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Chapter 1

Enterprise Knowledge Graph:

An Introduction

Jose Manuel Gomez-Perez, Jeff Z. Pan, Guido Vetere and Honghan Wu

A knowledge graph consists of a set of interconnected typed

entities and their attributes .

Compared to other knowledge-oriented information systems, the distinctive fea-

tures of knowledge graphs lie in their special combination of knowledge represen-

tation structures, information management processes and search algorithms. The

term ‘Knowledge Graph’ became well known in 2012 when Google started to use

knowledge graph in their search engine, allowing users to search for things, people

or places, rather than just matching strings in the search queries with those in Web

documents. Inspired by the success story of Google, knowledge graphs are gaining

momentum in the world’s leading information companies.

The idea of a knowledge graph is not completely new though. The original idea

dates back to the knowledge representation technique called the Semantic Network.

Later on, researchers in Knowledge Representation and Reasoning (KR) addressed

J.M. Gomez-Perez (

)

Expert System, Prof. Waksman 10, 28036 Madrid, Spain

e-mail: jmgomez@expertsystem.com

J.Z. Pan

University of Aberdeen, King’s College, Aberdeen AB24 3UE, UK

e-mail: jeff.z.pan@abdn.ac.uk

G. Vetere

IBM Italia, via Sciangai 53, 00144 Rome, Italy

e-mail: gvetere@it.ibm.com

H. Wu

King’s College London, De Crespigny Park, London SE5 8AF, UK

e-mail: honghan.wu@kcl.ac.uk

J.Z. Pan et al. (eds.), Exploiting Linked Data and Knowledge

Graphs in Large Organizations, DOI 10.1007/978-3-319-45654-6_1

2 J.M. Gomez-Perez et al.

some well-known issues on the Semantic Network when standardising the modern

version of Semantic Network, or RDF (Resource Description Frameworks). It turns

out that knowledge representation techniques, such as Knowledge Graph or Semantic

Network, are useful not only for Web search, but also in many other systems and

applications, including enterprise information management. The focus of the book,

therefore, is about constructing, understanding and exploiting knowledge graphs in

large organisations.

The basic unit of a knowledge graph is (the representation of) a singular entity,

such as a football match you are watching, a city you will visit soon or anything you

would like to describe. Each entity might have various attributes. For example, the

attributes of a person include name, birthdate, nationality, etc. Furthermore, entities

are connected to each other by relations; e.g. you follow one of your colleagues

in Twitter. Relations can be used to bridge two separate knowledge graphs. For

example, by saying that your Twitter ID and the ID on your driving license are

denoting one and the same person, this actually interlinks Twitter data with the

information space in the driver licensing agency of your country. Not surprisingly,

each entity needs an identiﬁcation to distinguish one another. This is the ﬁnal jigsaw

in the knowledge representation of knowledge graphs. Note that to facilitate the

interlinking between various knowledge graphs, the entity IDs need to be globally

unique. Types of entities and relations are deﬁned in some machine-understandable

dictionaries called ontologies. The standard ontology language is called OWL (Web

Ontology Language).

The quality of a knowledge graph is crucial for its applications. For example, a

knowledge graph should be consistent. In the above example, it could be the case

that your contact address in your driving license is different than that in your Twitter

proﬁle. To create a knowledge graph connecting these two information spaces, such

inconsistency should be resolved by keeping the correct one. In addition to consis-

tency, one also needs to consider correctness, and coverage of knowledge graphs,

as well as efﬁciency, fault tolerance and scalability of services based on knowledge

graphs. Many of those aspects are related to, among others, the schema (ontology)

of a knowledge graph.

A knowledge graph has an ontology as its schema defining

the vocabulary used in the knowledge graph .

1.1 A Brief History of Knowledge Graph

1.1.1 The Arrival of Semantic Networks

Knowledge management in early human history was largely shaped by oral com-

munication before the invention of languages, which then allowed human knowl-

1 Enterprise Knowledge Graph: An Introduction 3

edge to be recorded and passed on through generations. One of the ﬁrst computer-

based knowledge representation approaches are Semantic Networks, which represent

knowledge in the form of interconnected nodes and arcs, where nodes represent

objects, concepts or situations, and edges represent the relations between them,

including is-a (e.g. “a chair is a type of furniture”) and part-of (e.g. “a seat is part of

a chair”).

As regards the origin of Semantic Networks [38], some researchers argue that

Semantic Networks have come from Charles S. Peirce’s existential graphs, while

many of them pay tribute to Quillian, who was the ﬁrst to introduce Semantic Net-

works in his semantic memory models [194]. Semantic memory refers to general

knowledge (facts, concepts and relationship), such as a chair. It is different from

another kind of long-term memory, i.e. episodic memory, which relates to some

speciﬁc events, such as moving a chair. After Quillian, many variants of Semantic

Networks were proposed.

Compared to formal knowledge representation and reasoning formalisms, such

as predicate logics, Semantic Networks are relatively easy to use and maintain. On

the other hand, they suffer from some limitations. For example, there is no formal

syntax and semantics for Quillian’s Semantic Network. This leaves room for users

to have their own interpretations of constructors in Semantic Networks, such as the

is-a relation. This approach may be seen as ﬂexible for some, but it is also criticised

for making it hard to integrate Semantic Networks while preserving their original

meaning. Furthermore, Semantic Networks do not allow users to deﬁne the meaning

of labels on nodes and arcs.

1.1.2 From Semantic Networks to Linked Data

RDF (Resource Description Framework) is a modern standard from W3C, addressing

some of the issues related to classic Semantic Networks in terms of the lack of

formal syntax and semantics. For example, the is-a relation can be represented by

the subClassOf property in RDF, the semantics of which is clearly deﬁned in the RDF

speciﬁcations. It should be pointed out that RDF does not address all the limitations

of a Semantic Network, e.g. RDF does not allow users to deﬁne concepts either. This

is, however, addressed by OWL (Web Ontology Language), a W3C standard for

deﬁning vocabularies for RDF graphs. In OWL, the part-of relation is not a built-in

relation like the subClassOf property. Instead, it is a user-deﬁned relation that can be

expressed by using the existential constructor. Description Logics [18, 184] are the

underpinning of the OWL standard in the Semantic Web. More details of RDF and

OWL can be found in Chap. 2.

Based on RDF and OWL, Linked Data is a common framework to publish and

share data across different applications and domains, where RDF provides a graph-

based data model to describe objects. OWL offers a standard way of deﬁning vocab-

ularies for data annotations. In the Linked Data paradigm, RDF graphs can be linked

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