SlideShare a Scribd company logo
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

More Related Content

What's hot

Ontology and Ontology Libraries: a critical study
Ontology and Ontology Libraries: a critical studyOntology and Ontology Libraries: a critical study
Ontology and Ontology Libraries: a critical study
Debashisnaskar
 
Exploring Session Context using Distributed Representations of Queries and Re...
Exploring Session Context using Distributed Representations of Queries and Re...Exploring Session Context using Distributed Representations of Queries and Re...
Exploring Session Context using Distributed Representations of Queries and Re...
Bhaskar Mitra
 

What's hot (20)

Word Embedding In IR
Word Embedding In IRWord Embedding In IR
Word Embedding In IR
 
Question Answering - Application and Challenges
Question Answering - Application and ChallengesQuestion Answering - Application and Challenges
Question Answering - Application and Challenges
 
Ontology and its various aspects
Ontology and its various aspectsOntology and its various aspects
Ontology and its various aspects
 
Using Text Embeddings for Information Retrieval
Using Text Embeddings for Information RetrievalUsing Text Embeddings for Information Retrieval
Using Text Embeddings for Information Retrieval
 
Ontology and Ontology Libraries: a Critical Study
Ontology and Ontology Libraries: a Critical StudyOntology and Ontology Libraries: a Critical Study
Ontology and Ontology Libraries: a Critical Study
 
Ontology
OntologyOntology
Ontology
 
On the Semantic Mapping of Schema-agnostic Queries: A Preliminary Study
On the Semantic Mapping of Schema-agnostic Queries: A Preliminary StudyOn the Semantic Mapping of Schema-agnostic Queries: A Preliminary Study
On the Semantic Mapping of Schema-agnostic Queries: A Preliminary Study
 
Ontology and Ontology Libraries: a critical study
Ontology and Ontology Libraries: a critical studyOntology and Ontology Libraries: a critical study
Ontology and Ontology Libraries: a critical study
 
Word Tagging with Foundational Ontology Classes
Word Tagging with Foundational Ontology ClassesWord Tagging with Foundational Ontology Classes
Word Tagging with Foundational Ontology Classes
 
Deep Learning for Information Retrieval: Models, Progress, & Opportunities
Deep Learning for Information Retrieval: Models, Progress, & OpportunitiesDeep Learning for Information Retrieval: Models, Progress, & Opportunities
Deep Learning for Information Retrieval: Models, Progress, & Opportunities
 
Practical machine learning - Part 1
Practical machine learning - Part 1Practical machine learning - Part 1
Practical machine learning - Part 1
 
Exploring Session Context using Distributed Representations of Queries and Re...
Exploring Session Context using Distributed Representations of Queries and Re...Exploring Session Context using Distributed Representations of Queries and Re...
Exploring Session Context using Distributed Representations of Queries and Re...
 
What is word2vec?
What is word2vec?What is word2vec?
What is word2vec?
 
Word representation: SVD, LSA, Word2Vec
Word representation: SVD, LSA, Word2VecWord representation: SVD, LSA, Word2Vec
Word representation: SVD, LSA, Word2Vec
 
Intro to Deep Learning for Question Answering
Intro to Deep Learning for Question AnsweringIntro to Deep Learning for Question Answering
Intro to Deep Learning for Question Answering
 
NLP Project Full Cycle
NLP Project Full CycleNLP Project Full Cycle
NLP Project Full Cycle
 
The Rise of Approximate Ontology Reasoning: Is It Mainstream Yet? --- Revisit...
The Rise of Approximate Ontology Reasoning: Is It Mainstream Yet? --- Revisit...The Rise of Approximate Ontology Reasoning: Is It Mainstream Yet? --- Revisit...
The Rise of Approximate Ontology Reasoning: Is It Mainstream Yet? --- Revisit...
 
