The dynamic nature of web data brings forward the need for maintaining data versions as well as identifying changes between them. In this paper, we deal with problems regarding understanding evolution, focusing on RDF(S) knowledge bases, as RDF is a de-facto standard for representing data on the web. We argue that revisiting past snapshots or the differences between them is not enough for understanding how and why data evolved. Instead, changes should be treated as first-class citizens. In our view, this involves supporting semantically rich, user-defined changes, called complex changes, as well as identifying the relations between them. In this paper, we present our perspective regarding complex changes, formally define a declarative language for defining complex changes on RDF(S) knowledge bases and present how this language is used to detect complex change instances among dataset versions, which can be queried for analyzing evolution. The approach has been extensively evaluated in terms of language expressivity and detection performance on both artificial and real data.
Geospatial extensions of SPARQL, like GeoSPARQL and stSPARQL, have been defined since 2007, and while several geospatial RDF stores have implemented a substantial part of these extensions, other stores limited their support mostly on point geometry features. A parallel process with the above was that RDF frameworks evolved in an interesting way by presenting a more mature set of geospatial features, such as GeoSPARQL support and including the latest indexing technologies. As a logical consequence, a shift in the use of RDF frameworks is to be expected, from base platforms that users extend to create more complete geospatial RDF stores, to attractive finished RDF solutions for many geospatial applications. Alongside with the ever-increasing size of linked geospatial data that semantic stores need to handle, all the above provided our group the motivation to improve our single-node systems benchmark Geographica, originally defined in 2013. Geographica 2 is more comprehensive, because it now includes new geospatial RDF stores and frameworks, big real-world datasets of many hundred million triples with up to 50 million features of complex geometries, new tests and queries that reveal the scalability of these systems. The augmented and revised real-world workload of Geographica 2 tests the efficiency of primitive spatial functions in RDF stores, their performance in the geocoding scenario against the new Census dataset in addition to many other real use case scenarios and finally includes computation of statistics for geospatial datasets. A more detailed and systematic evaluation is performed using the synthetic workload. The new scalability workload aims at discovering the limits of centralized geospatial RDF stores of various architectures. It employs a set of six well-balanced real-world datasets with highly complex geometries covering many European countries and compares three RDF stores in terms of storage space, bulk loading and query response time. In addition, a special version of the benchmark has been created for systems with limited geospatial functionality and two more systems of this category are introduced along the six systems of the main benchmark, all stressed against point-only subsets of the workloads. Three out of the eight systems use an RDBMS for the persistence layer, while some of them offer a variety of persistence options.
A Cyber-Physical System-of-Systems (CPSoS) can be defined as a System-of-Systems (SoS), where its component systems are Cyber-Physical Systems (CPSs) that have been networked together for achieving a certain higher goal. Therefore, a key viability of any CPSoS is the integration of its CPSs to function as a single integrated system to support a common mission. Although such integration can be achieved relying on the exchange of information among CPSs, only few works have highlighted the importance of considering the quality of such information. Without considering Information Quality (IQ) requirements during the design of CPSoS, CPSs will be vulnerable to faults arising from depending on inaccurate, incomplete, inconsistent, and/or outdated information, which may influence the overall dependability, reliability, and performance of the CPSoS. This paper proposes a model-based approach that offers a novel UML profile, named IQCPSoS (Information Quality for Cyber-Physical System-of-Systems), which contains various stereotypes and tagged values for modeling and analyzing IQ requirements for CPSoS. The profile also proposes a set of constraints expressed in the Object Constraint Language (OCL) to be used for the verification of such models. We evaluate our approach by developing a prototype implementation and test its applicability, usability, and validity for modeling and analyzing IQ requirements for a realistic scenario concerning a Tram system.
Knowledge bases allow data organization and exploration, making easier the data semantic understanding and its use by machines. Traditional strategies for knowledge base construction and augmentation have mostly relied on manual effort or automatic extraction of content from structured and semi-structured sources. In this work, we present DeepEx, a system that autonomously extracts missing attributes of entities in knowledge bases from unstructured text. We use Wikipedia as data source. Given entities on Wikipedia represented by their articles (text and infobox), DeepEx uses a classifier to detect sentences in the articles mentioning the possible missing attributes of the entities and then employs a deep-learning extraction model on those sentences to identify the attributes. The sentence classifier and attribute extractor are built with labels automatically produced by a weak supervision approach using infobox structured information as supervision source. We have compared our strategy with previous approaches to this problem on 29 different attributes from 4 domains. The results showed that our extraction pipeline achieved statistically superior performance in comparison with some baselines and variations of our approach.
