Efficiently querying Description Logic (DL) ontologies is becoming a vital task in various data-intensive DL applications. Considered as a basic service for answering object queries over DL ontologies, instance checking can be realized by using the most specific concept (MSC) method, which converts instance checking into subsumption problems. This method, however, loses its simplicity and efficiency when applied to large and complex ontologies, as it tends to generate very large MSCs that could lead to intractable reasoning. In this paper, we propose a revision to this MSC method for DL SHI , allowing it to generate much simpler and smaller concepts that are specific enough to answer a given query. With independence between computed MSCs, scalability for query answering can also be achieved by distributing and parallelizing the computations. An empirical evaluation shows the efficacy of our revised MSC method and the significant efficiency achieved when using it for answering object queries.
Recently, techniques for partitioning or modularizing ABoxes into logically-independent fragments have been developed @cite_23 @cite_44 . These techniques partition ABoxes into logically-independent modules, such that each will preserve complete information of a given set of individuals, and thus can be reasoned independently w.r.t. the TBox and be able to take advantage of existing parallel-processing techniques.
@cite_44 @cite_23
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The extraction of logically-independent fragments out of an ontology ABox can be useful for solving the tractability problem of querying ontologies with large ABoxes. In this paper, we propose a formal definition of an ABox module, such that it guarantees complete preservation of facts about a given set of individuals, and thus can be reasoned independently w.r.t. the ontology TBox. With ABox modules of this type, isolated or distributed (parallel) ABox reasoning becomes feasible, and more efficient data retrieval from ontology ABoxes can be attained. To compute such an ABox module, we present a theoretical approach and also an approximation for @math ontologies. Evaluation of the module approximation on different types of ontologies shows that, on average, extracted ABox modules are significantly smaller than the entire ABox, and the time for ontology reasoning based on ABox modules can be improved significantly. Recently, several approaches have been proposed on combining description logic (DL) reasoning with database techniques. In this paper we report on the LAS (Large Abox Store) system extending the DL reasoner Racer with a database used to store and query Tbox and Abox information. LAS stores for given knowledge bases their taxonomy and their complete Abox in its database. The Aboxes may contain role assertions. LAS can answer Tbox and Abox queries by combining SQL queries with DL reasoning. The architecture of LAS is based on merging techniques for so-called individual pseudo models. We propose a new Description Logic, called DL-Lite, specifically tailored to capture basic ontology languages, while keeping low complexity of reasoning. Reasoning here means not only computing subsumption between concepts, and checking satisfiability of the whole knowledge base, but also answering complex queries (in particular, conjunctive queries) over the set of instances maintained in secondary storage. We show that in DL-Lite the usual DL reasoning tasks are polynomial in the size of the TBox, and query answering is polynomial in the size of the ABox (i.e., in data complexity). To the best of our knowledge, this is the first result of polynomial data complexity for query answering over DL knowledge bases. A notable feature of our logic is to allow for a separation between TBox and ABox reasoning during query evaluation: the part of the process requiring TBox reasoning is independent of the ABox, and the part of the process requiring access to the ABox can be carried out by an SQL engine, thus taking advantage of the query optimization strategies provided by current DBMSs. This paper investigates optimization techniques and data structures exploiting the use of so-called pseudo models. These techniques are applied to speed up TBox and ABox reasoning for the description logics ALCNHR+ and ALC(D). The advances are demonstrated by an empirical analysis using the description logic system RACE that implements TBox and ABox reasoning for ALCNHR+. Data complexity of reasoning in description logics (DLs) estimates the performance of reasoning algorithms measured in the size of the ABox only. We show that, even for the very expressive DL SHIQ, satisfiability checking is data complete for NP. For applications with large ABoxes, this can be a more accurate estimate than the usually considered combined complexity, which is EXPTIME-complete. Furthermore, we identify an expressive fragment, Horn-SHIQ, which is data complete for P, thus being very appealing for practical usage. Current techniques for query answering over DL-Lite ontologies have severe limitations in practice, since they either produce complex queries that are inefficient during execution, or require expensive data pre-processing. In light of this, we present two complementary sets of results that aim at improving the overall peformance of query answering systems. We show how to create ABox repositories that are complete w.r.t. a significant portion of DL-Lite TBoxes, including those expressed in RDFS, but where the data is not explicitly expanded. Second, we show how to characterize ABox completeness by means of dependencies, and how to use these and equivalence to optimize DL-Lite TBoxes. These results allow us to reduce the cost of query rewriting, often dramatically, and to generate highly efficient queries. We have implemented a novel system for query answering over DL-Lite ontologies that incorporates these techniques, and we present a series of data-intensive evaluations that show their effectiveness. The DL-Lite family of Description Logics has been designed with the specific goal of allowing for answering complex queries (in particular, conjunctive queries) over ontologies with very large instance sets (ABoxes). So far, in DL-Lite systems, this goal has been actually achieved only for relatively simple (short) conjunctive queries. In this paper we present Presto, a new query answering technique for DL-Lite ontologies, and an experimental comparison of Presto with the main previous approaches to query answering in DL-Lite. In