Knowledge Patterns:
Design and Extraction
Aldo Gangemi1,2,
joint work with Andrea Giovanni Nuzzolese2,
Valentina Presutti2, Diego Reforgiato Recupero2,3
1LIPN, Paris Nord University, CNRS UMR7030, France
2Semantic Technology Lab, ISTC-CNR, Rome, Italy
3Department of Informatics, University of Cagliari, Italy
aldo.gangemi@lipn.univ-paris13.fr,
{andrea.nuzzolese,valentina.presutti,diego.reforgiato}@istc.cnr.it

Invariances
• “The important things in the world appear as invariants […] of
[…] transformations” (P. Dirac, The principles of quantum
mechanics, 1947)
• “A property or relationship is objective when it is invariant
under the appropriate transformations” (R. Nozick,
Invariances, 2001)
• Multiple presentations (≈ under mapping)
• Multiple stages (≈ under change)
• Multiple contexts / perceivers / interpreters (≈ under different
reference frameworks)

Patterns in general
• “Invariances across observed data or objects”
• They exist in natural, social, cognitive, or abstract worlds
• Mathematical pattern science is about symbols, i.e. non-
interpreted information objects
• Objects of knowledge engineering are interpreted (cognitively,
and, by derivation, formally)
• Mutual support/dependencies
• Gibson (1966), Shepard (1987,1992): invariances in stimulus-
energy pair permanent (“projectable”) properties in the
environment (affordances)
• E.g. colors, shapes, features of entities can constitute value-
added references for behaviour

Knowledge as memory of (value-laden)
observable (ir)regularities?
Cure
Healer
Medication
Patient

1985

At the origins of modern ontologies:
Pat Hayes’ naïve physics manifesto

A Translation Approach to
Portable Ontology
Specifications. T. R. Gruber,
Knowledge Acquisition, 5(2):
199-220, 1993.
15459 citations!!!

CLib Attach component
(Attach has
(superclasses (Action))
(required-slot (object base))
(primary-slot (agent)) )
(every Attach has
(object ((exactly 1 Tangible-Entity) (a Tangible-Entity)))
(base ((exactly 1 Tangible-Entity) (a Tangible-Entity)))
(every Attach has
(preparatory-event ((:default
(a Make-Contact with
(object ((the object of Self)))
(base ((the base of Self))))
(a Detach with
(object ((the object of Self)))
(base ((the base of Self)))) ))))
RCC-8 Spatial Ontology
RCC: a calculus for region based
qualitative spatial reasoning
AG Cohn, B Bennett, JM Gooday, N Gotts -
GeoInformatica, 1997
A library of generic concepts for
composing knowledge bases
K Barker, B Porter, P Clark, 2001

DOLCE (S5) foundational
ontology patterns
Sowa’s Peirce-inspired
top-level ontology
Sweetening ontologies with DOLCE
A Gangemi, N Guarino, C Masolo, A Oltramari, …,
2002 - Springer
Knowledge Representation: Logical,
Philosophical, and Computationa Foundations
J SOWA - Brooks/Cole, 2000

Evidence of knowledge patterns
• In linguistic resources
– Sentence forms
– Sub-categorization frames
– Lexico-syntactic patterns
– Conceptual frames
– Question patterns
– (Bounded sets of) selectional preferences
• In data
– Data patterns
– Data models (xsd, rdb)
– Query types and views
– Microformats
– Infoboxes
• In interaction
– Interaction patterns
– Lenses
– HTML templates
• In semantic resources
– Competency questions
– n-ary relations
– OWL/RDFS classes with (locally complete?)
sets of restrictions or properties
– KM Component Library
– Content ontology design patterns (CPs)
– Knowledge patterns discovered from datasets

ConceptNet
MIT OpenMind common sense project
AtLocation(dog, kennel)[]
CapableOf(dog, bark)[]
CapableOf(dog, guard house)[]
CapableOf(dog, pet)[]
CapableOf(dog, run)[]
Desires(dog, bone)[]
Desires(dog, chew bone)[]
Desires(dog, pet)[]
Desires(dog, play)[]
HasA(dog, flea)[]
HasA(dog, four leg)[]
HasA(dog, fur)[]
HasProperty(dog, loyal)[]
IsA(dog, canine)[]
IsA(dog, domesticate animal)[]
IsA(dog, loyal friend)[]
IsA(dog, mammal)[]
IsA(dog, man best friend)[]
IsA(dog, pet)[]
UsedFor(dog, companionship)[]
ConceptNet—a practical
commonsense reasoning tool-kit.
Liu, Hugo, and Push Singh, BT
technology journal, 2004

http://framenet.icsi.berkeley.edu/
Cure
Healer
Medication
Patient
FrameNet Cure frame
The Berkeley Framenet project. Baker, Fillmore, Lowe, Association for Computational Linguistics, 1998.

