1,721,013 research outputs found
Unlocking Historical Insights: Developing a Dataset from Historical Archives
No full textThe proliferation of data on the Web has resulted in an increased need for effective techniques to extract relevant and valuable knowledge from this data. The intersection of the fields of Information Extraction and Semantic Web has created new opportunities to improve ontology-based information extraction tools. However, the development and evaluation of such systems have been hampered by the scarcity of annotated documents, particularly in historical domains. This article discusses the current state of our work in creating a large RDF dataset that aims to support the development of ontology-based extraction tools. The dataset was created through manual annotation by domain experts as part of the arkivo project and contains approximately 300,000 triples, which are freely available. This dataset can be used as a benchmark to evaluate systems that automatically extract entities and annotate documents
A Multi-engine Solver for Quantified Boolean Formulas
No full textn this paper we study the problem of yielding robust performances
from current state-of-the-art solvers for quantified Boolean formulas (QBFs).
Building on top of existing QBF solvers, we implement a new multi-engine solver
which can inductively learn its solver selection strategy. Experimental results con-
firm that our solver is always more robust than each single engine, that it is stable
with respect to various perturbations, and that such results can be partially ex-
plained by a handful of features playing a crucial role in our solver
Building the semantic layer of the Józef Piłsudski digital archive with an ontology-based approach
No full textUsing semantic web technologies is becoming an efficient way to overcome metadata storage and data integration problems in digital archives, thus enhancing the accuracy of the search process and leading to the retrieval of more relevant results. In this paper, the results of the implementation of the semantic layer of the Józef Piłsudski Institute of America digital archive are presented. In order to represent and integrate data about the archival collections housed by the institute, the authors developed arkivo, an ontology that accommodates the archival description of records but also provides a reference schema for publishing linked data. The authors describe the application of arkivo to the digitized archival collections of the institute, with emphasis on how these resources have been linked to external datasets in the linked data cloud. They also show the results of an experiment focused on the query answering task involving a state-of-the-art triple store system. The dataset related to the Piłsudski Institute archival collections has been made available for ontology benchmarking purposes
An Empirical study of QBF encodings: from treewidth estimation to useful preprocessing
No full textFrom an empirical point of view, the hardness of quantified Boolean formulas (QBFs), can be characterized by the (in)ability of current state-of-the-art QBF solvers to decide about the truth of formulas given limited computational resources. In this paper, we start from the problem of computing empirical hardness markers, i.e., features that can discriminate between hard and easy QBFs, and we end up showing that such markers can be useful to improve our understanding of QBF preprocessors. In particular, considering the connection between classes of tractable QBFs and the treewidth of associated graphs, we show that (an approximation of) treewidth is indeed a marker of empirical hardness and it is the only parameter which succeeds consistently in being so, even considering several other purely syntactic candidates which have been successfully employed to characterize QBFs in other contexts. We also show that treewidth approximations can be useful to describe the effect of QBF preprocessors, in that some QBF solvers benefit from a preprocessing phase when it reduces the treewidth of their input. Our experiments suggest that structural simplifications reducing treewidth are a potential enabler for the solution of hard QBF encodings
A Self-adaptive multi-engine solver for quantified Boolean formulas
No full textIn this paper we study the problem of engineering a robust solver for quantified Boolean formulas (QBFs), i.e., a tool that can efficiently solve formulas across different problem domains without the need for domain-specific tuning. The paper presents two main empirical results along this line of research. Our first result is the development of a multi-engine solver, i.e., a tool that selects among its reasoning engines the one which is more likely to yield optimal results. In particular, we show that syntactic QBF features can be correlated to the performances of existing QBF engines across a variety of domains. We also show how a multi-engine solver can be obtained by carefully picking state-of-the-art QBF solvers as basic engines, and by harnessing inductive reasoning techniques to learn engine-selection policies. Our second result is the improvement of our multi-engine solver with the capability of updating the learned policies when they fail to give good predictions. In this way the solver becomes also self-adaptive, i.e., able to adjust its internal models when the usage scenario changes substantially. The rewarding results obtained in our experiments show that our solver AQME--Adaptive QBF Multi-Engine--can be more robust and efficient than state-of-the-art single-engine solvers, even when it is confronted with previously uncharted formulas and competitors
Cyber-physical planning: Deliberation for hybrid systems with a continuous numeric state
No full textCyber-physical systems pose unique deliberation challenges, where complex strategies must be autonomously derived and executed in the physical world, relying on continuous state representations and subject to safety and security constraints. Robots are a typical example of cyber-physical systems where high-level decisions must be reconciled with motion-level decisions in order to provide guarantees on the validity and efficiency of the plan. In this work we propose techniques to refine a high-level plan into a continuous state trajectory. The refinement is done by translating a high-level plan into a nonlinear optimization problem with constraints that can encode the intrinsic limitations and dynamics of the system as well as the rules for its continuous control. The refinement process either succeeds or yields an explanation that we exploit to refine the search space of a domain-independent task planner. We evaluate our approach on existing PDDL+ benchmarks and on a more realistic and challenging rover navigation problem
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