Search

This paper describes Triplex-ST, a novel information extraction system for collecting spatio-temporal information from textual resources. Triplex-ST is based on a distantly supervised approach, which leverages rich linguistic annotations together with information in existing knowledge bases. In particular, we leverage triples associated with temporal and/or spatial contexts, e.g., as available from the YAGO knowledge base, so as to infer templates that capture new facts from previously unseen sentences.

Discovering knowledge from textual sources and subsequently expanding the coverage of knowledge bases like DBpedia or Freebase currently requires either extensive manual work or carefully designed information extractors. Information extractors capture triples from textual sentences. Each triple consists of a subject, a predicate/property, and an object. Triples can be mediated via verbs, nouns, adjectives, and appositions. We propose Triplex, an information extractor that complements previous efforts, concentrating on noun-mediated triples related to nouns, adjectives, and appositions. Triplex automatically constructs templates expressing noun-mediated triples from a bootstrapping set. The bootstrapping set is constructed without manual intervention by creating templates that include syntactic, semantic, and lexical constraints. We report on an automatic evaluation method to examine the output of information extractors both with and without the Triplex approach. Our experimental study indicates that Triplex is a promising approach for extracting noun-mediated triples.

One of the challenges of P2P systems is to perform load balancing efficiently. A distributed hash table (DHT) abstraction, heterogeneous nodes, and non uniform distribution of objects cause load imbalance in structured P2P overlay networks. Several solutions are suggested to solve this problem but they have some restrictions. They assume the homogeneous capabilities of nodes, unawareness of the link latency during transferring load and imposing logical structures to collect and reassign load. This paper presents a distributed load balancing algorithm with topology awareness using the concept of virtual servers. In our proposed approach, each node collects neighborhood load information from physically close nodes and reassigns virtual servers to overlay nodes according to topology of underlying network. Consequently, it provides rapid convergence on load balance and reduces the load transfer cost. Moreover, our parametric algorithm increases the quality of load balancing among close nodes of overlay and also provides a new tradeoff between the quality of load balancing and load transfer cost among all overlay nodes. Our simulations show that our approach reduces the load transfer cost and saves a great network bandwidth.