VerbNet Motion verb class
VerbNet: A broad-coverage, comprehensive verb lexicon. Schuler, KK, 2005

Knowledge patterns as expertise units
• Evidence that units of expertise are larger than what we have from average linked data triples, or
ontology learning
• Cf. cognitive scientist Dedre Gentner: “uniform relational representation is a hallmark of expertise”
• We need to create expertise-oriented boundaries unifying multiple triples
– “Competency questions” are used to link ontology design patterns to requirements:
•Which objects take part in a certain event?
•Which tasks should be executed in order to achieve a certain goal?
• What’s the function of that artifact?
•What norms are applicable to a certain case?
•What inflammation is active in what body part with what morphology?
– Sometimes exception conditions should be added
– Task-based ontology evaluation can be performed with unit tests against ontologies trying to satisfy
competency questions
Relational language and the development of relational mapping. Loewenstein, J, Gentner, D. Cognitive psychology, 2005

quality, patterns
The role of competency questions
in enterprise engineering
M Grüninger, MS Fox - Benchmarking—Theory
and Practice, 1995 - Springer
Modelling ontology
evaluation and validation
A Gangemi, C Catenacci, M Ciaramita, J
Lehmann - 2006 - Springer
Evaluating ontological
decisions with OntoClean
N Guarino, C Welty - Communications of
the ACM, 2002 - dl.acm.org
Ontology design patterns
A Gangemi, V Presutti - Handbook on
ontologies 2nd ed., 2009 - Springer

Ontology Design Patterns
An ontology design
pattern is a reusable
successful solution to
a recurrent modeling
problem
Visit www.ontologydesignpatterns.org

Maximal ontology design requirement:
What are we talking about, and why?
Generic Competency Questions Specific Modelling Use Case
Who does what, when and where? Production reports, schedules
Which objects take part in a certain event? Resource allocation, biochemical pathways
What are the parts of something? Component schemas, warehouse management
What’s an object made of? Drug and food composition, e.g. for safety (comp.)
What’s the place of something? Geographic systems, resource allocation
What’s the time frame of something? Dynamic knowledge bases
What technique, method, practice is being used? Instructions, enterprise know-how database
Which tasks should be executed in order to achieve a certain goal? Planning, workflow management
Does this behaviour conform to a certain rule? Control systems, legal reasoning services
What’s the function of that artifact? System description
How is that object built? Control systems, quality check
What’s the design of that artifact? Project assistants, catalogues
How did that phenomenon happen? Diagnostic systems, physical models
What’s your role in that transaction? Activity diagrams, planning, organizational models
What that information is about? How is it realized? Information and content modelling, computational models, subject
directories
What argumentation model are you adopting for negotiating an agreement? Cooperation systems
What’s the degree of confidence that you give to this axiom? Ontology engineering tools

Layered pattern morphisms
An ontology design pattern describes a formal expression
that can be exemplified, morphed, instantiated, and expressed in
order to solve a domain modelling problem
• owl:Class:_:x rdfs:subClassOf owl:Restriction:_:y
• Inflammation rdfs:subClassOf (localizedIn some BodyPart)
• Colitis rdfs:subClassOf (localizedIn some Colon)
• John’s_colitis isLocalizedIn John’s_colon
• “John’s colon is inflammated”,“John has got colitis”,“Colitis is the inflammation
of colon”
Logical
Pattern
(MBox)
Generic
Content
Pattern
(TBox)
Specific
Content
Pattern
(TBox)
Data
Pattern
(ABox)
exemplifiedAs morphedAs instantiatedAs Linguistic
Pattern
expressedAs
Logic Meaning Reference Expression
expressedAs
Abstraction
Aldo Gangemi,Valentina Presutti: Ontology Design Patterns. Handbook on Ontologies 2nd ed. (2009)

Problem example:
Temporal n-ary patterns
• Temporal indexing pattern
– (R(a,b))+t sentence indexing
• quads, external time stamps
– R(a,b)+t relation indexing
• reified n-ary relations (3D frames)
– R(a+t,b+t) individual indexing
• fluents, 4D, tropes,“context slices” (4D frames)
– tR name nesting
• ad hoc naming of binary relations
• More indexes for additional arguments
A Multi-dimensional Comparison of Ontology Design Patterns for Representing
n-ary Relations. A Gangemi,V Presutti. SOFSEM 2013: 86-105
An Empirical Perspective on Representing Time. A Scheuermann, E Motta, P
Mulholland, A Gangemi andV Presutti. K-CAP 2013
Formal Unifying Standards for the
Representation of Spatiotemporal
Knowledge.
P. Hayes, Advanced Decision
Architectures Alliance, 2004

Procedural patterns
• Precise
– Classification
– Subsumption
– Inheritance
– Materialization
– Rule firing
– Constructive query
• Approximate
– Fuzzy classification
– Information extraction (NER, RE)
– Similarity induction (e.g. alignment)
– Taxonomy induction
– Relevance detection
– Latent semantic indexing
• Thesaurus to SKOS
• Relational DB to RDF
• WordNet RDB to OWL
• XML to RDF
• FrameNet XML to RDF
• Microformat to RDF
• NER entities to ABox
• NLP to RDF
Reasoning patterns
Alignment patterns
Reengineering patterns

Anti-patterns (1/2)
• Partonomies or subject classifications as subsumption hierarchies
• *City subClassOf Country
• City subClassOf (partOf some Country)
• *City subClassOf Geography
• City broader Geography (e.g. in SKOS)
• Linguistic disjunction as class disjointness
• Dead or alive
• *Dead or Alive
• Dead disjointWith Alive
• Linguistic conjunction as class disjunction
• Pen and paper
• *Pen and Paper
• Pen or Paper | Collection subClassOf (hasMember some Paper ; some Pen)
A catalogue of OWL ontology
antipatterns.
Roussey, Corcho, Vilches-Blázquez,
ACM, 2009.
A user oriented owl development
environment designed to implement
common patterns and minimise common
errors. Horridge, Rector, Drummond,
Springer, 2004.

Anti-patterns (2/2)
• Causality as entailment
• Kaupthing bank behavior caused Iceland crisis
• *KaupthingBankBehavior subClassOf IcelandCrisis
• KaupthingBankBehavior isCauseOf IcelandCrisis
• Expressions as instances of the class representing their meaning
• *dog(word) rdf:type Dog
• dog(word) expresses Dog (with punning)
• Multiple domains or ranges of properties as intersection
• *hasInflammation rdfs:domain Epithelium ; Endothelium
• hasInflammation rdfs:domain (Epithelium or Endothelium)

Putting the pieces
together
“Mine & Design”
pattern induction from data
cleaning data by using (foundational) patterns
pattern-based knowledge extraction from text
axiom induction from knowledge graphs
…

Pattern induction from data:
centrality discovery in datasets
mo:Track
mo:MusicArtist
mo:Playlist
mo:Torrent
mo:ED2K
tags:Tag
mo:Record
foaf:maker
rdfs:Literal
dc:title
dc:datemo:image
dc:description
mo:track
tags:taggedWithTag
mo:available_as
mo:available_as
mo:available_as
Extracting Core Knowledge from
Linked Data.
Presutti, Aroyo et al., COLD2011.

Serving DBpedia with DOLCE–More than Just Adding a Cherry on Top. Paulheim, Gangemi, ISWC, 2015.

Information Extraction and the SW
• Historically SW mainly worked on ontology learning
– unconvincing results: sparseness, core knowledge difficult to
catch, etc. (cf. analyses in Coppola et al. 2009, Blomqvist, 2009)
– natural language understanding known to be an AI-complete
problem
• The paradigm of Open Information Extraction (Etzioni, 2006) fits the
lightweight and/or data-driven trend of current SW
• Semantic technologies need hybridization
31
Google Knowledge Graph
IBM Watson
QA, NL querying on LD,
full text search jointly with
queries, ...
Apple Siri, Google Now, SRI
startup Desti
Facebook Social Graph
Microsoft Cortana
OIE, NELL, BabelNet, ...

Stochasticity does it well?
• Purely stochastic approaches to NLU attempt to
learn models that solve one specific problem, but
how to compose the different models? How to
hybridise those models with logic/knowledge-
based approaches?
• Cf. NCM chatbot, MetaMind neural QA

Google’s “Neural Conversational Model”
one year ago on arXiv
mixed magic and
massive stupidity
in this model
deeply learnt from
open movie scripts

similar vs. opposite semantics,
but algorithm gives same
semantic similarity

We need intelligent
hybridisation!

E.g. how to do deep
semantic parsing?

• The Black Hand might not have decided to
barbarously assassinate Franz Ferdinand after
he arrived in Sarajevo on June 28th, 1914
event
negation
modality
participants
more participants
quality
coreference
deep semantic parsing:
not just annotation, but
formal knowledge extraction
event
relation

Open Information Extraction
pc5: NLPapps mac$ java -Xmx512m -jar reverb-latest.jar <<<"The Black Hand might
not have decided to barbarously assassinate Franz Ferdinand after he arrived in
Sarajevo on June 28th, 1914."
Initializing ReVerb extractor...Done.
Initializing confidence function...Done.
Initializing NLP tools...Done.
Starting extraction.
stdin 1 he arrived in Sarajevo 13 14 14 16 16
10.2200632195721161 The Black Hand might not have decided to barbarously
assassinate Franz Ferdinand after he arrived in Sarajevo on June 28th , 1914 .
DT NNP NNP MD RB VB VBN TO RB VB NNP NNP IN PRP VBD IN NNP IN NNP JJ , CD .
B-NP I-NP I-NP B-VP I-VP I-VP I-VP I-VP I-VP I-VP B-NP I-NP B-SBAR B-NP B-VP
B-PP B-NP B-PP B-NP I-NP I-NP I-NP O he arrive in sarajevo
Done with extraction.
Summary: 1 extractions, 1 sentences, 0 files, 1 seconds
http://ai.cs.washington.edu/projects/
open-information-extraction

Open Knowledge Extraction
• Open Knowledge Extraction (OKE) is a hybrid approach to
knowledge graph production that exploits some of the assumptions
of Open Information Extraction (open-domain, unsupervised),
together with formal semantic reengineering of NLP output,
Semantic Web and Linked Data patterns, entity linking, word-sense
disambiguation linked to Linguistic Linked Data
• The result of OKE is a two-layered OWL-RDF knowledge graph that
(1) lifts the content of a text into entities grounded into public web
identities, with formal axioms, and (2) deeply annotates the text
• OKE can be used as a semantic middleware between content and
knowledge management: querying, annotating, classifying,
detecting, …

LOD and ODP design
Aligned to WordNet, VerbNet,
FrameNet, DOLCE+DnS,
DBpedia, schema.org, BabelNet
RESTful or motif-based
Python query interface
Earmark
RDF, OWL
Apache Stanbol
Neo-Davidsonian, DRT- and Frame-based
High EE and RE accuracy
FRED integrates
NER, SenseTagging, WSD, Taxonomy
Induction, Relation/Event/Role Extraction
NIF
ALCO(D) DL language
http://wit.istc.cnr.it/stlab-tools/fred

OKE w. FRED
“The Black Hand might not have decided to barbarously
assassinate Franz Ferdinand after he arrived in Sarajevo
on June 28th, 1914”
type induction
negation
modality
taxonomy induction
semantic roles
entity linking
+ configurable namespaces,
Earmark text spans with semiotic relations to graph entities (denotes, hasInterpretant),
NIF annotations and text segmentation
events
qualities
tense representation
second order relations
role propagation
predicate-argument structures
coreference resolution