Knowledge base system appl. p 1,2-ver1
Knowledge base system appl.  p 1,2-ver1Knowledge base system appl.  p 1,2-ver1
Knowledge base system appl. p 1,2-ver1
 
Detecting and Describing Historical Periods in a Large Corpora
Detecting and Describing Historical Periods in a Large CorporaDetecting and Describing Historical Periods in a Large Corpora
Detecting and Describing Historical Periods in a Large Corpora
 
Deep Learning, an interactive introduction for NLP-ers
Deep Learning, an interactive introduction for NLP-ersDeep Learning, an interactive introduction for NLP-ers
Deep Learning, an interactive introduction for NLP-ers
 

Viewers also liked

Social Media and the Archive. Anthony Browne. BBC Scotland - FIAT/IFTA MMC Se...
Social Media and the Archive. Anthony Browne. BBC Scotland - FIAT/IFTA MMC Se...Social Media and the Archive. Anthony Browne. BBC Scotland - FIAT/IFTA MMC Se...
Social Media and the Archive. Anthony Browne. BBC Scotland - FIAT/IFTA MMC Se...
FIAT/IFTA
 

Viewers also liked (20)

Shifting Scientific Practice - ORCID 2015
Shifting Scientific Practice - ORCID 2015Shifting Scientific Practice - ORCID 2015
Shifting Scientific Practice - ORCID 2015
 
Why We Need Multiple Archives
Why We Need Multiple ArchivesWhy We Need Multiple Archives
Why We Need Multiple Archives
 
The DiNAR Project: Meaningful Mixed Reality for Heritage - Gareth Beale
The DiNAR Project: Meaningful Mixed Reality for Heritage - Gareth BealeThe DiNAR Project: Meaningful Mixed Reality for Heritage - Gareth Beale
The DiNAR Project: Meaningful Mixed Reality for Heritage - Gareth Beale
 
RDA Publishing Workflows
RDA Publishing WorkflowsRDA Publishing Workflows
RDA Publishing Workflows
 
Digital Preservation 2013
Digital Preservation 2013Digital Preservation 2013
Digital Preservation 2013
 
Laura Czerniewicz Open Repositories Conference 2016 Dublin
Laura Czerniewicz Open Repositories Conference 2016 Dublin Laura Czerniewicz Open Repositories Conference 2016 Dublin
Laura Czerniewicz Open Repositories Conference 2016 Dublin
 
[3.8] Archiving and Publishing in Practice Event Logs - Joos Buijs [3TU.Datac...
[3.8] Archiving and Publishing in Practice Event Logs - Joos Buijs [3TU.Datac...[3.8] Archiving and Publishing in Practice Event Logs - Joos Buijs [3TU.Datac...
[3.8] Archiving and Publishing in Practice Event Logs - Joos Buijs [3TU.Datac...
 
2016 07-kdl-interr-infra
2016 07-kdl-interr-infra2016 07-kdl-interr-infra
2016 07-kdl-interr-infra
 
Pedagogy in Public: Open Education Unbound
Pedagogy in Public: Open Education UnboundPedagogy in Public: Open Education Unbound
Pedagogy in Public: Open Education Unbound
 
Securing the future of OA policies - Rob Johnson
Securing the future of OA policies - Rob JohnsonSecuring the future of OA policies - Rob Johnson
Securing the future of OA policies - Rob Johnson
 
Annotating Scholarly Works - the W3C Open Annotation Model
Annotating Scholarly Works - the W3C Open Annotation ModelAnnotating Scholarly Works - the W3C Open Annotation Model
Annotating Scholarly Works - the W3C Open Annotation Model
 
ePADD and Access -- Society of American Archivists (SAA) Annual Meeting, 2015
ePADD and Access -- Society of American Archivists (SAA) Annual Meeting, 2015ePADD and Access -- Society of American Archivists (SAA) Annual Meeting, 2015
ePADD and Access -- Society of American Archivists (SAA) Annual Meeting, 2015
 
Social Media and the Archive. Anthony Browne. BBC Scotland - FIAT/IFTA MMC Se...
Social Media and the Archive. Anthony Browne. BBC Scotland - FIAT/IFTA MMC Se...Social Media and the Archive. Anthony Browne. BBC Scotland - FIAT/IFTA MMC Se...
Social Media and the Archive. Anthony Browne. BBC Scotland - FIAT/IFTA MMC Se...
 
The FP7 Post-Grant Open Access Pilot: An All-Encompassing Gold Open Access Fu...
The FP7 Post-Grant Open Access Pilot: An All-Encompassing Gold Open Access Fu...The FP7 Post-Grant Open Access Pilot: An All-Encompassing Gold Open Access Fu...
The FP7 Post-Grant Open Access Pilot: An All-Encompassing Gold Open Access Fu...
 
