Temporal changes in data set the need for the development of knowledge representation techniques able to capture these dynamics. Temporal Knowledge Graphs (TKGs) have emerged as a reference tool to represent dynamic, structured and machine-interpretable knowledge that evolves over time. However, characterizing the evolution of TKGs in a meaningful and human-understandable way remains a significant challenge. In this short paper, we argue that Linear Temporal Logic (LTL), with its expressive temporal constructs, is a promising approach for capturing a wide range of temporal behaviors and high-level evolutionary patterns occurring in TKGs.

Techniques to hide a community from community detection algorithms are emerging as a new way to protect the privacy of users. Existing techniques either adapt optimization criteria derived from community detection (e.g., minimizing instead of maximizing modularity) or define new ones (e.g., community safeness) to identify a set of updates (e.g., edge addition/deletions) that deceive community detection algorithms from recovering the original structure of a target community C. However, all existing approaches do not take into account the fact that network's edges can be weighted to take into account node similarity or relation strength. The goal of this paper is to present SECRETORUM, a novel community deception approach for community deception in weighted networks.

Most of the existing deep-learning-based network analysis techniques focus on the problem of learning low-dimensional node representations. However, networks can also be seen in the light of edges interlinking pairs of nodes. The broad goal of this paper is to introduce a deep-learning framework focused on computing edge-centric network embeddings. We present a novel approach called ECNE, which instead of computing edge embeddings by aggregating node embeddings, computes them directly. ECNE leverages the notion of line graph of a graph coupled with an edge weighting mechanism to preserve the dynamic of the original graph in the line graph. We show that ECNE brings benefits wrt the state-of-the-art.

In this paper, we research the community deception problem. Tackling this problem consists in developing techniques to hide a target community (C) from community detection algorithms. This need emerges whenever a group (e.g., activists, police enforcements, or network participants in general) want to observe and cooperate in a social network while avoiding to be detected. We introduce and formalize the community deception problem and devise an efficient algorithm that allows to achieve deception by identifying a certain number (b) of C's members connections to be rewired. Deception can be practically achieved in social networks like Facebook by friending or unfriending network members as indicated by our algorithm. We compare our approach with another technique based on modularity. By considering a variety of (large) real networks, we provide a systematic evaluation of the robustness of community detection algorithms to deception techniques. Finally, we open some challenging research questions about the design of detection algorithms robust to deception techniques.

The Semantic Web is commonly interpreted under the open-world assumption, meaning that information available (e.g., in a data source) captures only a subset of the reality. Therefore, there is no certainty about whether the available information provides a complete representation of the reality. The broad aim of this article is to contribute a formal study of how to describe the completeness of parts of the Semantic Web stored in RDF data sources. We introduce a theoretical framework allowing augmentation of RDF data sources with statements, also expressed in RDF, about their completeness. One immediate benefit of this framework is that now query answers can be complemented with information about their completeness. We study the impact of completeness statements on the complexity of query answering by considering different fragments of the SPARQL language, including the RDFS entailment regime, and the federated scenario. We implement an efficient method for reasoning about query completeness and provide an experimental evaluation in the presence of large sets of completeness statements.

The management of uncertainty is crucial when harvesting structured content from unstructured and noisy sources. Knowledge Graphs ( kg s), maintaining both numerical and non-numerical facts supported by an underlying schema, are a prominent example. Knowledge Graph management is challenging because: (i) most of existing kg s focus on static data, thus impeding the availability of timewise knowledge; (ii) facts in kg s are usually accompanied by a confidence score, which witnesses how likely it is for them to hold. We demonstrate T e C o R e , a system for temporal inference and conflict resolution in uncertain temporal knowledge graphs ( utkg s). At the heart of T e C o R e are two state-of-the-art probabilistic reasoners that are able to deal with temporal constraints efficiently. While one is scalable, the other can cope with more expressive constraints. The demonstration will focus on enabling users and applications to find inconsistencies in utkg s. T e C o R e provides an interface allowing to select utkg s and editing constraints; shows the maximal consistent subset of the utkg , and displays statistics (e.g., number of noisy facts removed) about the debugging process.