Sample translation table
Parsing pattern Logical pattern Example
Named entity <owl:NamedIndividuali> :BarackObama
Implicit discourse referent <owl:NamedIndividuali> rdf:type <owl:Classj> :doctor_1 rdf:type :Doctor
Entity resolution (NE) <owl:NamedIndividuali> owl:sameAs <owl:NamedIndividualj> :BarackObama owl:sameAs dbr:Barack_Obama
Entity coreference <owl:NamedIndividuali> owl:sameAs <owl:NamedIndividualj> :John owl:sameAs :doctor_1
Term <owl:Classi> || <owl:ObjectPropertyj> || <owl:DatatypePropertyk> :Doctor
Sense tag <owl:NamedIndividuali> rdf:type <owl:Classj> :BarackObama rdf:type dbo:Person
Sense disambiguation <owl:Classi> owl:equivalentClass <owl:Classj> :Doctor owl:equivalentClass n30:synset-doctor-noun-1
Compositional semantics
<owl:Classi> owl:subClassOf <owl:Classj> &&
<owl:NamedIndividuali> dul:associatedWith
<owl:NamedIndividualk>
:BrassInstrument rdfs:subClassOf :Instrument . :brass_1
dul:associatedWith :instrument_1
Extracted (binary) relationship
<owl:NamedIndividuali> <owl:ObjectProperty> ||
<owl:DatatypeProperty> <owl:NamedIndividualj>
:Cabeza :survivorOf :expedition_1
Semantic role
<semrolei> rdf:type (owl:ObjectProperty ||
owl:DatatypeProperty)
vn.role:Agent rdf:type owl:ObjectProperty
Event
<dul:Eventi> <semrolej> <Entityj> . <dul:Eventi> rdf:type
<Event.type> .
:visit_1 vn.role:Agent :doctor_1 . :visit_1 rdf:type :Visit
Frame <Event.typei> owl:subClassOf dul:Event || <ff:Framej>
:Visit rdfs:subClassOf vn.data:Visit_36030100 ||
ff:Visiting
Neo-Davidsonian situation <boxing:Situationi> boxing:involves <owl:NamedIndividualj>
:situation_1 boxing:involves :John , :Dog ;
boxing:hasTruthValue :False
Quantified expression <owl:NamedIndividuali> quant:hasQuantifier <quant:Quantifierj> :doctor_1 quant:hasQuantifier quant:some
Negation <dul:Eventi> boxing:hasTruthValue :False :visit_1 boxing:hasTruthValue :False
Modality <dul:Eventi> boxing:hasModality <boxing:Modalityj> :visit_1 boxing:hasModality :Possible
Regular adjectival semantics <owl:NamedIndividuali> dul:hasQuality <dul:Qualityj> :John dul:hasQuality :Smart
Alternative adjectival
semantics
<owl:Classi> dul:hasQuality <dul:Quality> . <owl:Classi>
dul:associatedWith <owl:Classj>
:AllegedDoctor dul:hasQuality :Alleged ; dul:associatedWith
:Doctor
Disjunction of individuals <owl:NamedIndividuali> boxing:union <owl:NamedIndividualj>
Factual entailment <Eventi> boxing:entails <Eventj>

FRED’s OKE pipeline
Semantic Web Machine Reading with FRED. Gangemi, Presutti, Reforgiato Recupero,
Nuzzolese, et al. Semantic Web Journal, 2016, http://semantic-web-journal.org/system/files/
swj1379.pdf

Semiotic driftin' with OKE:
an example
John Coltrane played with Miles Davis in Kind of Blue

(ROOT
(S
(NP (NNP John) (NNP Coltrane))
(VP (VBD played)
(PP (IN with)
(NP (NNP Miles) (NNP Davis)))
(PP (IN in)
(NP
(NP (NNP Kind))
(PP (IN of)
(NP (NNP Blue))))))))
root(ROOT-0, played-3)
nn(Coltrane-2, John-1)
nsubj(played-3, Coltrane-2)
nn(Davis-6, Miles-5)
prep_with(played-3, Davis-6)
prep_in(played-3, Kind-8)
prep_of(Kind-8, Blue-10)
___________________________ _____________
|x0 x1 x2 x3 | |e4 |
|...........................| |.............|
(|named(x0,john_coltrane,per)|A|play(e4) |)
|named(x1,kind,loc) | |Actor1(e4,x0)|
|named(x2,blue,loc) | |of(x1,x2) |
|named(x3,miles_davis,loc) | |in(e4,x1) |
|___________________________| |Actor2(e4,x3)|
|_____________|
DRT
Dependencies
LLD
(VerbNet)
Entity Linking