Scaling Islandora
Scaling IslandoraScaling Islandora
Scaling Islandora
 
NSW Open Data Challenge: Data Request Service
NSW Open Data Challenge: Data Request ServiceNSW Open Data Challenge: Data Request Service
NSW Open Data Challenge: Data Request Service
 
The Danish Open Access Indicator
The Danish Open Access IndicatorThe Danish Open Access Indicator
The Danish Open Access Indicator
 
Imperial College London - journey to open scholarship
Imperial College London - journey to open scholarshipImperial College London - journey to open scholarship
Imperial College London - journey to open scholarship
 
UCSD / DBMI seminar 2015-02-6
UCSD / DBMI seminar 2015-02-6UCSD / DBMI seminar 2015-02-6
UCSD / DBMI seminar 2015-02-6
 
FIBO & Schema.org
FIBO & Schema.orgFIBO & Schema.org
FIBO & Schema.org
 

Similar to Knowledge Patterns SSSW2016

Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
Semantic Techniques for Enabling Knowledge Reuse in Conceptual ModellingSemantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
Oscar Corcho
 
KMD 1001 Design Brief and Ontology Task
KMD 1001 Design Brief and Ontology TaskKMD 1001 Design Brief and Ontology Task
KMD 1001 Design Brief and Ontology Task
Stian Håklev
 
DBR (Design-Based Research) in mobile learning-Mlearn2013 Doha A_Palalas C_G...
DBR (Design-Based Research) in mobile learning-Mlearn2013 Doha  A_Palalas C_G...DBR (Design-Based Research) in mobile learning-Mlearn2013 Doha  A_Palalas C_G...
DBR (Design-Based Research) in mobile learning-Mlearn2013 Doha A_Palalas C_G...
Agnieszka (Aga) Palalas, Ed.D.
 
E-learning research methodological issues
E-learning research methodological issuesE-learning research methodological issues
E-learning research methodological issues
grainne
 
Qualitative data analysis - Martyn Hammersley
Qualitative data analysis - Martyn HammersleyQualitative data analysis - Martyn Hammersley
Qualitative data analysis - Martyn Hammersley
OUmethods
 

Similar to Knowledge Patterns SSSW2016 (20)

Research methodology (Philosophies and paradigms) in Arabic
Research methodology (Philosophies and paradigms) in ArabicResearch methodology (Philosophies and paradigms) in Arabic
Research methodology (Philosophies and paradigms) in Arabic
 
Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
Semantic Techniques for Enabling Knowledge Reuse in Conceptual ModellingSemantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
Semantic Techniques for Enabling Knowledge Reuse in Conceptual Modelling
 
11 - qualitative research data analysis ( Dr. Abdullah Al-Beraidi - Dr. Ibrah...
11 - qualitative research data analysis ( Dr. Abdullah Al-Beraidi - Dr. Ibrah...11 - qualitative research data analysis ( Dr. Abdullah Al-Beraidi - Dr. Ibrah...
11 - qualitative research data analysis ( Dr. Abdullah Al-Beraidi - Dr. Ibrah...
 
Research 101: Key aspects of a Thesis .
Research 101: Key aspects of a Thesis  .Research 101: Key aspects of a Thesis  .
Research 101: Key aspects of a Thesis .
 
KMD 1001 Design Brief and Ontology Task
KMD 1001 Design Brief and Ontology TaskKMD 1001 Design Brief and Ontology Task
KMD 1001 Design Brief and Ontology Task
 
Design research
Design researchDesign research
Design research
 
Configuring patterns for systemic design - PUARL 2018 conference
Configuring patterns for systemic design - PUARL 2018  conferenceConfiguring patterns for systemic design - PUARL 2018  conference
Configuring patterns for systemic design - PUARL 2018 conference
 
Data as a service: a human-centered design approach/Retha de la Harpe
Data as a service: a human-centered design approach/Retha de la HarpeData as a service: a human-centered design approach/Retha de la Harpe
Data as a service: a human-centered design approach/Retha de la Harpe
 
Whether simulation models that fall under the information systems category ad...
Whether simulation models that fall under the information systems category ad...Whether simulation models that fall under the information systems category ad...
Whether simulation models that fall under the information systems category ad...
 
