2,684 research outputs found

    Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

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    Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

    Answer Set Programming with External Sources

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    Answer Set Programming (ASP) is a well-known problem solving approach based on nonmonotonic logic programs and efficient solvers. To enable access to external information, HEX-programs extend programs with external atoms, which allow for a bidirectional communication between the logic program and external sources of computation (e.g., description logic reasoners and Web resources). Current solvers evaluate HEX-programs by a translation to ASP itself, in which values of external atoms are guessed and verified after the ordinary answer set computation. This elegant approach does not scale with the number of external accesses in general, in particular in presence of nondeterminism (which is instrumental for ASP). Hence, there is a need for genuine algorithms which handle external atoms as first-class citizens, which is the main focus of this PhD project. In the first phase of the project, state-of-the-art conflict driven algorithms were already integrated into the prototype system dlvhex and extended to external sources. In particular, the evaluation of external sources may trigger a learning procedure, such that the reasoner gets additional information about the internals of external sources. Moreover, problems on the second level of the polynomial hierarchy were addressed by integrating a minimality check, based on unfounded sets. First experimental results show already clear improvements

    Incremental Processing and Optimization of Update Streams

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    Over the recent years, we have seen an increasing number of applications in networking, sensor networks, cloud computing, and environmental monitoring, which monitor, plan, control, and make decisions over data streams from multiple sources. We are interested in extending traditional stream processing techniques to meet the new challenges of these applications. Generally, in order to support genuine continuous query optimization and processing over data streams, we need to systematically understand how to address incremental optimization and processing of update streams for a rich class of queries commonly used in the applications. Our general thesis is that efficient incremental processing and re-optimization of update streams can be achieved by various incremental view maintenance techniques if we cast the problems as incremental view maintenance problems over data streams. We focus on two incremental processing of update streams challenges currently not addressed in existing work on stream query processing: incremental processing of transitive closure queries over data streams, and incremental re-optimization of queries. In addition to addressing these specific challenges, we also develop a working prototype system Aspen, which serves as an end-to-end stream processing system that has been deployed as the foundation for a case study of our SmartCIS application. We validate our solutions both analytically and empirically on top of our prototype system Aspen, over a variety of benchmark workloads such as TPC-H and LinearRoad Benchmarks

    Answering SPARQL queries modulo RDF Schema with paths

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    SPARQL is the standard query language for RDF graphs. In its strict instantiation, it only offers querying according to the RDF semantics and would thus ignore the semantics of data expressed with respect to (RDF) schemas or (OWL) ontologies. Several extensions to SPARQL have been proposed to query RDF data modulo RDFS, i.e., interpreting the query with RDFS semantics and/or considering external ontologies. We introduce a general framework which allows for expressing query answering modulo a particular semantics in an homogeneous way. In this paper, we discuss extensions of SPARQL that use regular expressions to navigate RDF graphs and may be used to answer queries considering RDFS semantics. We also consider their embedding as extensions of SPARQL. These SPARQL extensions are interpreted within the proposed framework and their drawbacks are presented. In particular, we show that the PSPARQL query language, a strict extension of SPARQL offering transitive closure, allows for answering SPARQL queries modulo RDFS graphs with the same complexity as SPARQL through a simple transformation of the queries. We also consider languages which, in addition to paths, provide constraints. In particular, we present and compare nSPARQL and our proposal CPSPARQL. We show that CPSPARQL is expressive enough to answer full SPARQL queries modulo RDFS. Finally, we compare the expressiveness and complexity of both nSPARQL and the corresponding fragment of CPSPARQL, that we call cpSPARQL. We show that both languages have the same complexity through cpSPARQL, being a proper extension of SPARQL graph patterns, is more expressive than nSPARQL.Comment: RR-8394; alkhateeb2003

    Reasoning techniques for analysis and refinement of policies for service management

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    The work described in this technical report falls under the general problem of developing methods that would allow us to engineer software systems that are reliable and would offer a certain acceptable level of quality in their operation. This report shows how the analysis and refinement of policies for Quality of Service can be carried out within logic by exploiting forms of abductive and argumentative reasoning. In particular, it provides two main contributions. The first is an extension of earlier work on the use of abductive reasoning for automatic policy refinement by exploiting the use of integrity constraints within abduction and its integration with constraint solving. This has allowed us to enhance this refinement process in various ways, e.g. supporting parameter values derivation to quantify abstract refinement to specific policies ready to be put in operation, and calculating utility values to determine optimal refined policies. The second contribution is a new approach for modelling and formulating Quality of Service policies, and more general policies for software requirements, as preference policies within logical frameworks of argumentation. This is shown to be a flexible and declarative approach to the analysis of such policies through high-level semantic queries of argumentation, demonstrated here for the particular case of network firewall policies where the logical framework of argumentation allows us to detect anomalies in the firewalls and facilitates the process of their resolution. To our knowledge this is the first time that the link between argumentation and the specification and analysis of requirement policies has been studied

    Investigating language corpora as a grammar development resource

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    The digital era has brought new concepts and transformations into language development and has given rise to technology-based approaches to learner autonomy. It has shifted the focus from deductive to inductive learning, where the concept of ‘noticing’ (Schmidt, 1990) language forms is promoted. Literature suggests that this type of student-centered self-discovery of lexico-grammatical patterns can be greatly aided by corpus linguistics methods, specifically ‘Data-Driven Learning’ (DDL) (Johns, 1986; Braun, 2005; O’Keeffe et al, 2007). It reports on the valuable potential of DDL for developing learners’ multi-literacies and cognitive strategies, particularly raising their awareness of lexico-grammatical patterning (O’Keeffe and Farr, 2003). However, insights from corpus-based studies have not been widely applied in teaching practices (Reppen, 2022; Zareva, 2017). It has also been proposed that DDL enhances accurate representation of language, raises cultural understanding, provides learners with the freedom to explore and discover the language, and fosters learner autonomy, thus making them more effective language learners (Flowerdew, 2015). This affordance led to the design of a longitudinal experimental study which aimed to provide useful skills and processes in the use of language corpora as a grammar development resource in the pre-intermediate EFL classroom in an Armenain context outside of higher education. The evaluation data included pre-, post-, progress-, delayed post-test data, and Learner Autonomy Profile (LAP) form, the statistical analysis of which revealed the beneficial impact of the computer-based inductive approach of DDL on the learners’ grammar competency, independent learning skills, as well as the contribution of cognitive strategies to proceduralization of knowledge. It also included semi-structured interview data, which uncovered the learners’ increased engagement in the learning process, the positive change in their attitudes towards their own learning, and the ways of demonstrating autonomous abilities in working with concordances. These data also brought to light some of the fears and challenges of using DDL, as well discussing its theoretical and pedagogical underpinnings aligned with psychological processes of learning. The findings will serve all the participants of this hugely important ELT sector - researchers, language educators and learners. They will gain insights as to what is necessary to tap learners’ implicit long-term knowledge, to prepare them both psychologically and practically for independence so that they can be armed with confidence, interest in discovering the language, knowledge about their own learning, and understanding of how to make use of their learning styles and strategies. Keywords: conventional/technology-enhanced EFL classroom, corpus linguistics, data-driven learning (DDL), inductive/deductive grammar learning, direct/indirect written feedback, explicit/implicit knowledge, language awareness, learner autonomy.N
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