<http://dbpedia.org/resource/Kind_of_Blue> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://schema.org/MusicAlbum> .
<http://dbpedia.org/resource/Kind_of_Blue> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://schema.org/CreativeWork> .
<http://www.ontologydesignpatterns.org/ont/fred/domain.owl#Play> <http://www.w3.org/2002/07/owl#equivalentClass> <http://www.ontologydesignpatterns.org/ont/vn/data/Play_36030100> .
<http://www.ontologydesignpatterns.org/ont/fred/domain.owl#Play> <http://www.w3.org/2000/01/rdf-schema#subClassOf> <http://www.ontologydesignpatterns.org/ont/dul/DUL.owl#Event> .
<http://dbpedia.org/resource/John_Coltrane> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://schema.org/MusicGroup> .
<http://dbpedia.org/resource/John_Coltrane> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://schema.org/Person> .
<http://www.ontologydesignpatterns.org/ont/fred/domain.owl#play_1> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.ontologydesignpatterns.org/ont/fred/domain.owl#Play> .
<http://www.ontologydesignpatterns.org/ont/fred/domain.owl#play_1> <http://www.ontologydesignpatterns.org/ont/vn/abox/role/Actor1> <http://www.ontologydesignpatterns.org/ont/fred/domain.owl#John_coltrane> .
<http://www.ontologydesignpatterns.org/ont/fred/domain.owl#play_1> <http://www.ontologydesignpatterns.org/ont/vn/abox/role/Actor2> <http://www.ontologydesignpatterns.org/ont/fred/domain.owl#Miles_davis> .
<http://www.ontologydesignpatterns.org/ont/fred/domain.owl#play_1> <http://www.ontologydesignpatterns.org/ont/fred/domain.owl#locatedIn> <http://www.ontologydesignpatterns.org/ont/fred/domain.owl#Kind> .
<http://dbpedia.org/resource/Miles_Davis> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://schema.org/Person> .
<http://dbpedia.org/resource/Miles_Davis> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://schema.org/MusicGroup> .
<http://www.ontologydesignpatterns.org/ont/fred/domain.owl#Kind> <http://www.w3.org/2002/07/owl#sameAs> <http://dbpedia.org/resource/Kind_of_Blue> .
<http://www.ontologydesignpatterns.org/ont/fred/domain.owl#Kind> <http://www.ontologydesignpatterns.org/ont/fred/domain.owl#of> <http://www.ontologydesignpatterns.org/ont/fred/domain.owl#Blue> .
<http://www.ontologydesignpatterns.org/ont/fred/domain.owl#Miles_davis> <http://www.w3.org/2002/07/owl#sameAs> <http://dbpedia.org/resource/Miles_Davis> .
<http://www.ontologydesignpatterns.org/ont/fred/domain.owl#John_coltrane> <http://www.w3.org/2002/07/owl#sameAs> <http://dbpedia.org/resource/John_Coltrane> .
select ?p where {dbpedia:Kind_of_Blue ?p dbpedia:Miles_Davis}
p
http://dbpedia.org/ontology/artist
http://dbpedia.org/property/writer
Semantic subgraph
Query

<http://dbpedia.org/resource/Kind_of_Blue>
<http://www.w3.org/2000/01/rdf-schema#comment>
"Kind of Blue is a studio album by American jazz musician Miles Davis,
released on August 17, 1959, by Columbia Records. Recording sessions
for the album took place at Columbia's 30th Street Studio in New York City
on March 2 and April 22, 1959. The sessions featured Davis's ensemble
sextet, with pianist Bill Evans, drummer Jimmy Cobb, bassist Paul
Chambers, and saxophonists John Coltrane and Julian Cannonball
Adderley."@en .
describe dbpedia:Kind_of_Blue
<http://dbpedia.org/resource/Kind_of_Blue> <http://dbpedia.org/ontology/producer> <http://dbpedia.org/resource/Irving_Townsend> .
<http://dbpedia.org/resource/Kind_of_Blue> <http://purl.org/dc/terms/subject> <http://dbpedia.org/resource/Category:Albums_certified_gold_by_the_British_Phonographic_Industry> .
<http://dbpedia.org/resource/Kind_of_Blue> <http://dbpedia.org/ontology/recordDate> "1958-05-25+02:00"^^<http://www.w3.org/2001/XMLSchema#date> .
<http://dbpedia.org/resource/Kind_of_Blue> <http://dbpedia.org/ontology/recordedIn> <http://dbpedia.org/resource/CBS_30th_Street_Studio> .
<http://dbpedia.org/resource/Kind_of_Blue> <http://dbpedia.org/ontology/recordedIn> <http://dbpedia.org/resource/New_York_City> .
<http://dbpedia.org/resource/Kind_of_Blue> <http://dbpedia.org/ontology/recordLabel> <http://dbpedia.org/resource/Legacy_Recordings> .
<http://dbpedia.org/resource/Kind_of_Blue> <http://dbpedia.org/ontology/genre> <http://dbpedia.org/resource/Modal_jazz> .
<http://dbpedia.org/resource/Kind_of_Blue> <http://dbpedia.org/property/title> <http://dbpedia.org/resource/Blue_in_Green> .
<http://dbpedia.org/resource/Kind_of_Blue> <http://dbpedia.org/property/producer> <http://dbpedia.org/resource/Irving_Townsend> .
<http://dbpedia.org/resource/Kind_of_Blue> <http://dbpedia.org/property/title> <http://dbpedia.org/resource/All_Blues> .
<http://dbpedia.org/resource/Kind_of_Blue> <http://dbpedia.org/property/title> <http://dbpedia.org/resource/Freddie_Freeloader> .
Entity Linking
again
OKE again

