Combining Flexible Queries and Knowledge Anchors to facilitate the exploration of Knowledge Graphs

Semantic web and information extraction technologies are enabling the creation of vast information and knowledge repositories, particularly in the form of knowledge graphs comprising entities and the relationships between them. Users are often unfamiliar with the complex structure and vast content of such graphs. Hence, users need to be assisted by tools that support interactive exploration and flexible querying. In this paper we draw on recent work in flexible querying for graph-structured data and identifying good anchors for knowledge graph exploration in order to demonstrate how users can be supported in incrementally querying, exploring and learning from large complex knowledge graphs. We demonstrate our techniques through a case study in the domain of lifelong learning and career guidance

Combining flexible queries and knowledge anchors to facilitate the exploration of knowledge graphs

Semantic web and information extraction technologies are enabling the creation of vast information and knowledge repositories, particularly in the form of knowledge graphs comprising entities and the relationships between them. Users are often unfamiliar with the complex structure and vast content of such graphs. Hence, users need to be assisted by tools that support interactive exploration and flexible querying. In this paper we draw on recent work in flexible querying for graph-structured data and identifying good anchors for knowledge graph exploration in order to demonstrate how users can be supported in incrementally querying, exploring and learning from large complex knowledge graphs. We demonstrate our techniques through a case study in the domain of lifelong learning and career guidance

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

Graph Summarization

The continuous and rapid growth of highly interconnected datasets, which are both voluminous and complex, calls for the development of adequate processing and analytical techniques. One method for condensing and simplifying such datasets is graph summarization. It denotes a series of application-specific algorithms designed to transform graphs into more compact representations while preserving structural patterns, query answers, or specific property distributions. As this problem is common to several areas studying graph topologies, different approaches, such as clustering, compression, sampling, or influence detection, have been proposed, primarily based on statistical and optimization methods. The focus of our chapter is to pinpoint the main graph summarization methods, but especially to focus on the most recent approaches and novel research trends on this topic, not yet covered by previous surveys.Comment: To appear in the Encyclopedia of Big Data Technologie

RDF Querying

Reactive Web systems, Web services, and Web-based publish/ subscribe systems communicate events as XML messages, and in many cases require composite event detection: it is not sufficient to react to single event messages, but events have to be considered in relation to other events that are received over time. Emphasizing language design and formal semantics, we describe the rule-based query language XChangeEQ for detecting composite events. XChangeEQ is designed to completely cover and integrate the four complementary querying dimensions: event data, event composition, temporal relationships, and event accumulation. Semantics are provided as model and fixpoint theories; while this is an established approach for rule languages, it has not been applied for event queries before

Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF