
Message Passing for Complex Question Answering over Knowledge Graphs
Question answering over knowledge graphs (KGQA) has evolved from simple ...
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RBBG_2: Recursive Bipartition of Biconnected Graphs
Given an undirected graph G(V, E), it is well known that partitioning a ...
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Exact Algorithms for Finding WellConnected 2Clubs in RealWorld Graphs: Theory and Experiments
Finding (maximumcardinality) "cliquish" subgraphs is a central topic in...
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Ripple Down Rules for Question Answering
Recent years have witnessed a new trend of building ontologybased quest...
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RDF Knowledge Graph Visualization From a Knowledge Extraction System
In this paper, we present a system to visualize RDF knowledge graphs. Th...
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Discovering Dense Correlated Subgraphs in Dynamic Networks
Given a dynamic network, where edges appear and disappear over time, we ...
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Braininspired Search Engine Assistant based on Knowledge Graph
Search engines can quickly response a hyperlink list according to query ...
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Finding Minimum Connected Subgraphs with Ontology Exploration on Large RDF Data
In this paper, we study the following problem: given a knowledge graph (KG) and a set of input vertices (representing concepts or entities) and edge labels, we aim to find the smallest connected subgraphs containing all of the inputs. This problem plays a key role in KGbased search engines and natural language question answering systems, and it is a natural extension of the Steiner tree problem, which is known to be NPhard. We present RECON, a system for finding approximate answers. RECON aims at achieving high accuracy with instantaneous response (i.e., subsecond/millisecond delay) over KGs with hundreds of millions edges without resorting to expensive computational resources. Furthermore, when no answer exists due to disconnection between concepts and entities, RECON refines the input to a semantically similar one based on the ontology, and attempt to find answers with respect to the refined input. We conduct a comprehensive experimental evaluation of RECON. In particular we compare it with five existing approaches for finding approximate Steiner trees. Our experiments on four large real and synthetic KGs show that RECON significantly outperforms its competitors and incurs a much smaller memory footprint.
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