“CBS 30th Street Studio was an American recording studio operated
by Columbia Records, and located at 207 East 30th Street, between
Second and Third Avenues in Manhattan, New York City.”
<http://dbpedia.org/resource/Columbia_Records> <http://www.ontologydesignpatterns.org/ont/fred/
domain.owl#operateBetweenAvenueLocatedIn> <http://dbpedia.org/resource/New_York_City> .
etc. …
Legalo synthetic path finder

Complexity of FRED’s models and
computational time
• typically, a 100-word sentence takes less than 1 second to be processed,
also considering the lag due to the Web API, and the load of a diagram
when using the Web application
• the expressivity of an OKE knowledge graph dataset produced by FRED
calculated from a composite text corpus from four different textual types
(approx. 19,000 axioms) is equivalent to an ALCO(D) (Attributive
Language with Complements, Object value restrictions and Data
properties) DL language
• ALCO(D) includes atomic negation, class intersection, universal and
existential restrictions, and nominals (closed world classes)
• computational complexity of ALCO(D) is PSpace-Complete, and enjoys
both finite model and tree model properties (cf. http://www.cs.man.ac.uk/
~ezolin/dl/ for a complexity navigator)

Challenges for OKE

Four classes of semantic
problems
1. partial accuracy of specific NLP components lead to
global errors
2. “in praesentia” semantics besides literal interpretation:
various kinds of coercion, met* phenomena
3. “in absentia” semantics: implicatures, presuppositions,
tacit knowledge, reference to the physical context
4. higher-order phenomena: emergent frames, social
(and legal) norms, attitudes, argumentation, cultural
frames and narratives, discourse marks, text types

1. Partial accuracy of specific NLP
components lead to global errors
• N/V POS tagging (specially for English)
David Moyes shares Manchester United fans' frustration
• Complex multiword extraction
Myeloid hepatosplenomegaly is an enlargement of liver and kidney due to myelofibrosis.
• Coordinations are difficult
Uncaria est une liane des jungles tropicales de l'Amérique du Sud et Centrale.
Aristotle was a Greek philosopher, a student of Plato and teacher of Alexander the Great.
• Citations and titles need to be treated differently
Anna Karenina is also mentioned in R. L. Stine's Goosebumps series Don't Go To Sleep.
• Plural coreferences are hard
When Carol helps Bob and Bob helps Carol, they can accomplish any task.

• The path descended abruptly
• The road runs along the coast for two hours
• The fence zigzags from the plateau to the valley
• The highway crawls through the city
• The road leads us to Bordeaux
• Need for “type coercion” to satisfy hidden frame
• highway is actually a path that “can be crawled”, therefore the crawling frame here is descriptive
of a state, not of an action
• fence is actually an object whose shape “can be followed by zigzaging”
• road is actually an object that “can be followed as an indication” to our destination
• sometimes an inversion of roles: the path descends because it can be descended
2. “in praesentia” semantics besides literal
interpretation
E.g. fictive motion and coercion (Talmy, Welty, Retoré)

adjective semantics
• Carmelo is a Sicilian surgeon
• Carmelo is an arsonist
• ⊨ Carmelo is a Sicilian arsonist
• Carmelo is a skilful surgeon
• ⊭ Carmelo is a skilful arsonist
• Carmelo is the alleged surgeon
• ⊭ Carmelo is the alleged arsonist
• ?⊨ Carmelo is a surgeon
• Carmelo is a fake surgeon
• ⊭ Carmelo is a fake arsonist
• ⊭ Carmelo is a surgeon

• How many frames? (FrameNet, VerbNet, etc. have a small
coverage), roles are often partly covered, mapping between frame
resources and linguistic constructions is seriously incomplete
• Interaction between in praesentia (traditional machine reading)
and in absentia (SW-machine reading) knowledge is complicated:
how about relevance, novelty, situatedness, etc.?
• Mario, please pass me the glass over there
• Mario, I feel sick … how about the meal we had yesterday?
• Mario, we are in last year’s situation
3. “in absentia” semantics: implicatures,
presuppositions, tacit knowledge, reference to
the physical world

