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Fueling the future with 
Semantic Web Patterns 
Valentina Presutti! 
STLab Institute of Cognitive Sciences and Technologie...
Outline 
• Can we implement the original Semantic Web scenario? 
• Knowledge sources heterogeneity problem 
• Semantic ali...
What’s the message? 
Knowledge Patterns are a wormhole in 
the Web to knowledge interpretation and 
understanding 
3
We all want a Personal Assistant Robot! 
Answering our 
Giving opinion questions 
on facts and 
things Providing 
guidelin...
WOODY 
“Pete and Lucy could use their agents to carry 
out all these tasks thanks not to the World Wide 
Web of today but ...
Today is 13 years later 
How would we implement it? 6
Background knowledge 
7
Background knowledge 
We want WOODY to read and understand 
background knowledge and use it in a smart way 
Heterogeneity ...
Heterogeneity 
Syntactic interoperability 
• To unify the format of 
knowledge sources 
enabling e.g. distributed 
query 
...
Semantic interoperability 
• Making sense of distributed 
data 
• Enabling their automatic 
interpretation 
• Different se...
Semantic interoperability 
An ontology is a formal 
specification of a shared 
conceptualisation 
11 
Heterogeneity 
This ...
Semantic interoperability: 
formal specification 
• Shared knowledge 
representation language 
• Semantic interoperability...
Semantic interoperability: 
conceptualisation 
• We have to cope with 
knowledge sources 
conceptualisations 
• Aligning k...
Semantic alignment
Semantic alignment 1+2+3 
• One-by-one alignment 
of classes, properties 
and individuals 
Xianpei Han, Le Sun, Jun Zhao: ...
Semantic alignment 1+2+3 
• Alignment to foundational 
theories, e.g. DOLCE 
• They provide a universal 
reference framewo...
Semantic alignment 1+2+3 
• They provide a decontextualized view on data 
• It is not enough for contextualized interopera...
Imagine we are interested in comparing the governors of California based 
on the laws they created. 
18
Imagine we are interested in comparing the governors of California based 
on the laws they created. 
18 
one-by-one 
one-b...
Imagine we are interested in comparing the governors of California based 
on the laws they created. 
In order to select th...
The boundary problem 
ex:law_dp_CA_2010 rdf:type ex:Law 
ex:law_dp_CA_2010 ex:creator dbpedia:Arnold_Schwarzenegger 
ex:la...
The boundary problem 
ex:law_dp_CA_2010 rdf:type ex:Law 
ex:law_dp_CA_2010 ex:creator dbpedia:Arnold_Schwarzenegger 
ex:la...
Semantic alignment 1+2+3 
• We need interoperability at the level of groups of 
relations that together identify specific ...
Patterns are present in 
the (Semantic) Web 
domain
Administrative 
frames 
Geographic 
frames 
Communication 
22 
frames 
DBpedia
Top-down resources 
• Linguistic resources: FrameNet, 
VerbNet, Corpus Pattern Analysis 
• Ontology Design Patterns 
(Cont...
Knowledge extraction 
methods 
• Entity Linking based on 
key discovery (almost-key 
discovery*) 
• Data/graph mining: 
fr...
KP hypothesis 
Independently of the specific data structure or 
knowledge representation format, certain patterns 
share a...
Three heterogeneous knowledge sources (different data structures, different format), 
but sharing the same intensional mea...
Three heterogeneous knowledge sources (different data structures, different format), 
but sharing the same intensional mea...
Three heterogeneous knowledge sources (different data structures, different format), but 
sharing the same intensional mea...
Three heterogeneous knowledge sources (different data structures, different format), but 
sharing the same intensional mea...
Cognitive foundations of KPs 
• People tend to remember items that fit into a 
schema (cf. Bartlett and a lot of CS from t...
How to represent KPs 
• Class or property punning (with KP description) 
• Property domain/range axiom punning (with KP ro...
Pattern alignment 
30 
Peter Clark’s KP morphisms 
Content Pattern specialisation 
Dedre Gentner’s analogical 
structure m...
Pattern alignment 
31 
Investigating the 
application of similarity 
measures to complex 
structures 
vector spaces, graph...
Pattern alignment 
• Network alignment (cf. 
Roded Sharan*) 
! 
• Modular structure of 
conserved clusters among 
yeast, w...
Some results at STLab 
on KP-based KE
Content Ontology Patterns 
http://www.ontologydesignpatterns.org 
34
Pattern-based Ontology Design 
35 
eXtreme Design 
Including patterns in ontologies 
by design
Schema induction of linked datasets based on patterns. 
Patterns are built around central concepts and used for automatic ...
Encyclopedic Knowledge 
Patterns: example 
• An Encyclopedic Knowledge Pattern (EKP) is discovered from the 
paths emergin...
Using Encyclopedic Knolwedge Patterns for browsing Wikipedia 
Serendipity in exploratory browsing 
http://www.aemoo.org 
A...
KP-based machine reading with FRED 
39 
http://wit.istc.cnr.it/stlab-tools/fred/ 
Valentina Presutti, Francesco Draicchio,...
KP-based machine reading with FRED 
http://wit.istc.cnr.it/stlab-tools/fred/ 
The New York Times reported that John McCart...
Relation discovery and property generation 
http://wit.istc.cnr.it/kore-dev/legalo 
41 
f-measure=.83 
Exploiting event- a...
Overimposing sentic frames on event- and frame-based linked 
data graphs representing opinions, for sentiment analysis 
Se...
Overimposing sentic frames on event- and frame-based linked 
data graphs representing opinions, for sentiment analysis 
Se...
Overimposing sentic frames on event- and frame-based linked 
data graphs representing opinions, for sentiment analysis 
Se...
• Hybridisation is the common factor of these 
methods 
• Still far from solving the pattern alignment problem 
• KP-based...
Back to pattern 
alignment
KP hypothesis 
45 
Independently of the 
specific data structure or 
knowledge representation 
format, certain patterns 
s...
Building a KP distributed system 
Event extraction Events 
46 
Ontology Matching 
Social Network 
Analysis 
Frame detectio...
Knowledge pattern system 
• Inspired by Minsky’s 
frame-systems 
• Statistical methods 
can help to identify 
relations be...
Knowledge pattern system 
• Inspired by Minsky’s 
frame-systems 
• Statistical methods 
can help to identify 
relations be...
A reviewing complaint case 
• Imagine someone gets a paper rejection … 
• … and comments on Facebook …
If we want to enable smart reasoning on 
heterogeneous sources we need a way to relate data 
like this paper’s review with...
KP entailment 
E.g. Patrick Pantel’s “Verb Ocean” 
reject [can-result-in] argue :: 11.634112 
fn:Respond_to_proposal vo:ca...
reject ⊑ Respond_to_proposal argue ⊑ Quarreling 
x ∈ Interlocutor.respond_to_proposal 
y ∈ Speaker.respond_to_proposal 
z ...
However… 
• Automatic methods 
are never 100% 
accurate 
• Regularities can 
emerge for statistical 
significance even if ...
Patterns vs KP 
• A pattern is a motivated structure that is proposed 
by experts or emerges from inductive methods 
• A K...
“Human is the measure of all things.” 
–Protagoras, ~450 B.C. 
54
We need humans in the cycle 
55 
K KP 
K 
K 
K 
K 
K 
Correspondence 
patterns 
Unusual records 
Frames 
Association rules...
We need humans in the cycle 
55 
K KP 
K 
K 
K 
K 
K 
Correspondence 
patterns 
Unusual records 
Frames 
Association rules...
Conclusion 
• We are less than half-way for implementing the original Semantic Web scenario 
• A significant step ahead is...
Special thanks to: 
Aldo Gangemi, Malvina Nissim, Misael Mongiovì, Claudia d’Amato for their help 
and inspiring discussio...
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Fueling the future with Semantic Web patterns - Keynote at WOP2014@ISWC

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I will claim that Semantic Web Patterns can drive the next technological breakthrough: they can be key for providing intelligent applications with sophisticated ways of interpreting data. I will picture scenarios of a possible not so far future in order to support my claim. I will argue that current Semantic Web Patterns are not sufficient for addressing the envisioned requirements, and I will suggest a research direction for fixing the problem, which includes the hybridisation of existing computer science pattern-based approaches, and human computing.

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Fueling the future with Semantic Web patterns - Keynote at WOP2014@ISWC

  1. 1. Fueling the future with Semantic Web Patterns Valentina Presutti! STLab Institute of Cognitive Sciences and Technologies, CNR, Rome (IT)! ! WOP 2014, October 19th, Riva del Garda (IT)!
  2. 2. Outline • Can we implement the original Semantic Web scenario? • Knowledge sources heterogeneity problem • Semantic alignment at pattern level • Knowledge Patterns as key elements • Some STLab results on KP-based knowledge extraction • A possible research direction to pattern alignment 2 • Conclusion
  3. 3. What’s the message? Knowledge Patterns are a wormhole in the Web to knowledge interpretation and understanding 3
  4. 4. We all want a Personal Assistant Robot! Answering our Giving opinion questions on facts and things Providing guidelines for procedures Solving our problems Planning and reminding our schedule WOODY 4
  5. 5. WOODY “Pete and Lucy could use their agents to carry out all these tasks thanks not to the World Wide Web of today but rather the Semantic Web that it will evolve into tomorrow.” –Tim Berners-Lee, James Hendler and Ora Lassila, 2001 5
  6. 6. Today is 13 years later How would we implement it? 6
  7. 7. Background knowledge 7
  8. 8. Background knowledge We want WOODY to read and understand background knowledge and use it in a smart way Heterogeneity ! Structured and Unstructured data Syntactic and Semantic introperability 8
  9. 9. Heterogeneity Syntactic interoperability • To unify the format of knowledge sources enabling e.g. distributed query Tom Heath, Christian Bizer: Linked Data: Evolving the Web into a Global Data Space. Synthesis Lectures on the Semantic Web, Morgan & Claypool Publishers 2011
  10. 10. Semantic interoperability • Making sense of distributed data • Enabling their automatic interpretation • Different semantic perspectives must be addressed 10 Heterogeneity
  11. 11. Semantic interoperability An ontology is a formal specification of a shared conceptualisation 11 Heterogeneity This definition is valid for any Semantic Web knowledge resource
  12. 12. Semantic interoperability: formal specification • Shared knowledge representation language • Semantic interoperability to the extent of its formal semantics 12 rdfs:subClassOf owl:sameAs rdfs:subPropertyOf owl:equivalentProperty owl:equivalentClass
  13. 13. Semantic interoperability: conceptualisation • We have to cope with knowledge sources conceptualisations • Aligning knowledge sources at a conceptual level formal specification 13 knowledge representation cognition conceptualisation
  14. 14. Semantic alignment
  15. 15. Semantic alignment 1+2+3 • One-by-one alignment of classes, properties and individuals Xianpei Han, Le Sun, Jun Zhao: Collective entity linking in web text: a graph-based method, Proceedings of SIGIR 2011, ACM. Euzenat, Jérôme, Shvaiko, Pavel: Ontology Matching 2nd ed. 2013, Springer.
  16. 16. Semantic alignment 1+2+3 • Alignment to foundational theories, e.g. DOLCE • They provide a universal reference framework from which to derive all possible consequences, inferences, errors. • Assumption: foundational theory axioms always hold dul:Agent! dul:NaturalPerson Daniel Oberle et al., DOLCE ergo SUMO: On foundational and domain models in the SmartWeb Integrated Ontology (SWIntO). J. Web Sem. 5(3): 156-174 (2007) Aldo Gangemi, Nicola Guarino, Claudio Masolo, Alessandro Oltramari, Luc Schneider: Sweetening Ontologies with DOLCE. EKAW 2002: 166-181 Prateek Jain et al.: Contextual Ontology Alignment of LOD with an Upper Ontology: A Case Study with Proton Smith B, Rosse C.: The role of foundational relations in the alignment of biomedical ontologies. Stud Health Technol Inform. 2004;107(Pt 1):444-8
  17. 17. Semantic alignment 1+2+3 • They provide a decontextualized view on data • It is not enough for contextualized interoperability: making sense of data for a certain interactive/ cognitive task 17 Alignment one-by-one Alignment to foundational theories
  18. 18. Imagine we are interested in comparing the governors of California based on the laws they created. 18
  19. 19. Imagine we are interested in comparing the governors of California based on the laws they created. 18 one-by-one one-by-one one-by-one one-by-one one-by-one one-by-one
  20. 20. Imagine we are interested in comparing the governors of California based on the laws they created. In order to select the information that are relevant for performing our task we need to extract only those facts that are framed by certain political concepts and relations. 18 one-by-one one-by-one one-by-one one-by-one one-by-one one-by-one
  21. 21. The boundary problem ex:law_dp_CA_2010 rdf:type ex:Law ex:law_dp_CA_2010 ex:creator dbpedia:Arnold_Schwarzenegger ex:law_dp_CA_2010 ex:jurisdiction dbpedia:California ex:law_dp_CA_2010 ex:name ex:drug_policy_CA_2010 ex:law_dp_CA_2010 ex:creationTime ^^xsd:date:2010 ex:law_dp_CA_2010 ex:forbidden “marijuana possession of up to one ounce” lmdb:Terminator rdf:type lmdb:film lmdb:Terminator lmdb:actor dbpedia:Arnold_Schwarzenegger lmdb:Terminator lmdb:date ^^xsd:date:1984 lmdb:Terminator lmdb:directordbpedia:James_Cameron lmdb:Terminator lmdb:sequel dbpedia:Terminator_2 dbpedia:Arnold_Schwarzenegger rdf:type dbpedia-owl:Office_Holder dbpedia:Arnold_Schwarzenegger dbpprop:predecessor dbpedia:Lee_Haney dbpedia:California_foie_gras_law dbpprop:governor dbpedia:Arnold_Schwarzenegger Aldo Gangemi, Valentina Presutti: Towards a pattern science for the Semantic Web. Semantic Web 1(1-2): 61-68 (2010)
  22. 22. The boundary problem ex:law_dp_CA_2010 rdf:type ex:Law ex:law_dp_CA_2010 ex:creator dbpedia:Arnold_Schwarzenegger ex:law_dp_CA_2010 ex:jurisdiction dbpedia:California ex:law_dp_CA_2010 ex:name ex:drug_policy_CA_2010 ex:law_dp_CA_2010 ex:creationTime ^^xsd:date:2010 ex:law_dp_CA_2010 ex:forbidden “marijuana possession of up to one ounce” similar lmdb:Terminator rdf:type lmdb:film lmdb:Terminator lmdb:actor dbpedia:Arnold_Schwarzenegger lmdb:Terminator lmdb:date ^^xsd:date:1984 lmdb:Terminator lmdb:directordbpedia:James_Cameron lmdb:Terminator lmdb:sequel dbpedia:Terminator_2 dbpedia:Arnold_Schwarzenegger rdf:type dbpedia-owl:Office_Holder dbpedia:Arnold_Schwarzenegger dbpprop:predecessor dbpedia:Lee_Haney dbpedia:California_foie_gras_law dbpprop:governor dbpedia:Arnold_Schwarzenegger Aldo Gangemi, Valentina Presutti: Towards a pattern science for the Semantic Web. Semantic Web 1(1-2): 61-68 (2010)
  23. 23. Semantic alignment 1+2+3 • We need interoperability at the level of groups of relations that together identify specific interpretational contexts! • We need local reference theories defining conceptual boundaries -> Knowledge Patterns* 20 *(cf. Gangemi&Presutti, 2010)
  24. 24. Patterns are present in the (Semantic) Web domain
  25. 25. Administrative frames Geographic frames Communication 22 frames DBpedia
  26. 26. Top-down resources • Linguistic resources: FrameNet, VerbNet, Corpus Pattern Analysis • Ontology Design Patterns (Content Patterns) • EarthCube content patterns • Component Library • Cyc micro theories • Data model patterns (David C. Hay) • Infobox templates, microformats 23 All of them define patterns that provide conceptual context for representing data
  27. 27. Knowledge extraction methods • Entity Linking based on key discovery (almost-key discovery*) • Data/graph mining: frequent itemset/ subgraphs, anomalies • NLP: frame detection, event extraction * Danai Symeonidou: Automatic key discovery for Data Linking, PhD Thesis, 2014. 24 They all mine data looking for patterns that allow to make sense of it.
  28. 28. KP hypothesis Independently of the specific data structure or knowledge representation format, certain patterns share a same intensional meaning 25
  29. 29. Three heterogeneous knowledge sources (different data structures, different format), but sharing the same intensional meaning i.e. describing a cooking situation 26
  30. 30. Three heterogeneous knowledge sources (different data structures, different format), but sharing the same intensional meaning i.e. describing a cooking situation 26 Knowledge Pattern
  31. 31. Three heterogeneous knowledge sources (different data structures, different format), but sharing the same intensional meaning i.e. modelling of a cooking situation 27
  32. 32. Three heterogeneous knowledge sources (different data structures, different format), but sharing the same intensional meaning i.e. modelling of a cooking situation 27 Knowledge Pattern
  33. 33. Cognitive foundations of KPs • People tend to remember items that fit into a schema (cf. Bartlett and a lot of CS from then) • In particular, schemas that are associated with some functional similarity (cf. Gibson’s affordances) • Schema similar to (conceptual) frame, script, knowledge pattern 28
  34. 34. How to represent KPs • Class or property punning (with KP description) • Property domain/range axiom punning (with KP roles) • Typed named graphs • OWL ontology modules (cf. ODP) • SPARQL query patterns, SPIN patterns • hasKey patterns 29
  35. 35. Pattern alignment 30 Peter Clark’s KP morphisms Content Pattern specialisation Dedre Gentner’s analogical structure mapping
  36. 36. Pattern alignment 31 Investigating the application of similarity measures to complex structures vector spaces, graph matching, structure matching, etc.
  37. 37. Pattern alignment • Network alignment (cf. Roded Sharan*) ! • Modular structure of conserved clusters among yeast, worm, and fly ! • Multiple network alignment revealed 183 conserved clusters. *Roded Sharan et al.: Conserved patterns of protein interaction in multiple species, Pnas, 2005. 32
  38. 38. Some results at STLab on KP-based KE
  39. 39. Content Ontology Patterns http://www.ontologydesignpatterns.org 34
  40. 40. Pattern-based Ontology Design 35 eXtreme Design Including patterns in ontologies by design
  41. 41. Schema induction of linked datasets based on patterns. Patterns are built around central concepts and used for automatic design of SPARQL queries Centrality discovery in datasets mo:Track mo:track mo:MusicArtist mo:Playlist mo:Torrent tags:taggedWithTag tags:Tag mo:Record foaf:maker mo:image dc:date rdfs:Literal dc:title dc:description mo:available_as mo:available_as mo:available_as Valentina Presutti, Lora Aroyo, Alessandro Adamou, Balthasar Schopman, Aldo Gangemi, Guus Schreiber: Extracting Core Knowledge from Linked Data. COLD2011, CEUR-WS.org Vol-782. 36
  42. 42. Encyclopedic Knowledge Patterns: example • An Encyclopedic Knowledge Pattern (EKP) is discovered from the paths emerging from Wikipedia page link structure • They are represented as OWL2 ontologies Andrea Giovanni Nuzzolese, Aldo Gangemi, Valentina Presutti, Paolo Ciancarini: Encyclopedic Knowledge Patterns from Wikipedia Links. International Semantic Web Conference (1) 2011: 520-536 37
  43. 43. Using Encyclopedic Knolwedge Patterns for browsing Wikipedia Serendipity in exploratory browsing http://www.aemoo.org Andrea Giovanni Nuzzolese, Valentina Presutti, Aldo Gangemi, Alberto Musetti, Paolo Ciancarini: Aemoo: exploring knowledge on the web. WebSci 2013: 272-275 Aemoo: exploratory search based on EKP - Semantic Web Challenge @ISWC 2011 – Short listed, 4th place 38
  44. 44. KP-based machine reading with FRED 39 http://wit.istc.cnr.it/stlab-tools/fred/ Valentina Presutti, Francesco Draicchio, Aldo Gangemi: Knowledge Extraction Based on Discourse Representation Theory and Linguistic Frames. EKAW 2012: 114-129
  45. 45. KP-based machine reading with FRED http://wit.istc.cnr.it/stlab-tools/fred/ The New York Times reported that John McCarthy died. He invented the programming language LISP. From natural language to linked data graphs, which are designed including event- and frame-based patterns 40
  46. 46. Relation discovery and property generation http://wit.istc.cnr.it/kore-dev/legalo 41 f-measure=.83 Exploiting event- and frame-based patterns for relation discovery Valentina Presutti et al. Uncovering the semantics of Wikipedia pagelinks. EKAW 2014.
  47. 47. Overimposing sentic frames on event- and frame-based linked data graphs representing opinions, for sentiment analysis Sentic frames from text http://wit.istc.cnr.it/stlab-tools/sentilo 42
  48. 48. Overimposing sentic frames on event- and frame-based linked data graphs representing opinions, for sentiment analysis Sentic frames from text http://wit.istc.cnr.it/stlab-tools/sentilo 42
  49. 49. Overimposing sentic frames on event- and frame-based linked data graphs representing opinions, for sentiment analysis Sentic frames from text http://wit.istc.cnr.it/stlab-tools/sentilo 42
  50. 50. • Hybridisation is the common factor of these methods • Still far from solving the pattern alignment problem • KP-based design of knowledge sources can support easier procedure for pattern alignment 43
  51. 51. Back to pattern alignment
  52. 52. KP hypothesis 45 Independently of the specific data structure or knowledge representation format, certain patterns share a same intensional meaning
  53. 53. Building a KP distributed system Event extraction Events 46 Ontology Matching Social Network Analysis Frame detection Leveraging different techniques for knowledge extraction Data Mining Graph Mining Rules Correspondence patterns Unusual records Frames Association rules Frequent subgraphs Anomalies Frequent itemset Unifying their results by representing them as KPs KP distributed system The KP system starts with potentially approximate and incomplete patterns and evolves to become more and more robust and accurate thanks to continuous feedback
  54. 54. Knowledge pattern system • Inspired by Minsky’s frame-systems • Statistical methods can help to identify relations between KPs: • co-occurrence, causality, triggering, etc. 47 KPs KPs KPs KPs KPs KPs KPs
  55. 55. Knowledge pattern system • Inspired by Minsky’s frame-systems • Statistical methods can help to identify relations between KPs: • co-occurrence, causality, triggering, etc. 47 KPs KPs KPs KPs KPs KPs KPs
  56. 56. A reviewing complaint case • Imagine someone gets a paper rejection … • … and comments on Facebook …
  57. 57. If we want to enable smart reasoning on heterogeneous sources we need a way to relate data like this paper’s review with this FB status
  58. 58. KP entailment E.g. Patrick Pantel’s “Verb Ocean” reject [can-result-in] argue :: 11.634112 fn:Respond_to_proposal vo:can-result-in fn:Quarreling
  59. 59. reject ⊑ Respond_to_proposal argue ⊑ Quarreling x ∈ Interlocutor.respond_to_proposal y ∈ Speaker.respond_to_proposal z ∈ Proposal.respond_to_proposal k ∈ Arguer1.quarreling m ∈ Arguer2.quarreling n ∈ Issue.quarreling = = ≈ ⊢ reject(r,x,y,z,…) entails argue(s,k,m,n,…)
  60. 60. However… • Automatic methods are never 100% accurate • Regularities can emerge for statistical significance even if they are not relevant • We need procedure and metrics for validating KPs http://tylervigen.com/ 52
  61. 61. Patterns vs KP • A pattern is a motivated structure that is proposed by experts or emerges from inductive methods • A KP formalises the intensional description of a class of situations, events, cases, etc. • When a proposed or emerging pattern is a KP? • Real data are dirty: spurious correlations • How to single out spurious ones?
  62. 62. “Human is the measure of all things.” –Protagoras, ~450 B.C. 54
  63. 63. We need humans in the cycle 55 K KP K K K K K Correspondence patterns Unusual records Frames Association rules Frequent subgraphs Anomalies Frequent itemset Events Ontology Matching Social Network Analysis Frame detection Data Mining Graph Mining Rules Event extraction Crowdsourcing methods
  64. 64. We need humans in the cycle 55 K KP K K K K K Correspondence patterns Unusual records Frames Association rules Frequent subgraphs Anomalies Frequent itemset Events Ontology Matching Social Network Analysis Frame detection Data Mining Graph Mining Rules Event extraction Crowdsourcing methods Marco Fossati, Claudio Giuliano, Sara Tonelli: Outsourcing FrameNet to the Crowd. ACL (2) 2013: 742-747 VideoGames with a purpose applied to semantic tasks http://knowledgeforge.org/, Roberto Navigli
  65. 65. Conclusion • We are less than half-way for implementing the original Semantic Web scenario • A significant step ahead is introducing semantic interoperability at pattern level • This requires the hybridisation of knowledge extraction methods as well as the reconciliation of patterns having different provenance (data mining, graph mining, ontology patterns, etc.) • Knowledge Patterns are key element for enabling such hybridisation • Knowledge Patterns should be organised as a distributed linked system where links are relations enabling smart reasoning • A distributed KP system is a resource evolving by a feeding cycle, which includes human computation 56
  66. 66. Special thanks to: Aldo Gangemi, Malvina Nissim, Misael Mongiovì, Claudia d’Amato for their help and inspiring discussions.

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