Property Graphs are a versatile and expressive data model that has gained widespread adoption due to their flexibility in supporting labeled and attributed nodes and edges. They are well-established in research communities and are becoming widespread in companies and organizations across various sectors. They have been boosted by a fervent ISO/IEC standardization activity, leading to dedicated query and schema languages. While the current standards are still evolving, opportunities remain to enrich them with features such as composability. The plethora of existing query languages reflects a rich and diverse ecosystem, which ongoing unification efforts aim to align. This tutorial aims to deepen the understanding of Property Graph standards by showcasing their strengths, highlighting recent unification efforts, clarifying the central role of schema constraints, and exploring the rich landscape of research and industrial opportunities shaping the future of graph data management.

Infusing autonomous artificial systems with knowledge about the physical world they inhabit is a critical and long-held aim for the Artificial Intelligence community. Training systems with relevant data is a typical approach; however, finding the data required is not always possible, especially when much of this knowledge is commonsense. In this paper, we present a comparison of topology-based and semantics-based methods for extracting information about object-action and object-state association relations from knowledge graphs, such as ConceptNet, WordNet, ATOMIC, YAGO, WebChild and DBpedia. Moreover, we propose a novel method for extracting information about object-action and object-state associations from knowledge graphs. Our method is composed of a set of techniques for locating, enriching, evaluating, cleaning and exposing knowledge from such resources, relying on semantic similarity methods. Some important aspects of our method are the flexibility in deciding how to deal with the noise that exists in the data, and the capability to determine the importance of a path through training, rather than through manual annotation.

The rapid explosion of linked data has resulted into many weakly structured and incomplete data sources, where typing information might be missing. On the other hand, type information is essential for a number of tasks such as query answering, integration, summarization and partitioning. Existing approaches for type discovery, either completely ignore type declarations available in the dataset (implicit type discovery approaches), or rely only on existing types, in order to complement them (explicit type enrichment approaches). Implicit type discovery approaches are based on instance grouping, which requires an exhaustive comparison between the instances. This process is expensive and not incremental. Explicit type enrichment approaches on the other hand, are not able to identify new types and they can not process data sources that have little or no schema information. In this paper, we present HInT, the first incremental and hybrid type discovery system for RDF datasets, enabling type discovery in datasets where type declarations are missing. To achieve this goal, we incrementally identify the patterns of the various instances, we index and then group them to identify the types. During the processing of an instance, our approach exploits its type information, if available, to improve the quality of the discovered types by guiding the classification of the new instance in the correct group and by refining the groups already built. We analytically and experimentally show that our approach dominates in terms of efficiency, competitors from both worlds, implicit type discovery and explicit type enrichment while outperforming them in most of the cases in terms of quality.

Over the last few years we witness an explosion on the development of data management solutions for big data applications. To this direction NoSQL databases provide new opportunities by enabling elastic scaling, fault tolerance, high availability and schema flexibility. Despite these benefits, their limitations in the flexibility of query mechanisms impose a real barrier for any application that has not predetermined access use-cases. One of the main reasons for this bottleneck is that NoSQL databases do not support joins. In this poster we present a solution that efficiently supports joins over such databases. More specifically, we present a query optimization and execution module placed on top of Cassandra clusters that is able to efficiently combine information stored in different columnfamilies. Our preliminary evaluation demonstrates the feasibility of our solution and the advantages gained when compared to a recent commercial solution by DataStax. To the best of our knowledge our approach is the first and the only available open source solution allowing joins over NoSQL Cassandra databases.

The evolution of ontologies is an undisputed necessity in ontology-based data integration. Yet, few research efforts have focused on addressing the need to reflect ontology evolution onto the underlying data integration systems. We present Exelixis, a web platform that enables query answering over evolving ontologies without mapping redefinition. This is achieved by rewriting queries among ontology versions. First, changes between ontologies are automatically detected and described using a high level language of changes. Those changes are interpreted as sound global-as-view (GAV) mappings. Then query expansion is applied in order to consider constraints from the ontology and unfolding to apply the GAV mappings. Whenever equivalent rewritings cannot be produced we a) guide query redefinition and/or b) provide the best "over-approximations", i.e. the minimally-containing and minimally-generalized rewritings. For the demonstration we will use four versions of the CIDOC-CRM ontology and real user queries to show the functionality of the system. Then we will allow conference participants to directly interact with the system to test its capabilities.

In p2p networks, top-k query processing can provide a lot of advantages both in time and bandwidth consumption. Several algorithms have been proposed for the evaluation of top-k queries. A large percentage of them follow the Threshold Approach. We focus on the main adaptations of threshold algorithms fulfilling the requirements of modern p2p applications. We introduce two algorithms optimized for ranking queries in p2p networks and present their characteristics. In the setting of a simulation of large p2p networks, we evaluate the algorithms' performance. Our experiments demonstrate that in some cases a threshold algorithm can improve top-k query processing, while in others it is far more costly. The results show that distributed query processing can be more effective than a simple threshold algorithm in a p2p network.

This paper focuses on the optimization of the navigation through voluminous subsumption hierarchies of topics employed by Portal Catalogs like Netscape Open Directory (ODP). We advocate for the use of labeling schemes for modeling these hierarchies in order to efficiently answer queries such as subsumption check, descendants, ancestors or nearest common ancestor, which usually require costly transitive closure computations. We first give a qualitative comparison of three main families of schemes, namely bit vector, prefix and interval based schemes. We then show that two labeling schemes are good candidates for an efficient implementation of label querying using standard relational DBMS, namely, the Dewey Prefix scheme [6] and an Interval scheme by Agrawal, Borgida and Jagadish [1]. We compare their storage and query evaluation performance for the 16 ODP hierarchies using the PostgreSQL engine.

Real-scale Semantic Web applications, such as Knowledge Portals and E-Marketplaces, require the management of large volumes of metadata, i.e., information describing the available Web content and services. Better knowledge about their meaning, usage, accessibility or quality will considerably facilitate an automated processing of Web resources. The Resource Description Framework (RDF) enables the creation and exchange of metadata as normal Web data. Although voluminous RDF descriptions are already appearing, sufficiently expressive declarative languages for querying both RDF descriptions and schemas are still missing. In this paper, we propose a new RDF query language called RQL. It is a typed functional language (a la OQL) and relies on a formal model for directed labeled graphs permitting the interpretation of superimposed resource descriptions by means of one or more RDF schemas. RQL adapts the functionality of semistructured/XML query languages to the peculiarities of RDF but, foremost, it enables to uniformly query both resource descriptions and schemas. We illustrate the RQL syntax, semantics and typing system by means of a set of example queries and report on the performance of our persistent RDF Store employed by the RQL interpreter.