• I saw the Coliseum in my tourist guide and wanted to go there
• artifact vs. place
• Actually relevant?
• Power of ambiguity (“systematic polysemy”)
• Minimal effort seems to count in human evolution of lexical knowledge, but
only if we can easily reconstruct the context (or frame, relation, ...)
• “The communicative function of ambiguity in language” (Piantadosi, Tilyb, Gibson,
@PLoS): ambiguity allows for greater ease of processing by permitting efficient
linguistic units to be re-used. “All efficient communication systems will be ambiguous,
assuming that context is informative about meaning”
• Also in science: inflammation has several interrelated meanings
Dot objects, co-predication
(Pustejovsky, Asher)

4. higher-order phenomena: emergent frames, social
(and legal) norms, attitudes, argumentation, cultural
frames and narratives, discourse marks, text types
• Weak results for automatic extraction of discourse marks and their related semantics
• Attitude/argumentation lenses over basic semantics
• Bhatkal's father: I'm glad he has been arrested
• I disagree with the comments of reviewer 1, but reviewer 2 should provide a stronger
basis to his low rating
• Text types
• A cat is on the mat / A cat is a mammal
• Norms
• I should/feel obliged/want/obey/fear … it’s required/acceptable/convenient/proper/
suggested …
• Complex frames/narratives
• We need tax relief vs. Taxes are investments

Example:
Extracting opinion graphs

Filtering FRED’s graphs with
opinions
People hope that the President will be condemned by the judges
Triggering
event
Main topic
Subtopics
Holder

Sentilo opinion ontology
wit.istc.cnr.it/sentilo-release/sentilo

Result in triples
People hope that the President will be condemned by the judges
Aldo Gangemi, Valentina Presutti, Diego Reforgiato Recupero.
Frame-based detection of opinion holders and topics: a model
and a tool. IEEE Computational Intelligence Magazine, 9(1), 2014

Example:
Extracting adjectival qualities

Approximating adjective
semantics
• New adjective ontology derived by reasoning on
top of an integrated resource including
(Onto)WordNet and FrameNet-RDF
• Four ontology design patterns for the four main
semantics identified
Adjective Semantics in Open Knowledge Extraction. A. Gangemi, A.G. Nuzzolese, V. Presutti and
D. Reforgiato, Formal Ontology in Information Systems Conference (FOIS2106), IOS Press, 2016.

Approximating adjective
semantics: patterns
• Base:
--> create taxonomy and intensional quality
:SkilfulSurgeon rdfs:subClassOf Surgeon .
:surgeon_1 a :SkilfulSurgeon .
:SkilfulSurgeon dul:hasQuality :Skilful .
• Extensional (Base + individual Quality):
--> create taxonomy and individual+intensional quality
:CanadianSurgeon rdfs:subClassOf :Surgeon .
:surgeon_1 a :CanadianSurgeon .
:surgeon_1 dul:hasQuality :Canadian .
:CanadianSurgeon dul:hasQuality :Canadian .
• Modal:
--> create association and intensional modality
:surgeon_1 a :AllegedSurgeon .
:AllegedSurgeon dul:associatedWith :Surgeon .
:AllegedSurgeon boxing:hasModality :Alleged .
• Privative:
--> create association and intensional quality
:surgeon_1 a :Fake_surgeon .
:Fake_surgeon dul:associatedWith :Surgeon .
:Fake_surgeon dul:hasQuality :Fake .

Approximating adjective semantics: example
The alleged doctor failed to transplant the fake organ
into the nice patient that borrowed a Canadian car

Passing the baton
• We have seen knowledge engineering on the SW as kind of
pattern science
• Reusable patterns
• Procedural practices
• Discoverable patterns
• Pattern-based formal knowledge extraction
• How logical and statistical techniques can be formally
hybridised, so leveraging the legacy of Pat Hayes and David
Mumford?

Other useful links
• FRED web application
• http://wit.istc.cnr.it/stlab-tools/fred/demo
• FRED API documentation
• http://wit.istc.cnr.it/stlab-tools/fred/api
• A FRED benchmark in N-Quads
• complete with annotations
• https://www.dropbox.com/s/q6b47dxmwyyseij/goldfrombenchmark.nq?dl=0
• only the semantic subgraph
• https://www.dropbox.com/s/p7w8nojb2g2yf8k/
goldfrombenchmark_semtriples.nq?dl=0