practice, our experiments show that, in real ontologies, current techniques are only able to answer conjunctive queries of less than 7-10 atoms (depending on the complexity of the TBox), while Presto is actually able to handle conjunctive queries of up to 30 atoms. Furthermore, in the cases that are already successfully handled by previous approaches, Presto is significantly more efficient. From the Publisher: Multiprocessor Execution of Logic Programs addresses the problem of efficient implementation of Logic Programming Languages, specifically Prolog, on multiprocessor architectures. The approaches and implementations developed attempt to take full advantage of sequential implementation technology developed for Prolog (such as the WAM) while exploiting all forms of control parallelism present in Logic Programs, namely, or-parallelism, independent and-parallelism and dependent and-parallelism. Coverage includes a thorough survey of parallel implementation techniques and parallel systems developed for Prolog. Multiprocessor Execution of Logic Programs will be useful for people implementing parallel logic programming systems, parallel symbolic systems Parallel AI systems, and parallel theorem proving systems. This work will also be useful to people who wish to learn about implementation of parallel logic programming systems. We describe an extension of the description logic underlying OWL-DL, SHOIN, with a number of expressive means that we believe will make it more useful in practice. Roughly speaking, we extend SHOIN with all expressive means that were suggested to us by ontology developers as useful additions to OWL-DL, and which, additionally, do not affect its decidability and practicability. We consider complex role inclusion axioms of the form R o S ⊑ R or S o R ⊑ R to express propagation of one property along another one, which have proven useful in medical terminologies. Furthermore, we extend SHOIN with reflexive, antisymmetric, and irreflexive roles, disjoint roles, a universal role, and constructs ∃R. Self, allowing, for instance, the definition of concepts such as a "narcist". Finally, we consider negated role assertions in Aboxes and qualified number restrictions. The resulting logic is called SROIQ. We present a rather elegant tableau-based reasoning algorithm: it combines the use of automata to keep track of universal value restrictions with the techniques developed for SHOIQ. The logic SROIQ has been adopted as the logical basis for the next iteration of OWL, OWL 1.1. An important aspect of the packaging of digital networks is the allocation of logic gates to modules such that a predetermined objective function is minimized. In order to develop techniques for this partitioning of a logic network we have considered the following problem: Given an acyclic combinational network composed of various primitive blocks such as NOR gates, assume that a maximum of M gates can be "clustered" together into larger modules, and that a maximum of P pins can be accommodated in each larger module. Assume also that in a network composed of such larger modules, no delay is encountered on the interconnections linking two gates internal to a module and a delay of one time unit is encountered on interconnections linking two gates in different modules . Find an easily applied algorithm that will result in a network such that the maximum delay through the network is minimized. Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner. The generation of RDF data has accelerated to the point where many data sets need to be partitioned across multiple machines in order to achieve reasonable performance when querying the data. Although tremendous progress has been made in the Semantic Web community for achieving high performance data management on a single node, current solutions that allow the data to be partitioned across multiple machines are highly inecient. In this paper, we introduce a scalable RDF data management system that is up to three orders of magnitude more ecient than popular multi-node RDF data management systems. In so doing, we introduce techniques for (1) leveraging state-of-the-art single node RDF-store technology (2) partitioning the data across nodes in a manner that helps accelerate query processing through locality optimizations and (3) decomposing SPARQL queries into high performance fragments that take advantage of how data is partitioned in a cluster. The goal of this paper is to present how the Prolog Technology Theorem Proving (PTTP) approach can be used for ABox-reasoning. This work presents an inference algorithm over the language ALC, and evaluates its performance highlighting the advantages and drawbacks of this method. Two emerging hardware trends will dominate the database system technology in the near future: increasing main memory capacities of several TB per server and massively parallel multi-core processing. Many algorithmic and control techniques in current database technology were devised for disk-based systems where I O dominated the performance. In this work we take a new look at the well-known sort-merge join which, so far, has not been in the focus of research in scalable massively parallel multi-core data processing as it was deemed inferior to hash joins. We devise a suite of new massively parallel sort-merge (MPSM) join algorithms that are based on partial partition-based sorting. Contrary to classical sort-merge joins, our MPSM algorithms do not rely on a hard to parallelize final merge step to create one complete sort order. Rather they work on the independently created runs in parallel. This way our MPSM algorithms are NUMA-affine as all the sorting is carried out on local memory partitions. An extensive experimental evaluation on a modern 32-core machine with one TB of main memory proves the competitive performance of MPSM on large main memory databases with billions of objects. It scales (almost) linearly in the number of employed cores and clearly outperforms competing hash join proposals -- in particular it outperforms the "cutting-edge" Vectorwise parallel query engine by a factor of four. Self-reconfigurable robots are versatile systems consisting of large numbers of independent modules. Effective use of these systems requires parallel actuation and planning, both for efficiency and independence from a central controller. In this paper we present the PacMan algorithm, a technique for distributed actuation and planning for systems with two- or three-dimensional unit-compressible modules. We give two versions