Term-Specific Eigenvector-Centrality in Multi-Relation Networks

Fuzzy matching and ranking are two information retrieval techniques widely used in web search. Their application to structured data, however, remains an open problem. This article investigates how eigenvector-centrality can be used for approximate matching in multi-relation graphs, that is, graphs where connections of many different types may exist. Based on an extension of the PageRank matrix, eigenvectors representing the distribution of a term after propagating term weights between related data items are computed. The result is an index which takes the document structure into account and can be used with standard document retrieval techniques. As the scheme takes the shape of an index transformation, all necessary calculations are performed during index tim

Using Incomplete Information for Complete Weight Annotation of Road Networks -- Extended Version

We are witnessing increasing interests in the effective use of road networks. For example, to enable effective vehicle routing, weighted-graph models of transportation networks are used, where the weight of an edge captures some cost associated with traversing the edge, e.g., greenhouse gas (GHG) emissions or travel time. It is a precondition to using a graph model for routing that all edges have weights. Weights that capture travel times and GHG emissions can be extracted from GPS trajectory data collected from the network. However, GPS trajectory data typically lack the coverage needed to assign weights to all edges. This paper formulates and addresses the problem of annotating all edges in a road network with travel cost based weights from a set of trips in the network that cover only a small fraction of the edges, each with an associated ground-truth travel cost. A general framework is proposed to solve the problem. Specifically, the problem is modeled as a regression problem and solved by minimizing a judiciously designed objective function that takes into account the topology of the road network. In particular, the use of weighted PageRank values of edges is explored for assigning appropriate weights to all edges, and the property of directional adjacency of edges is also taken into account to assign weights. Empirical studies with weights capturing travel time and GHG emissions on two road networks (Skagen, Denmark, and North Jutland, Denmark) offer insight into the design properties of the proposed techniques and offer evidence that the techniques are effective.Comment: This is an extended version of "Using Incomplete Information for Complete Weight Annotation of Road Networks," which is accepted for publication in IEEE TKD

Building ontologies from folksonomies and linked data: Data structures and Algorithms

We present the data structures and algorithms used in the approach for building domain ontologies from folksonomies and linked data. In this approach we extracts domain terms from folksonomies and enrich them with semantic information from the Linked Open Data cloud. As a result, we obtain a domain ontology that combines the emergent knowledge of social tagging systems with formal knowledge from Ontologies

Applying an abstract data structure description approach to parallelizing scientific pointer programs

Even though impressive progress has been made in the area of parallelizing scientific programs with arrays, the application of similar techniques to programs with pointer data structures has remained difficult. Unlike arrays which have a small number of well-defined properties that can be utilized by a parallelizing compiler, pointer data structures are used to implement a wide variety of structures that exhibit a much more diverse set of properties. The complexity and diversity of such properties means that, in general, scientific programs with pointer data structures cannot be effectively analyzed by an optimizing and parallelizing compiler.In order to provide a system in which the compiler can fully utilize the properties of different types of pointer data structures, we have developed a mechanism for the Abstract Description of Data Structures (ADDS). With our approach, the programmer can explicitly describe important properties such as dimensionality of the pointer data structure, independence of dimensions, and direction of traversal. These abstract descriptions of pointer data structures are then used by the compiler to guide analysis, optimization, and parallelization.In this paper we summarize the ADDS approach through the use of numerous examples of data structures used in scientific computations, we illustrate how such declarations are natural and non-tedious to specify, and we show how the ADDS declarations can be used to improve compile-time analysis. In order to demonstrate the viability of our approach, we show how such techniques can be used to parallelize an important class of scientific codes which naturally use recursive pointer data structures. In particular, we use our approach to develop the parallelization of an N-body simulation that is based on a relatively complicated pointer data structure, and we report the speedup results for a Sequent multiprocessor

Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness

In many applications, it is important to characterize the way in which two concepts are semantically related. Knowledge graphs such as ConceptNet provide a rich source of information for such characterizations by encoding relations between concepts as edges in a graph. When two concepts are not directly connected by an edge, their relationship can still be described in terms of the paths that connect them. Unfortunately, many of these paths are uninformative and noisy, which means that the success of applications that use such path features crucially relies on their ability to select high-quality paths. In existing applications, this path selection process is based on relatively simple heuristics. In this paper we instead propose to learn to predict path quality from crowdsourced human assessments. Since we are interested in a generic task-independent notion of quality, we simply ask human participants to rank paths according to their subjective assessment of the paths' naturalness, without attempting to define naturalness or steering the participants towards particular indicators of quality. We show that a neural network model trained on these assessments is able to predict human judgments on unseen paths with near optimal performance. Most notably, we find that the resulting path selection method is substantially better than the current heuristic approaches at identifying meaningful paths.Comment: In Proceedings of the Web Conference (WWW) 201