kg-tutorial [at] googlegroups [dot] comThough the phrase “knowledge graph” has been used in the literature since at least 1972 [Schneider, 1973], the modern incarnation of the phrase stems from the 2012 announcement of the Google Knowledge Graph [Singhal, 2012], followed by further announcements of knowledge graphs by Airbnb [Chang, 2018], Amazon [Krishnan, 2018], eBay [Pittman et al., 2017], Facebook [Noy et al., 2019], IBM [Devarajan, 2017], LinkedIn [He et al., 2016], Microsoft [Shrivastava, 2017], Uber [Hamad et al., 2018], and more besides. The growing industrial uptake of the concept proved difficult for academia to ignore: more and more scientific literature is being published on knowledge graphs, which includes books (e.g., [Pan et al., 2017, Qi et al., 2021, Fensel et al., 2020, Kejriwal et al., 2021]), as well as papers outlining definitions (e.g., [Ehrlinger and Wöß, 2016]), novel techniques (e.g., [Pujara et al., 2013, Wang et al., 2014, Lin et al., 2015]), and surveys of specific aspects of knowledge graphs (e.g., [Paulheim, 2017, Wang et al., 2017]).
Underlying all such developments is the core idea of using graphs to represent data, often enhanced with some way to explicitly represent knowledge [Noy et al., 2019]. The result is most often used in application scenarios that involve integrating, managing and extracting value from diverse sources of data at large scale [Noy et al., 2019]. Employing a graph-based abstraction of knowledge has numerous benefits in such settings when compared with, for example, a relational model or NoSQL alternatives. Graphs provide a concise and intuitive abstraction for a variety of domains, where edges capture the (potentially cyclical) relations between the entities inherent in social data, biological interactions, bibliographical citations and co-authorships, transport networks, and so forth [Angles and Gutierrez, 2008]. Graphs allow maintainers to postpone the definition of a schema, allowing the data – and its scope – to evolve in a more flexible manner than typically possible in a relational setting, particularly for capturing incomplete knowledge [Abiteboul, 1997]. Unlike (other) NoSQL models, specialised graph query languages support not only standard relational operators (joins, unions, projections, etc.), but also navigational operators for recursively finding entities connected through arbitrary-length paths [Angles et al., 2017]. Standard knowledge representation formalisms – such as ontologies [Hitzler et al., 2012, Brickley and Guha, 2014, Mungall et al., 2012] and rules [Horrocks et al., 2004, Kifer and Boley, 2013] – can be employed to define and reason about the semantics of the terms used to label and describe the nodes and edges in the graph. Scalable frameworks for graph analytics [Malewicz et al., 2010, Xin et al., 2013a, Stutz et al., 2016] can be leveraged for computing centrality, clustering, summarisation, etc., in order to gain insights about the domain being described. Various representations have also been developed that support applying machine learning techniques both directly and indirectly over graphs [Wang et al., 2017, Wu et al., 2019].
In summary, the decision to build and use a knowledge graph opens up a range of techniques that can be brought to bear for integrating and extracting value from diverse sources of data at large scale. The goal of this book is to motivate and give a comprehensive introduction to knowledge graphs: to describe their foundational data models and how they can be queried; to discuss representations relating to schema, identity, and context; to discuss deductive and inductive ways to make knowledge explicit; to present a variety of techniques that can be used for the creation and enrichment of graph-structured data; to describe how the quality of knowledge graphs can be discerned and how they can be refined; to discuss standards and best practices by which knowledge graphs can be published; and to provide an overview of existing knowledge graphs found in practice. Our intended audience includes researchers and practitioners who are new to knowledge graphs. As such, we do not assume that readers have specific expertise on knowledge graphs.
