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JeffZ.Pan· GuidoVetere
JoseManuelGomez-Perez
HonghanWu Editors
Exploiting
Linked Data
and Knowledge
Graphs in Large
Organizations
Exploiting Linked Data and Knowledge Graphs
in Large Organizations
Jeff Z. Pan
•
Guido Vetere
Jose Manuel Gomez-Perez
Honghan Wu
Editors
Exploiting Linked Data
and Knowledge Graphs
in Large Organizations
123
Editors
Jeff Z. Pan
University of Aberdeen
Aberdeen
UK
Guido Vetere
IBM Italia
Rome
Italy
Jose Manuel Gomez-Perez
iSOCO Lab
Madrid
Spain
Honghan Wu
University of Aberdeen
Aberdeen
UK
ISBN 978-3-319-45652-2 ISBN 978-3-319-45654-6 (eBook)
DOI 10.1007/978-3-319-45654-6
Library of Congress Control Number: 2016949103
© Springer International Publishing Switzerland 2017
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part
of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,
recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission
or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar
methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this
publication does not imply, even in the absence of a specific statement, that such names are exempt from
the relevant protective laws and regulations and therefore free for general use.
The publisher, the authors and the editors are safe to assume that the advice and information in this
book are believed to be true and accurate at the date of publication. Neither the publisher nor the
authors or the editors give a warranty, express or implied, with respect to the material contained herein or
for any errors or omissions that may have been made.
Printed on acid-free paper
This Springer imprint is published by Springer Nature
The registered company is Springer International Publishing AG
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Foreword
When I began my research career as a graduate student at Rensselaer Polytechnic
Institute in 1989, the phrase “knowledge graph” was not in use. The use of graphs,
however, as a notation for “knowledge representation” (KR) was quite common.
CLASSIC, the first real implemented description logic, was just being introduced
from Bell Labs, and although it had a linear syntax, the community was still in the
habit of drawing graphs that depicted the knowledge that was being represented.
This habit traced its history at least as far as M. Ross Quilian ’s work on Semantic
Networks, and subsequent researchers imagined knowledge to be intrinsic in the
design of Artificial Intelligence (AI) systems, universally sketching the role of
knowledge in a graphical form. By the late 1980s the community had more or less
taken up the call for formalisation proposed by Bill Woods and later his student,
Ron Brachman; graph formalisms were perhaps the central focus of AI at the time,
and stayed that way for another decade.
Despite this attention and focus, by the time I moved from academia to industrial
research at IBM’s Watson Research Centre in 2002, the knowledge representation
community had never really solved any problems other than our own. Knowledge
representation and reasoning evolved, or perhaps devolved, into a form of mathe-
matics, in which researchers posed difficult-to-solve puzzles that arose more from
syntactic proper ties of various formalisms than consideration of anyone else’s
actual use cases. Even though we tended to use the words, “semantic” and
“knowledge”, there was nothing particularly semantic about any of it, and indeed
the co-opting by the KR community of terms like semantics, ontology, episte-
mology, etc. to refer to our largely algorithmic work, reliably confused the hell out
of people who actually knew what those terms meant.
In my 12-year career at IBM, I found myself shifting with the times as a
revolution was happening in AI. Many researchers roundly rejected the assumptions
of the KR field, finding the focus on computation rather than data to be problematic.
A new generation of data scientists who wanted to instrument and measure
everything began to take over. I spent a lot of my time at IBM trying to convince
others that the KR technology was useful, and even helping them use it. It was a
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