4,611 research outputs found
Keyword Search on RDF Graphs - A Query Graph Assembly Approach
Keyword search provides ordinary users an easy-to-use interface for querying
RDF data. Given the input keywords, in this paper, we study how to assemble a
query graph that is to represent user's query intention accurately and
efficiently. Based on the input keywords, we first obtain the elementary query
graph building blocks, such as entity/class vertices and predicate edges. Then,
we formally define the query graph assembly (QGA) problem. Unfortunately, we
prove theoretically that QGA is a NP-complete problem. In order to solve that,
we design some heuristic lower bounds and propose a bipartite graph
matching-based best-first search algorithm. The algorithm's time complexity is
, where is the number of the keywords and is a
tunable parameter, i.e., the maximum number of candidate entity/class vertices
and predicate edges allowed to match each keyword. Although QGA is intractable,
both and are small in practice. Furthermore, the algorithm's time
complexity does not depend on the RDF graph size, which guarantees the good
scalability of our system in large RDF graphs. Experiments on DBpedia and
Freebase confirm the superiority of our system on both effectiveness and
efficiency
Fast Search for Dynamic Multi-Relational Graphs
Acting on time-critical events by processing ever growing social media or
news streams is a major technical challenge. Many of these data sources can be
modeled as multi-relational graphs. Continuous queries or techniques to search
for rare events that typically arise in monitoring applications have been
studied extensively for relational databases. This work is dedicated to answer
the question that emerges naturally: how can we efficiently execute a
continuous query on a dynamic graph? This paper presents an exact subgraph
search algorithm that exploits the temporal characteristics of representative
queries for online news or social media monitoring. The algorithm is based on a
novel data structure called the Subgraph Join Tree (SJ-Tree) that leverages the
structural and semantic characteristics of the underlying multi-relational
graph. The paper concludes with extensive experimentation on several real-world
datasets that demonstrates the validity of this approach.Comment: SIGMOD Workshop on Dynamic Networks Management and Mining (DyNetMM),
201
Reasoning & Querying – State of the Art
Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF
Expanding Database Keyword Search for Database Exploration
AbstractDatabase keyword search (DB KWS) has received a lot of attention in database research community. Although much of the research has been motivated by improving performance, recent research has also paid increased attention to its role in database contents exploration or data mining. In this paper we explore aspects related to DB KWS in two steps: First, we expand DB KWS by incorporating ontologies to better capture users’ intention. Furthermore, we examine how KWS or ontology-enriched KWS can offer useful hints for better understanding of the data and in-depth analysis of the data contents, or data mining
Impliance: A Next Generation Information Management Appliance
ably successful in building a large market and adapting to the changes of the
last three decades, its impact on the broader market of information management
is surprisingly limited. If we were to design an information management system
from scratch, based upon today's requirements and hardware capabilities, would
it look anything like today's database systems?" In this paper, we introduce
Impliance, a next-generation information management system consisting of
hardware and software components integrated to form an easy-to-administer
appliance that can store, retrieve, and analyze all types of structured,
semi-structured, and unstructured information. We first summarize the trends
that will shape information management for the foreseeable future. Those trends
imply three major requirements for Impliance: (1) to be able to store, manage,
and uniformly query all data, not just structured records; (2) to be able to
scale out as the volume of this data grows; and (3) to be simple and robust in
operation. We then describe four key ideas that are uniquely combined in
Impliance to address these requirements, namely the ideas of: (a) integrating
software and off-the-shelf hardware into a generic information appliance; (b)
automatically discovering, organizing, and managing all data - unstructured as
well as structured - in a uniform way; (c) achieving scale-out by exploiting
simple, massive parallel processing, and (d) virtualizing compute and storage
resources to unify, simplify, and streamline the management of Impliance.
Impliance is an ambitious, long-term effort to define simpler, more robust, and
more scalable information systems for tomorrow's enterprises.Comment: This article is published under a Creative Commons License Agreement
(http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute,
display, and perform the work, make derivative works and make commercial use
of the work, but, you must attribute the work to the author and CIDR 2007.
3rd Biennial Conference on Innovative Data Systems Research (CIDR) January
710, 2007, Asilomar, California, US
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