Acknowledging the dynamic nature of knowledge graphs, the problem of learning temporal knowledge graph embeddings has recently gained attention. Essentially, the goal is to learn vector representation for the nodes and edges of a knowledge graph taking time into account. These representations must preserve certain properties of the original graph, so as to allow not only classification or clustering tasks, as for classical graph embeddings, but also approximate time-dependent query answering or link predictions over knowledge graphs. For instance, "who was the leader of Germany in 1994?'' or "when was Bonn the capital of Germany?''

Data journalism designates journalistic work inspired by digital data sources. A particularly popular and active area of data journalism is concerned with fact-checking. The term was born in the journalist community and referred the process of verifying and ensuring the accuracy of published media content; since 2012, however, it has increasingly focused on the analysis of politics, economy, science, and news content shared in any form, but first and foremost on the Web (social and otherwise). These trends have been noticed by computer scientists working in the industry and academia. Thus, a very lively area of digital content management research has taken up these problems and works to propose foundations (models), algorithms, and implement them through concrete tools. Our tutorial: (i) Outlines the current state of affairs in the area of digital (or computational) fact-checking in newsrooms, by journalists, NGO workers, scientists and IT companies; (ii) Shows which areas of digital content management research, in particular those relying on the Web, can be leveraged to help fact-checking, and gives a comprehensive survey of efforts in this area; (iii) Highlights ongoing trends, unsolved problems, and areas where we envision future scientific and practical advances.

Nowadays, journalism is facilitated by the existence of large amounts of publicly available digital data sources. In particular, journalists can do investigative work, which typically consists on keyword-based searches over many heterogeneous, independently produced and dynamic data sources, to obtain useful, interconnecting and traceable information. We propose to demonstrate C onnection L ens , a system based on a novel algorithm for keyword search across heterogeneous data sources. Our demonstration scenarios are based on use cases suggested by journalists from the french journal Le Monde, with whom we collaborate.

Fact checking is an essential part of any investigative work. For linguistic, psychological and social reasons, it is an inherently human task. Yet, modern media make it increasingly difficult for experts to keep up with the pace at which information is produced. Hence, we believe there is value in tools to assist them in this process. Much of the effort on Web data research has been focused on coping with incompleteness and uncertainty. Comparatively, dealing with context has received less attention, although it is crucial in judging the validity of a claim. For instance, what holds true in a US state, might not in its neighbors, e.g., due to obsolete or superseded laws. In this work, we address the problem of checking the validity of claims in multiple contexts. We define a language to represent and query facts across different dimensions. The approach is non-intrusive and allows relatively easy modeling, while capturing incompleteness and uncertainty. We describe the syntax and semantics of the language. We present algorithms to demonstrate its feasibility, and we illustrate its usefulness through examples.

Using the Web to assess the validity of claims presents many challenges. Whether the data comes from social networks or established media outlets, individual or institutional data publishers, one has to deal with scale and heterogeneity, as well as with incomplete, imprecise and sometimes outright false information. All of these are closely studied issues. Yet in many situations, the claims under scrutiny, and the data itself, have some inherent context-dependency making them impossible to completely disprove, or evaluate through a simple (e.g. scalar) measure. While data models used on the Web typically deal with universal knowledge, we believe the time has come to put context, such as time or provenance, at the forefront and watch knowledge through multiple lenses. We present BackDrop, an application that enables annotating knowledge and ontologies found online to explore how the veracity of claims varies with context. BackDrop comes in the form of a Web interface, in which users can interactively populate and annotate knowledge bases, and explore under which circumstances certain claims are more or less credible.

Traditional query processing involves a search for plans formed by applying algebraic operators on top of primitives representing access to relations in the input query. But many querying scenarios involve two interacting issues that complicate the search. On the one hand, the search space may be limited by access restrictions associated with the interfaces to datasources, which require certain parameters to be given as inputs. On the other hand, the search space may be extended through the presence of integrity constraints that relate sources to each other, allowing for plans that do not match the structure of the user query. In this paper we present the first optimization approach that attacks both these difficulties within a single framework, presenting a system in which classical cost-based join optimization is extended to support both access-restrictions and constraints. Instead of iteratively exploring subqueries of the input query, our optimizer explores a space of proofs that witness the answering of the query, where each proof has a direct correspondence with a query plan.

The data needed to answer queries is often available through Web-based APIs. Indeed, for a given query there may be many Web-based sources which can be used to answer it, with the sources overlapping in their vocabularies, and differing in their access restrictions (required arguments) and cost. We introduce PDQ (Proof-Driven Query Answering), a system for determining a query plan in the presence of web-based sources. It is: (i) constraint-aware -- exploiting relationships between sources to rewrite an expensive query into a cheaper one, (ii) access-aware -- abiding by any access restrictions known in the sources, and (iii) cost-aware -- making use of any cost information that is available about services. PDQ takes the novel approach of generating query plans from proofs that a query is answerable. We demonstrate the use of PDQ and its effectiveness in generating low-cost plans.

Fact checking and data journalism are currently strong trends. The sheer amount of data at hand makes it difficult even for trained professionals to spot biased, outdated or simply incorrect information. We propose to demonstrate FactMinder, a fact checking and analysis assistance application. SIGMOD attendees will be able to analyze documents using FactMinder and experience how background knowledge and open data repositories help build insightful overviews of current topics.

We consider the setting of a Semantic Web database, containing both explicit data encoded in RDF triples, and implicit data, implied by the RDF semantics. Based on a query workload, we address the problem of selecting a set of views to be materialized in the database, minimizing a combination of query processing, view storage, and view maintenance costs. Starting from an existing relational view selection method, we devise new algorithms for recommending view sets, and show that they scale significantly beyond the existing relational ones when adapted to the RDF context. To account for implicit triples in query answers, we propose a novel RDF query reformulation algorithm and an innovative way of incorporating it into view selection in order to avoid a combinatorial explosion in the complexity of the selection process. The interest of our techniques is demonstrated through a set of experiments.

In recent years, the significant growth of RDF data used in numerous applications has made its efficient and scalable manipulation an important issue. In this paper, we present RDFViewS, a system capable of choosing the most suitable views to materialize, in order to minimize the query response time for a specific SPARQL query workload, while taking into account the view maintenance cost and storage space constraints. Our system employs practical algorithms and heuristics to navigate through the search space of potential view configurations, and exploits the possibly available semantic information - expressed via an RDF Schema - to ensure the completeness of the query evaluation.