Computing shortest paths between two given nodes is a fundamental operation over graphs, but known to be nontrivial over large disk-resident instances of graph data. While a number of techniques exist for answering reachability queries and approximating node distances efficiently, determining actual shortest paths (i. e. the sequence of nodes involved) is often neglected. However, in applications arising in massive online social networks, biological networks, and knowledge graphs it is often essential to find out many, if not all, shortest paths between two given nodes. In this paper, we address this problem and present a scalable sketch-based index structure that not only supports estimation of node distances, but also computes corresponding shortest paths themselves. Generating the actual path information allows for further improvements to the estimation accuracy of distances (and paths), leading to near-exact shortest-path approximations in real world graphs. We evaluate our techniques -implemented within a fully functional RDF graph database system -over large realworld social and biological networks of sizes ranging from tens of thousand to millions of nodes and edges. Experiments on several datasets show that we can achieve query response times providing several orders of magnitude speedup over traditional path computations while keeping the estimation errors between 0% and 1% on average.

Abstract-In this paper, we propose a scalable and highly efficient index structure for the reachability problem over graphs. We build on the well-known node interval labeling scheme where the set of vertices reachable from a particular node is compactly encoded as a collection of node identifier ranges. We impose an explicit bound on the size of the index and flexibly assign approximate reachability ranges to nodes of the graph such that the number of index probes to answer a query is minimized. The resulting tunable index structure generates a better range labeling if the space budget is increased, thus providing a direct control over the trade off between index size and the query processing performance. By using a fast recursive querying method in conjunction with our index structure, we show that, in practice, reachability queries can be answered in the order of microseconds on an off-the-shelf computer -even for the case of massive-scale real world graphs. Our claims are supported by an extensive set of experimental results using a multitude of benchmark and real-world web-scale graph datasets.

Measuring the semantic relatedness between two entities is the basis for numerous tasks in IR, NLP, and Web-based knowledge extraction. This paper focuses on disambiguating names in a Web or text document by jointly mapping all names onto semantically related entities registered in a knowledge base. To this end, we have developed a novel notion of semantic relatedness between two entities represented as sets of weighted (multi-word) keyphrases, with consideration of partially overlapping phrases. This measure improves the quality of prior link-based models, and also eliminates the need for (usually Wikipedia-centric) explicit interlinkage between entities. Thus, our method is more versatile and can cope with long-tail and newly emerging entities that have few or no links associated with them. For efficiency, we have developed approximation techniques based on min-hash sketches and locality-sensitive hashing. Our experiments on semantic relatedness and on named entity disambiguation demonstrate the superiority of our method compared to state-of-the-art baselines.

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We investigate a new approach to the design of distributed, sharednothing RDF engines. Our engine, coined "TriAD", combines joinahead pruning via a novel form of RDF graph summarization with a locality-based, horizontal partitioning of RDF triples into a gridlike, distributed index structure. The multi-threaded and distributed execution of joins in TriAD is facilitated by an asynchronous Message Passing protocol which allows us to run multiple join operators along a query plan in a fully parallel, asynchronous fashion. We believe that our architecture provides a so far unique approach to join-ahead pruning in a distributed environment, as the more classical form of sideways information passing would not permit for executing distributed joins in an asynchronous way. Our experiments over the LUBM, BTC and WSDTS benchmarks demonstrate that TriAD consistently outperforms centralized RDF engines by up to two orders of magnitude, while gaining a factor of more than three compared to the currently fastest, distributed engines. To our knowledge, we are thus able to report the so far fastest query response times for the above benchmarks using a mid-range server and regular Ethernet setup.

The need for scalable and efficient RDF stores has seen a high demand recently. Many efficient systems, both centralized and distributed, have been proposed. Since a row-oriented output is required by SPARQL, most of the current systems rely on relational joins. One of the problems with relational joins, though, is a performance bottleneck imposed by the generation of large intermediate relations which could be avoided by using more accurate data and pruning statistics. To address this problem, recently several systems have been proposed that employ bisimulation-based graph summaries -adopted from XML indexing -over large RDF graphs in order to facilitate join-ahead pruning. In this paper, we discuss a different, locality-based, graph summarization approach for RDF data and highlight its utilization for join-ahead pruning in a distributed SPARQL engine. Based on our recently developed TriAD engine, we present a detailed comparison of processing techniques for these graph summaries over the synthetic LUBM benchmark.

Graphs are increasingly used to model a variety of loosely structured data such as biological or social networks and entity-relationships. Given this profusion of large-scale graph data, efficiently discovering interesting substructures buried within is essential. These substructures are typically used in determining subsequent actions, such as conducting visual analytics by humans or designing expensive biomedical experiments. In such settings, it is often desirable to constrain the size of the discovered results in order to directly control the associated costs. In this paper, we address the problem of finding cardinality-constrained connected subtrees in large node-weighted graphs that maximize the sum of weights of selected nodes. We provide an efficient constant-factor approximation algorithm for this strongly NP-hard problem. Our techniques can be applied in a wide variety of application settings, for example in differential analysis of graphs, a problem that frequently arises in bioinformatics but also has applications on the web

As Semantic Web efforts continue to gather steam, the RDF engines are faced with graphs with millions of nodes and billions of edges. While much recent work in addressing the resulting scalability issues in processing queries over these datasets have mainly considered SPARQL 1.0, the next-generation query language recommendations have proposed the addition of regular expression restricted navigation queries into SPARQL. We address the problem of supporting efficient processing of property paths into RDF-3Xa high-performance RDF engine.In this paper, we restrict our attention to a restricted definition of property paths that is not only tractable but also most commonly used -instead of enumerating all paths that satisfy the given query, we focus on regular expression based reachability queries. Based on this, we make the following three major technical contributions: first, we present a detailed account of integrating the recently proposed highly compact reachability index called FERRARI into the RDF-3X engine to support property path evaluation; second, we show how property path queries can be efficiently answered using multiple instances of this index -one instance for each distinct label in the graph; and finally, we develop a set of queries over realworld RDF data that can serve as benchmark set for evaluating the efficiency of property path queries. Our experimental results over Yago2, a large RDF-based knowledge base, show that our proposed approach is highly scalable and flexible.