We demonstrate RECAP, a tool that explains relatedness between entities in Knowledge Graphs (KGs) and implements a query by relatedness paradigm that allows to retrieve entities related to those in input. One of the peculiarities of RECAP is that it does not require any data preprocessing and can combine knowledge from multiple KGs. The underlying algorithmic techniques are reduced to the execution of SPARQL queries plus some local refinement. This makes the tool readily available on a large variety of KGs accessible via SPARQL endpoints. To show the general applicability of the tool, we will cover a set of use cases drawn from a variety of knowledge domains (e.g., biology, movies, co-authorship networks) and report on the concrete usage of RECAP in the SENSE4US FP7 project. We will underline the technical aspects of the system and give details on its implementation. The target audience of the demo includes both researchers and practitioners and aims at reporting on the benefits of RECAP in practical knowledge discovery applications.

We demonstrate S+EPPs, a system that provides fast construction of bisimulation summaries using graph analytics platforms, and then enhances existing SPARQL engines to support summary-based exploration and navigational query optimization. The construction component adds a novel optimization to a parallel bisimulation algorithm implemented on a multi-core graph processing framework. We show that for several large, disk resident, real world graphs, full summary construction can be completed in roughly the same time as the data load. The query translation component supports Extended Property Paths (EPPs), an enhancement of SPARQL 1.1 property paths that can express a significantly larger class of navigational queries. EPPs are implemented via rewritings into a widely used SPARQL subset. The optimization component can (transparently to users) translate EPPs defined on instance graphs into EPPs that take advantage of bisimulation summaries. S+EPPs combines the query and optimization translations to enable summary-based optimization of graph traversal queries on top of off-the-shelf SPARQL processors. The demonstration showcases the construction of bisimulation summaries of graphs (ranging from millions to billions of edges), together with the exploration benefits and the navigational query speedups obtained by leveraging summaries stored alongside the original datasets.

The Web of Linked Data is a huge graph of distributed and interlinked datasources fueled by structured information. This new environment calls for formal languages and tools to automatize navigation across datasources (nodes in such graph) and enable semantic-aware and Web-scale search mechanisms. In this article we introduce a declarative navigational language for the Web of Linked Data graph called N auti LOD. N auti LOD enables one to specify datasources via the intertwining of navigation and querying capabilities. It also features a mechanism to specify actions (e.g., send notification messages) that obtain their parameters from datasources reached during the navigation. We provide a formalization of the N auti LOD semantics, which captures both nodes and fragments of the Web of Linked Data. We present algorithms to implement such semantics and study their computational complexity. We discuss an implementation of the features of N auti LOD in a tool called swget, which exploits current Web technologies and protocols. We report on the evaluation of swget and its comparison with related work. Finally, we show the usefulness of capturing Web fragments by providing examples in different knowledge domains.

This paper presents GuLP a graph query language that enables to declaratively express preferences. Preferences enable to order the answers to a query and can be stated in terms of nodes/edge attributes and complex paths. We present the formal syntax and semantics of GuLP and a polynomial time algorithm for evaluating GuLP expressions. We describe an implementation of GuLP in the GuLP-it system, which is available for download. We evaluate the GuLP-it system on real-world and synthetic data.

The massive semantic data sources linked in the Web of Data give new meaning to old features like navigation; introduce new challenges like semantic specification of Web fragments; and make it possible to specify actions relying on semantic data. In this paper we introduce a declarative language to face these challenges. Based on navigational features, it is designed to specify fragments of the Web of Data and actions to be performed based on these data. We implement it in a centralized fashion, and show its power and performance. Finally, we explore the same ideas in a distributed setting, showing their feasibility, potentialities and challenges.