEE Symp. on Foundations of Computer Science, October 1995, pp. 392-403)). We further study some special cases of the k-page migration problem and get optimal deterministic algorithms. For the classical page migration problem we present a deterministic algorithm that achieves a competitive ratio of 4:086, improving upon the previously best competitive ratio of 7 (, Proc. 25th ACM Symp. on Theory of Computing, May 1993, pp. 164-173). (The current lower bound on the problem is 3:148 (, J. Algorithms 24(1) (1997) 124-157). Copyright 2001 Elsevier Science B.V. This paper presents a technique for class-dependent decoding for statistical machine translation (SMT). The approach differs from previous methods of class-dependent translation in that the class-dependent forms of all models are integrated directly into the decoding process. We employ probabilistic mixture weights between models that can change dynamically on a segment-by-segment basis depending on the characteristics of the source segment. The effectiveness of this approach is demonstrated by evaluating its performance on travel conversation data. We used the approach to tackle the translation of questions and declarative sentences using class-dependent models. To achieve this, our system integrated two sets of models specifically built to deal with sentences that fall into one of two classes of dialog sentence: questions and declarations, with a third set of models built to handle the general class. The technique was thoroughly evaluated on data from 17 language pairs using 6 machine translation evaluation metrics. We found the results were corpus-dependent, but in most cases our system was able to improve translation performance, and for some languages the improvements were substantial. In a sparse-representation-based face recognition scheme, the desired dictionary should have good representational power (i.e., being able to span the subspace of all faces) while supporting optimal discrimination of the classes (i.e., different human subjects). We propose a method to learn an over-complete dictionary that attempts to simultaneously achieve the above two goals. The proposed method, discriminative K-SVD (D-KSVD), is based on extending the K-SVD algorithm by incorporating the classification error into the objective function, thus allowing the performance of a linear classifier and the representational power of the dictionary being considered at the same time by the same optimization procedure. The D-KSVD algorithm finds the dictionary and solves for the classifier using a procedure derived from the K-SVD algorithm, which has proven efficiency and performance. This is in contrast to most existing work that relies on iteratively solving sub-problems with the hope of achieving the global optimal through iterative approximation. We evaluate the proposed method using two commonly-used face databases, the Extended YaleB database and the AR database, with detailed comparison to 3 alternative approaches, including the leading state-of-the-art in the literature. The experiments show that the proposed method outperforms these competing methods in most of the cases. Further, using Fisher criterion and dictionary incoherence, we also show that the learned dictionary and the corresponding classifier are indeed better-posed to support sparse-representation-based recognition. Abstract In the framework of distributed network computing , it is known that not all Turing-decidable predicates on labeled networks can be decided locally whenever the computing entities are Turing machines (TM). This holds even if nodes are running non-deterministic Turing machines (NTM). In contrast, we show that every Turing-decidable predicate on labeled networks can be decided locally if nodes are running alternating Turing machines (ATM). More specifically, we show that, for every such predicate, there is a local algorithm for ATMs, with at most two alternations, that decides whether the actual labeled network satisfies that predicate. To this aim, we define a hierarchy of classes of decision tasks, where the lowest level contains tasks solvable with TMs, the first level those solvable with NTMs, and the level k > 1 contains those tasks solvable with ATMs with k − 1 alternations. We characterize the entire hierarchy, and show that it collapses in the second level.