Qualitative Studies in Software Engineering - Interviews, Observation, Ground...
Qualitative Studies in Software Engineering - Interviews, Observation, Ground...Qualitative Studies in Software Engineering - Interviews, Observation, Ground...
Qualitative Studies in Software Engineering - Interviews, Observation, Ground...
 
Experimenting with eXtreme Design (EKAW2010)
Experimenting with eXtreme Design (EKAW2010)Experimenting with eXtreme Design (EKAW2010)
Experimenting with eXtreme Design (EKAW2010)
 
DBR (Design-Based Research) in mobile learning-Mlearn2013 Doha A_Palalas C_G...
DBR (Design-Based Research) in mobile learning-Mlearn2013 Doha  A_Palalas C_G...DBR (Design-Based Research) in mobile learning-Mlearn2013 Doha  A_Palalas C_G...
DBR (Design-Based Research) in mobile learning-Mlearn2013 Doha A_Palalas C_G...
 
Fueling the future with Semantic Web patterns - Keynote at WOP2014@ISWC
Fueling the future with Semantic Web patterns - Keynote at WOP2014@ISWCFueling the future with Semantic Web patterns - Keynote at WOP2014@ISWC
Fueling the future with Semantic Web patterns - Keynote at WOP2014@ISWC
 
43144 12
43144 1243144 12
43144 12
 
Domain Modeling for Personalized Learning
Domain Modeling for Personalized LearningDomain Modeling for Personalized Learning
Domain Modeling for Personalized Learning
 
E-learning research methodological issues
E-learning research methodological issuesE-learning research methodological issues
E-learning research methodological issues
 
Defense Ates Gursimsek Mutlimodal Semiotics and Collaborative Design
Defense Ates Gursimsek Mutlimodal Semiotics and Collaborative DesignDefense Ates Gursimsek Mutlimodal Semiotics and Collaborative Design
Defense Ates Gursimsek Mutlimodal Semiotics and Collaborative Design
 
Experiments on Pattern-based Ontology Design
Experiments on Pattern-based Ontology DesignExperiments on Pattern-based Ontology Design
Experiments on Pattern-based Ontology Design
 
Qualitative data analysis - Martyn Hammersley
Qualitative data analysis - Martyn HammersleyQualitative data analysis - Martyn Hammersley
Qualitative data analysis - Martyn Hammersley
 
Ontology Mapping
Ontology MappingOntology Mapping
Ontology Mapping
 

Recently uploaded

Aerodynamics. flippatterncn5tm5ttnj6nmnynyppt
Aerodynamics. flippatterncn5tm5ttnj6nmnynypptAerodynamics. flippatterncn5tm5ttnj6nmnynyppt
Aerodynamics. flippatterncn5tm5ttnj6nmnynyppt
sreddyrahul
 
The importance of continents, oceans and plate tectonics for the evolution of...
The importance of continents, oceans and plate tectonics for the evolution of...The importance of continents, oceans and plate tectonics for the evolution of...
The importance of continents, oceans and plate tectonics for the evolution of...
Sérgio Sacani
 
Detectability of Solar Panels as a Technosignature
Detectability of Solar Panels as a TechnosignatureDetectability of Solar Panels as a Technosignature
Detectability of Solar Panels as a Technosignature
Sérgio Sacani
 
The solar dynamo begins near the surface
The solar dynamo begins near the surfaceThe solar dynamo begins near the surface
The solar dynamo begins near the surface
Sérgio Sacani
 
Quantifying Artificial Intelligence and What Comes Next!
Quantifying Artificial Intelligence and What Comes Next!Quantifying Artificial Intelligence and What Comes Next!
Quantifying Artificial Intelligence and What Comes Next!
University of Hertfordshire
 
Exomoons & Exorings with the Habitable Worlds Observatory I: On the Detection...
Exomoons & Exorings with the Habitable Worlds Observatory I: On the Detection...Exomoons & Exorings with the Habitable Worlds Observatory I: On the Detection...
Exomoons & Exorings with the Habitable Worlds Observatory I: On the Detection...
Sérgio Sacani
 
Continuum emission from within the plunging region of black hole discs
Continuum emission from within the plunging region of black hole discsContinuum emission from within the plunging region of black hole discs
Continuum emission from within the plunging region of black hole discs
Sérgio Sacani
 

Recently uploaded (20)

WASP-69b’s Escaping Envelope Is Confined to a Tail Extending at Least 7 Rp
WASP-69b’s Escaping Envelope Is Confined to a Tail Extending at Least 7 RpWASP-69b’s Escaping Envelope Is Confined to a Tail Extending at Least 7 Rp
WASP-69b’s Escaping Envelope Is Confined to a Tail Extending at Least 7 Rp
 
Aerodynamics. flippatterncn5tm5ttnj6nmnynyppt
Aerodynamics. flippatterncn5tm5ttnj6nmnynypptAerodynamics. flippatterncn5tm5ttnj6nmnynyppt
Aerodynamics. flippatterncn5tm5ttnj6nmnynyppt
 
The importance of continents, oceans and plate tectonics for the evolution of...
The importance of continents, oceans and plate tectonics for the evolution of...The importance of continents, oceans and plate tectonics for the evolution of...
The importance of continents, oceans and plate tectonics for the evolution of...
 
Detectability of Solar Panels as a Technosignature
Detectability of Solar Panels as a TechnosignatureDetectability of Solar Panels as a Technosignature
Detectability of Solar Panels as a Technosignature
 
The solar dynamo begins near the surface
The solar dynamo begins near the surfaceThe solar dynamo begins near the surface
The solar dynamo begins near the surface
 
mixotrophy in cyanobacteria: a dual nutritional strategy
mixotrophy in cyanobacteria: a dual nutritional strategymixotrophy in cyanobacteria: a dual nutritional strategy
mixotrophy in cyanobacteria: a dual nutritional strategy
 
Constraints on Neutrino Natal Kicks from Black-Hole Binary VFTS 243
Constraints on Neutrino Natal Kicks from Black-Hole Binary VFTS 243Constraints on Neutrino Natal Kicks from Black-Hole Binary VFTS 243
Constraints on Neutrino Natal Kicks from Black-Hole Binary VFTS 243
 
Triploidy ...............................pptx
Triploidy ...............................pptxTriploidy ...............................pptx
Triploidy ...............................pptx
 
Gliese 12 b, a temperate Earth-sized planet at 12 parsecs discovered with TES...
Gliese 12 b, a temperate Earth-sized planet at 12 parsecs discovered with TES...Gliese 12 b, a temperate Earth-sized planet at 12 parsecs discovered with TES...
Gliese 12 b, a temperate Earth-sized planet at 12 parsecs discovered with TES...
 
KOCH'S POSTULATE: an extensive over view.pptx
KOCH'S POSTULATE: an extensive over view.pptxKOCH'S POSTULATE: an extensive over view.pptx
KOCH'S POSTULATE: an extensive over view.pptx
 
Quantifying Artificial Intelligence and What Comes Next!
Quantifying Artificial Intelligence and What Comes Next!Quantifying Artificial Intelligence and What Comes Next!
Quantifying Artificial Intelligence and What Comes Next!
 
Exomoons & Exorings with the Habitable Worlds Observatory I: On the Detection...
Exomoons & Exorings with the Habitable Worlds Observatory I: On the Detection...Exomoons & Exorings with the Habitable Worlds Observatory I: On the Detection...
Exomoons & Exorings with the Habitable Worlds Observatory I: On the Detection...
 
B lymphocytes, Receptors, Maturation and Activation
B lymphocytes, Receptors, Maturation and ActivationB lymphocytes, Receptors, Maturation and Activation
B lymphocytes, Receptors, Maturation and Activation
 
PLANT DISEASE MANAGEMENT PRINCIPLES AND ITS IMPORTANCE
PLANT DISEASE MANAGEMENT PRINCIPLES AND ITS IMPORTANCEPLANT DISEASE MANAGEMENT PRINCIPLES AND ITS IMPORTANCE
PLANT DISEASE MANAGEMENT PRINCIPLES AND ITS IMPORTANCE
 
GBSN - Microbiology Lab 1 (Microbiology Lab Safety Procedures)
GBSN -  Microbiology Lab  1 (Microbiology Lab Safety Procedures)GBSN -  Microbiology Lab  1 (Microbiology Lab Safety Procedures)
GBSN - Microbiology Lab 1 (Microbiology Lab Safety Procedures)
 
Continuum emission from within the plunging region of black hole discs
Continuum emission from within the plunging region of black hole discsContinuum emission from within the plunging region of black hole discs
Continuum emission from within the plunging region of black hole discs
 
NuGOweek 2024 full programme - hosted by Ghent University
NuGOweek 2024 full programme - hosted by Ghent UniversityNuGOweek 2024 full programme - hosted by Ghent University
NuGOweek 2024 full programme - hosted by Ghent University
 
Application of Mass Spectrometry In Biotechnology
Application of Mass Spectrometry In BiotechnologyApplication of Mass Spectrometry In Biotechnology
Application of Mass Spectrometry In Biotechnology
 
Topography and sediments of the floor of the Bay of Bengal
Topography and sediments of the floor of the Bay of BengalTopography and sediments of the floor of the Bay of Bengal
Topography and sediments of the floor of the Bay of Bengal
 
METHODS OF TRANSCRIPTOME ANALYSIS....pptx
METHODS OF TRANSCRIPTOME ANALYSIS....pptxMETHODS OF TRANSCRIPTOME ANALYSIS....pptx
METHODS OF TRANSCRIPTOME ANALYSIS....pptx
 

Knowledge Patterns SSSW2016

  • 1. 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
  • 2. 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)
  • 3. 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
  • 4. Knowledge as memory of (value-laden) observable (ir)regularities? Cure Healer Medication Patient
  • 6. At the origins of modern ontologies: Pat Hayes’ naïve physics manifesto
  • 7.
  • 8. A Translation Approach to Portable Ontology Specifications. T. R. Gruber, Knowledge Acquisition, 5(2): 199-220, 1993. 15459 citations!!!
  • 9. 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
  • 10. 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
  • 11. 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
  • 12. 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
  • 13. http://framenet.icsi.berkeley.edu/ Cure Healer Medication Patient FrameNet Cure frame The Berkeley Framenet project. Baker, Fillmore, Lowe, Association for Computational Linguistics, 1998.
  • 14. VerbNet Motion verb class VerbNet: A broad-coverage, comprehensive verb lexicon. Schuler, KK, 2005
  • 15. 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
  • 16. 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
  • 17. Ontology Design Patterns An ontology design pattern is a reusable successful solution to a recurrent modeling problem Visit www.ontologydesignpatterns.org
  • 18. 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
  • 19. 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)
  • 20. 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
  • 21. 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
  • 22. 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.
  • 23. 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)
  • 24.
  • 25. 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 …
  • 26. 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.
  • 27. Serving DBpedia with DOLCE–More than Just Adding a Cherry on Top. Paulheim, Gangemi, ISWC, 2015.
  • 28.
  • 29.
  • 30.
  • 31. 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, ...
  • 32.
  • 33. 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
  • 34. Google’s “Neural Conversational Model” one year ago on arXiv mixed magic and massive stupidity in this model deeply learnt from open movie scripts
  • 35. similar vs. opposite semantics, but algorithm gives same semantic similarity
  • 37. E.g. how to do deep semantic parsing?
  • 38. • 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
  • 39. 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
  • 40. 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, …
  • 41. 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
  • 42. 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
  • 43. 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>
  • 44. 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
  • 45. Semiotic driftin' with OKE: an example John Coltrane played with Miles Davis in Kind of Blue
  • 46. (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
  • 47. <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
  • 48. <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
  • 49. “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
  • 50. 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)
  • 52. 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
  • 53. 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.
  • 54. • 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)
  • 55. 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
  • 56. • 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
  • 57. • 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)
  • 58. 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
  • 60. Filtering FRED’s graphs with opinions People hope that the President will be condemned by the judges Triggering event Main topic Subtopics Holder
  • 62. 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
  • 64. 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.
  • 65. 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 .
  • 66. Approximating adjective semantics: example The alleged doctor failed to transplant the fake organ into the nice patient that borrowed a Canadian car
  • 67. 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?
  • 68. 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