123 research outputs found
SMART-KG: Hybrid Shipping for SPARQL Querying on the Web
While Linked Data (LD) provides standards for publishing (RDF) and (SPARQL) querying Knowledge Graphs (KGs) on the Web, serving, accessing and processing such open, decentralized KGs is often practically impossible, as query timeouts on publicly available SPARQL endpoints show. Alternative solutions such as Triple Pattern Fragments (TPF) attempt to tackle the problem of availability by pushing query processing workload to the client side, but suffer from unnecessary transfer of irrelevant data on complex queries with large intermediate results. In this paper we present smart-KG, a novel approach to share the load between servers and clients, while significantly reducing data transfer volume, by combining TPF with shipping compressed KG partitions. Our evaluations show that smart-KG outperforms state-of-the-art client-side solutions and increases server-side availability towards more cost-effective and balanced hosting of open and decentralized KGs
Message Passing for Complex Question Answering over Knowledge Graphs
Question answering over knowledge graphs (KGQA) has evolved from simple
single-fact questions to complex questions that require graph traversal and
aggregation. We propose a novel approach for complex KGQA that uses
unsupervised message passing, which propagates confidence scores obtained by
parsing an input question and matching terms in the knowledge graph to a set of
possible answers. First, we identify entity, relationship, and class names
mentioned in a natural language question, and map these to their counterparts
in the graph. Then, the confidence scores of these mappings propagate through
the graph structure to locate the answer entities. Finally, these are
aggregated depending on the identified question type. This approach can be
efficiently implemented as a series of sparse matrix multiplications mimicking
joins over small local subgraphs. Our evaluation results show that the proposed
approach outperforms the state-of-the-art on the LC-QuAD benchmark. Moreover,
we show that the performance of the approach depends only on the quality of the
question interpretation results, i.e., given a correct relevance score
distribution, our approach always produces a correct answer ranking. Our error
analysis reveals correct answers missing from the benchmark dataset and
inconsistencies in the DBpedia knowledge graph. Finally, we provide a
comprehensive evaluation of the proposed approach accompanied with an ablation
study and an error analysis, which showcase the pitfalls for each of the
question answering components in more detail.Comment: Accepted in CIKM 201
Policy specification enforcement and integration
European Network of Excellence, FP7, IST-50677
Extending disjunctive logic programming with functions: Theoretical and algorithmic issues
Estensione dell'Answer Set programming con simboli di funzione, identificando frammenti decidibili e semidecidibili
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