Knowledge Patterns for the Web: extraction, tranformation and reuse

201 Pages·2014·8.33 MB·English

Alma Mater Studiorum - Universita` di Bologna
DOTTORATO DI RICERCA IN
INFORMATICA
Ciclo XXVI
Settore Concorsuale di afferenza: 01/B1
Settore Scientifico disciplinare: INF01
Knowledge Patterns for the Web:
extraction, tranformation and reuse
Presentata da: Andrea Giovanni Nuzzolese
Coordinatore Dottorato: Relatore:
Maurizio Gabbrielli Paolo Ciancarini
Esame finale anno 2014

To my family.
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Abstract
This thesis aims at investigating methods and software architectures for discovering
what are the typical and frequently occurring structures used for organizing knowl-
edge in the Web. We identify these structures as Knowledge Patterns (KPs), i.e.,
small, well connected units of meaning which are task-based, well-grounded, and
cognitively sound. KPs are an abstraction of frames as introduced by Fillmore [51]
and Minsky [101]. KP discovery needs to address two main research problems: the
heterogeneity of sources, formats and semantics in the Web (i.e., the knowledge
soup problem) and the difficulty to draw relevant boundary around data that al-
lows to capture the meaningful knowledge with respect to a certain context (i.e.,
the knowledge boundary problem). Hence, we introduce two methods that provide
different solutions to these two problems by tackling KP discovery from two differ-
ent perspectives: (i) the transformation of KP-like artifacts (i.e., top-down defined
artifacts that can be compared to KPs, such as FrameNet frames [11] or Ontology
Design Patterns [65]) to KPs formalized as OWL2 ontologies; (ii) the bottom-up
extraction of KPs by analyzing how data are organized in Linked Data. The two
methods address the knowledge soup and boundary problems in different ways. The
first method provides a solution to the two aforementioned problems that is based
on a purely syntactic transformation step of the original source to RDF followed
by a refactoring step whose aim is to add semantics to RDF by select meaningful
RDF triples. The second method allows to draw boundaries around RDF in Linked
Data by analyzing type paths. A type path is a possible route through an RDF that
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takes into account the types associated to the nodes of a path. Unfortunately, type
paths are not always available. In fact, Linked Data is a knowledge soup because of
the heterogeneous semantics of its datasets and because of the limited intentional as
well as extensional coverage of ontologies (e.g., DBpedia ontology 1, YAGO [133]) or
other controlled vocabularies (e.g., SKOS [99], FOAF [28], etc.). Thus, we propose
a solution for enriching Linked Data with additional axioms (e.g., rdf:type ax-
ioms) by exploiting the natural language available for example in annotations (e.g.
rdfs:comment) or in corpora on which datasets in Linked Data are grounded (e.g.
DBpedia is grounded on Wikipedia). Then we present K∼ore, a software architec-
ture conceived to be the basis for developing KP discovery systems and designed
according to two software architectural styles, i.e, the Component-based and REST.
K∼ore is the architectural binding of a set of tools, i.e., K∼tools, which implements
the methods for KP transformation and extraction. Finally we provide an example
of reuse of KP based on Aemoo, an exploratory search tool which exploits KPs for
performing entity summarization.
1http://dbpedia.org/ontology
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Acknowledgements
Now that my Ph.D. is going to an end and this dissertation is finalized it is time
to write acknowledgements. I know that, as it usually happens in writing acknowl-
edgements, I will miss someone whose support has been very important during these
years, but I am sure that they will understand that these acknowledgements are also
for them.
First of all, I would like to thank Pamela for her love that has made my life
marvelous. This achievement is mine and yours as well.
I would like to thank my parents that always and unconditionally endured, sup-
ported and encouraged me in everything.
A big thanks to my brother Paolo who introduced me in Computer Science some
years ago and gave me, together with his wife Erika, my wonderful nephew Aurora.
I would like to express my deep gratitude to my tutors, prof. Aldo Gangemi and
dr. Valentina Presutti, who have involved me in their extraordinary research group
and who have patiently guided and encouraged me during my Ph.D.
I would like to offer my special thanks to my advisor, prof. Paolo Ciancarini, for
his frank, valuable and constructive suggestions and useful critiques to my research
activities.
I wish to acknowledge prof. Paola Mello for having always been ready to discuss
with me about my Ph.D. topics.
Mygratefulthanksarealsoextendedtothereferees, i.e., profEnricoMotta, prof.
Lora Aroyo and prof. Robert Tolksdorf, for their precious and careful comments and
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advises.
I would also like to extend my thanks to all the people that during these years
have been part of my research group, namely, Alberto Musetti, Francesco Draicchio,
Silvio Peroni, Angelo Di Iorio, Enrico Daga, Alessandro Adamou, Eva Blomqvist,
Diego Reforgiato, Sergio Consoli, Daria Spampinato and Stefania Capotosti.
Special thanks go to the people who shared with me this hard but amazing
Ph.D. program, namely, Ornela Dardha, Alexandru Tudor Lascu, Giulio Pellitta,
Francesco Poggi, Roberto Amadini and Gioele Barabucci.
Another big thanks to Michele, Katia and Alfonso for their cheerfulness and the
great fun we have had so far and we will still have.
Last but not least, I would like to thank all my friends that during these years
have been simply friends.
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Contents
Abstract v
Acknowledgements vii
List of Tables xiii
List of Figures xv
List of Publications xxi
1 Introduction 1
2 Background 7
2.1 The Semantic Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Ontologies and Ontology Design Patterns . . . . . . . . . . . . . . . . 12
2.2.1 Ontology Design Patterns . . . . . . . . . . . . . . . . . . . . 13
2.2.2 Pattern-based methodologies . . . . . . . . . . . . . . . . . . . 20
2.3 Ontology Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.4 Knowledge patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
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3 Knowledge Patterns for the Web 27
3.1 A definition for Knowledge Pattern . . . . . . . . . . . . . . . . . . . 27
3.2 Knowledge Patterns in literature . . . . . . . . . . . . . . . . . . . . 29
3.3 Sources of Knowledge Patterns . . . . . . . . . . . . . . . . . . . . . 34
3.3.1 KP-like repositories . . . . . . . . . . . . . . . . . . . . . . . . 35
3.3.2 The Web of Data . . . . . . . . . . . . . . . . . . . . . . . . . 39
4 Knowledge Pattern transformation from KP-like sources 43
4.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.2 A case study: transforming KPs from FrameNet . . . . . . . . . . . . 49
4.2.1 FrameNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.2.2 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.2.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5 Knowledge Pattern extraction from the Web of Data 65
5.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
5.1.1 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.1.2 Boundary induction . . . . . . . . . . . . . . . . . . . . . . . . 69
5.1.3 KP formalization . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.2 A case study: extracting KPs from Wikipedia links . . . . . . . . . . 73
5.2.1 Matherial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.2.2 Obtained results . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.2.3 KP discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.2.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
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