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.

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.

Although Big RDF management is an emerging topic in the so-called Web of Data, existing techniques disregard the dynamic nature of RDF data. These RDF archives evolve over time and need to be preserved and queried across it. This paper presents v-RDFCSA, an RDF archiving solution that extends RDFCSA (an RDF self-index) to provide version-based queries on top of compressed RDF archives. Our experiments show that v-RDFCSA reduces space requirements up to 35 - 60 times over a state-of-the-art baseline, and gets more than one order of magnitude ahead over it for query resolution.

The Encyclopedia of DNA Elements (ENCODE) is a huge and still expanding public repository of more than 4,000 experiments and 25,000 data files, assembled by a large international consortium since 2007; unknown biological knowledge can be extracted from these huge and largely unexplored data, leading to data-driven genomic, transcriptomic, and epigenomic discoveries. Yet, search of relevant datasets for knowledge discovery is limitedly supported: metadata describing ENCODE datasets are quite simple and incomplete, and not described by a coherent underlying ontology. Here, we show how to overcome this limitation, by adopting an ENCODE metadata searching approach which uses high-quality ontological knowledge and state-of-the-art indexing technologies. Specifically, we developed S.O.S. GeM (http://www.bioinformatics.deib.polimi.it/SOSGeM/), a system supporting effective semantic search and retrieval of ENCODE datasets. First, we constructed a Semantic Knowledge Base by starting with concepts extracted from ENCODE metadata, matched to and expanded on biomedical ontologies integrated in the well-established Unified Medical Language System. We prove that this inference method is sound and complete. Then, we leveraged the Semantic Knowledge Base to semantically search ENCODE data from arbitrary biologists' queries. This allows correctly finding more datasets than those extracted by a purely syntactic search, as supported by the other available systems. We empirically show the relevance of found datasets to the biologists' queries.

The amount of generated RDF data has grown impressively over the last decade, promoting compression as an essential tool for storage and exchange. RDF compression techniques leverage syntactic and semantic redundancies, but structural repetitions are not always addressed effectively. This paper first shows two schema-based sources of redundancy underlying to the schema-relaxed nature of RDF. Then, we revisit the W3C HDT binary format to further compact its graph structure encoding. Our HDT++ approach reduces the original HDT Triples requirements up to 2 times for more structured datasets, and reports significant improvements even for highly semi-structured datasets like DBpedia. In general, HDT++ competes with the current state of the art for structural RDF compression, leading the comparison for three of the four analyzed datasets.

Censuses are one of the most relevant types of statistical data, allowing analyses of the population in terms of demography, economy, sociology, and culture. For fine‐grained analysis, census agencies publish census microdata that consist of a sample of individual records of the census containing detailed anonymous individual information. Working with microdata from different censuses and doing comparative studies are currently difficult tasks due to the diversity of formats and granularities. In this article, we show that novel data processing techniques can be applied to make census microdata interoperable and easy to access and combine. In fact, we demonstrate how Linked Open Data principles, a set of techniques to publish and make connections of (semi‐)structured data on the web, can be fruitfully applied to census microdata. We present a step‐by‐step process to achieve this goal and we study, in theory and practice, two real case studies: the 2001 Spanish census and a general framework for Integrated Public Use Microdata Series (IPUMS‐I).

Summary form only given.The hierarchical Edge-Guided techniques (called E-Gfc) enhance the original E-G approach to support high-order text statistics. These consider the same graph-based model to represent an extended input alphabet obtained by using a variant of the Re-Pair algorithm. E-Gfc adapts the previous coding scheme to grasp the features of the bit-oriented canonical Huffman code chosen as output alphabet.

This paper studies the compressibility of RDF data sets. We show that big RDF data sets are highly compressible due to the structure of RDF graphs (power law), organization of URIs and RDF syntax verbosity. We present basic approaches to compress RDF data and test them with three well-known, real-world RDF data sets.