of the algorithm along with correctness analysis. We also analyze the parallel actuation capability of the algorithm, showing that it will not deadlock and will avoid disconnecting the robot. We have implemented PacMan on the Crystal robot, a hardware system developed in our lab, and we present experiments and discuss the feasibility of large-scale implementation. Social networks have been part of people's daily life and plenty of users have registered accounts in multiple social networks. Interconnections among multiple social networks add a multiplier effect to social applications when fully used. With the sharp expansion of network size, traditional standalone algorithms can no longer support computing on large scale networks while alternatively, distributed and parallel computing become a solution to utilize the data-intensive information hidden in multiple social networks. As such, synergistic partitioning, which takes the relationships among different networks into consideration and focuses on partitioning the same nodes of different networks into same partitions. With that, the partitions containing the same nodes can be assigned to the same server to improve the data locality and reduce communication overhead among servers, which are very important for distributed applications. To date, there have been limited studies on multiple large scale network partitioning due to three major challenges: 1) the need to consider relationships across multiple networks given the existence of intricate interactions, 2) the difficulty for standalone programs to utilize traditional partitioning methods, 3) the fact that to generate balanced partitions is NP-complete. In this paper, we propose a novel framework to partition multiple social networks synergistically. In particular, we apply a distributed multilevel k-way partitioning method to divide the first network into k partitions. Based on the given anchor nodes which exist in all the social networks and the partition results of the first network, using MapReduce, we then develop a modified distributed multilevel partitioning method to divide other networks. Extensive experiments on two real data sets demonstrate that our method can significantly outperform baseline independent-partitioning method in accuracy and scalability. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. In this paper we investigate updates of knowledge bases represented by logic programs. In order to represent negative information, we use generalized logic programs which allow default negation not only in rule bodies but also in their heads. We start by introducing the notion of an update P U of one logic program P by another logic program U. Subsequently, we provide a precise semantic characterization of P U , and study some basic properties of program updates. In particular, we show that our update programs generalize the notion of interpretation update. We then extend this notion to compositional sequences of logic programs updates P1 P2 ; defining a dynamic program update, and thereby introducing the paradigm of dynamic logic programming. This paradigm significantly facilitates modularization of logic programming, and thus modularization of non-monotonic reasoning as a whole. Specifically, suppose that we are given a set of logic program modules, each describing a diAerent state of our knowledge of the world. DiAerent states may represent diAerent time points or diAerent sets of priorities or perhaps even diAerent viewpoints. Consequently, program modules may contain mutually contradictory as well as overlapping information. The role of the dynamic program update is to employ the mutual relationships existing between diAerent modules to precisely determine, at any given module composition stage, the declarative as well as the procedural semantics of the combined program resulting from the modules. ” 2000 Elsevier Science Inc. All rights reserved. Parallel database systems horizontally partition large amounts of structured data in order to provide parallel data processing capabilities for analytical workloads in shared-nothing clusters. One major challenge when horizontally partitioning large amounts of data is to reduce the network costs for a given workload and a database schema. A common technique to reduce the network costs in parallel database systems is to co-partition tables on their join key in order to avoid expensive remote join operations. However, existing partitioning schemes are limited in that respect since only subsets of tables in complex schemata sharing the same join key can be co-partitioned unless tables are fully replicated. In this paper we present a novel partitioning scheme called predicate-based reference partition (or PREF for short) that allows to co-partition sets of tables based on given join predicates. Moreover, based on PREF, we present two automatic partitioning design algorithms to maximize data-locality. One algorithm only needs the schema and data whereas the other algorithm additionally takes the workload as input. In our experiments we show that our automated design algorithms can partition database schemata of different complexity and thus help to effectively reduce the runtime of queries under a given workload when compared to existing partitioning approaches. Historically, papers have been physically bound to the journal in which they were published; but in the digital age papers are available individually, no longer tied to their respective journals. Hence, papers now can be read and cited based on their own merits, independently of the journal's physical availability, reputation, or impact factor (IF). We compare the strength of the relationship between journals' IFs and the actual citations received by their respective papers from 1902 to 2009. Throughout most of the 20th century, papers' citation rates were increasingly linked to their respective journals' IFs. However, since 1990, the advent of the digital age, the relation between IFs and paper citations has been weakening. This began first in physics, a field that was quick to make the transition into the electronic domain. Furthermore, since 1990 the overall proportion of highly cited papers coming from highly cited journals has been decreasing and, of these highly cited papers, the proportion not coming from highly cited journals has been increasing. Should this pattern continue, it might bring an end to the use of the IF as a way to evaluate the quality of journals, papers, and researchers. © 2012 Wiley Periodicals, Inc.