Knowledge graph. The definition of a “knowledge graph” remains contentious [Ehrlinger and Wöß, 2016, Bonatti et al., 2018, Bergman, 2019], where a number of (sometimes conflicting) definitions have emerged, varying from specific technical proposals to more inclusive general proposals; we address these prior definitions in Appendix A. Herein we adopt an inclusive definition, where we view a knowledge graph as a graph of data intended to accumulate and convey knowledge of the real world, whose nodes represent entities of interest and whose edges represent relations between these entities. The graph of data (aka data graph) conforms to a graph-based data model, which may be a directed edge-labelled graph, a property graph, etc. (we discuss concrete alternatives in Chapter 2). By knowledge, we refer to something that is known. Such knowledge may be accumulated from external sources, or extracted from the knowledge graph itself. Knowledge may be composed of simple statements, such as “Santiago is the capital of Chile”, or quantified statements, such as “all capitals are cities”. Simple statements can be accumulated as edges in the data graph. If the knowledge graph intends to accumulate quantified statements, a more expressive way to represent knowledge – such as ontologies or rules – is required. Deductive methods can then be used to entail and accumulate further knowledge (e.g., “Santiago is a city”). Additional knowledge – based on simple or quantified statements – can also be extracted from and accumulated by the knowledge graph using inductive methods.
Knowledge graphs are often assembled from numerous sources, and as a result, can be highly diverse in terms of structure and granularity. To address this diversity, representations of schema, identity, and context often play a key role, where a schema defines a high-level structure for the knowledge graph, identity denotes which nodes in the graph (or in external sources) refer to the same real-world entity, while context may indicate a specific setting in which some unit of knowledge is held true. As aforementioned, effective methods for extraction, enrichment, quality assessment, and refinement are required for a knowledge graph to grow and improve over time.
In practice. Knowledge graphs aim to serve as an ever-evolving shared substrate of knowledge within an organisation or community [Noy et al., 2019]. We distinguish two types of knowledge graphs in practice: open knowledge graphs and enterprise knowledge graphs. Open knowledge graphs are published online, making their content accessible for the public good. The most prominent examples – DBpedia [Lehmann et al., 2015], Freebase [Bollacker et al., 2007b], Wikidata [Vrandečić and Krötzsch, 2014], YAGO [Hoffart et al., 2011], etc. – cover many domains and are either extracted from Wikipedia [Lehmann et al., 2015, Hoffart et al., 2011], or built by communities of volunteers [Bollacker et al., 2007b, Vrandečić and Krötzsch, 2014]. Open knowledge graphs have also been published within specific domains, such as media [Raimond et al., 2014], government [Hendler et al., 2012, Shadbolt and O'Hara, 2013], geography [Stadler et al., 2012], tourism [Lu et al., 2016, Kärle et al., 2018, Maturana et al., 2018, Zhang et al., 2019], life sciences [Callahan et al., 2013], and more besides. Enterprise knowledge graphs are typically internal to a company and applied for commercial use-cases [Noy et al., 2019]. Prominent industries using enterprise knowledge graphs include Web search (e.g., Bing [Shrivastava, 2017], Google [Singhal, 2012]), commerce (e.g., Airbnb [Chang, 2018], Amazon [Krishnan, 2018, Dong, 2019], eBay [Pittman et al., 2017], Uber [Hamad et al., 2018]), social networks (e.g., Facebook [Noy et al., 2019], LinkedIn [He et al., 2016]), finance (e.g., Accenture [Okorafor and Ray, 2019], Banca d’Italia [Bellomarini et al., 2019], Bloomberg [Meij, 2019], Capital One [Branum and Sehon, 2019], Wells Fargo [Newman, 2019]), among others. Applications include search [Shrivastava, 2017, Singhal, 2012], recommendations [Chang, 2018, Hamad et al., 2018, He et al., 2016, Noy et al., 2019], personal agents [Pittman et al., 2017], advertising [He et al., 2016], business analytics [He et al., 2016], risk assessment [Tobin, 2017, Dalgliesh, 2016], automation [Henson et al., 2019], and more besides. We will provide more details on the use of knowledge graphs in practice in Chapter 10.
Running example. To keep the discussion accessible, throughout the book, we present concrete examples in the context of a hypothetical knowledge graph relating to tourism in Chile (loosely inspired by related use-cases [Kärle et al., 2018, Lu et al., 2016]). The knowledge graph is managed by a tourism board that aims to increase tourism in the country and promote new attractions in strategic areas. The knowledge graph itself will eventually describe tourist attractions, cultural events, services, businesses, travel routes, etc. Some applications the organisation envisages are to:
Outline. The remainder of the book is structured as follows: