This document discusses knowledge patterns (KPs), which are small well-connected units of meaning that emerge from analyzing structured and unstructured data on the semantic web. It proposes that KPs can be extracted using both top-down and bottom-up approaches, and validated through their emergence from multiple sources. FrameNet frames and Wikipedia links are used as examples for reengineering KPs. The efficacy of KPs is evaluated based on their ability to support exploratory search tasks by filtering and organizing knowledge in a cognitively sound way. An exploratory search application called Aemoo is presented as an example of using KPs to aggregate and explore knowledge from various linked data sources.

1. Towards an Empirical Semantic
Web Science: Knowledge Pattern
Extraction and Usage
Andrea Nuzzolese
Ph.D. Student
Università di Bologna
STLab, ISTC-CNR

2. Outline
• Empirical Semantic Web Science and Knowledge Patterns (KPs)
• A possible methodology for making KPs emerge from the Web of
Data
• The work done so far in KP extraction
• Evaluating KPs' efficacy through Exploratory Search
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3. Does a Web science exist?
• A science usually is applied to clear research objects
✦ Physical and biological science analyzes the natural world, and tries to find
microscopic laws that, extrapolated to the macroscopic realm, would
generate the behavior observed
• The Web is an engineered space created through formally
specified languages and protocols
• Web pages with their content and links are created by humans
with a particular task governed by social conventions and laws
• A Web science exists [Berners-Lee Et Al., 2006] and is oriented
to:
✦ Growth of the engineered space;
✦ Human-web interaction patterns
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4. What about a Web of Data science?
• Linked data offers huge data for empirical research
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5. What are the research objects of the empirical
SW science?
• The Semantic Web and Linked data give us the chance to
empirically study what are the patterns in organizing and
representing knowledge
• The research objects of the Semantic Web as an empirical science
are Knowledge Patterns (KPs)
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6. Knoweldge Patterns
• KPs are small well connected units of meaning, which are
✦ task based
✦ well grounded
✦ cognitively sound
• KPs find their theoretical grounding in frames
✦ “… a frame is a data-structure for representing a stereotyped
situation.” [Minsky 1975]
✦ “...the availability of global patterns of knowledge cuts down on non-determinacy
enough to offset idiosyncratic bottom-up input that might otherwise be
confusing.” [Beaugrande 1980]
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7. An example of KP
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8. Empirical Semantic Web and KPs
• KPs emerge from the knowledge soup deriving from the Web
• A methodology for KP extraction from the Web
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9. KP extraction
• The Web is populated by heterogeneous sources
• We can classify sources in two categories
✦ Formal and semi-formal sources modeled by adopting a top-down approach
✴ e.g., foundational ontologies, frames, thesauri, etc.
✦ Non-formal sources modeled by adopting a bottom-up approach
✴ e.g., RDBs, Linked Data, Web pages, XML documents, etc.
• Our KP extraction methodology is based on two complementary
approaches
✦ A top-down approach
✦ A bottom-up approach
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10. KP boundary
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11. KP detection and discovery
• The top-down approach is aimed to extract KPs that already
exists in a formal or semi-formal structure
✦ Possible techniques: reengineering, refactoring based on association rules,
key concept identification, ontology mapping, etc.
• The bottom-up approach is aimed to extract to discover or detect
KPs from data
✦ Possible techniques: inductive techniques, machine learning, data mining,
ontology mining, etc.
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12. KP validation
• The top-down and the bottom-up approaches concur in the
validation of KPs
• KP extraction is a matter of understanding how the world or
specific domains have been described from different perspectives
✦ The perspective of domain experts, ontologists, etc., which try to give
formalizations either of the world or of specific domains
✦ The perspective of users, data entries, etc, which effectively populate and
manage data that report facts about the world
• For example it would be cognitively relevant if an occurrence of
KP emerges both with the top-down and the bottom-up
approach
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13. KP extraction methodology
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14. KP reengineering from FrameNet’s frames
• FrameNet is a cognitive sound lexical knowledge base, which is
grounded in a large corpus
• FrameNet consists of a set of frames, which have frame elements
lexical units, which pair words (lexemes) to frames, and relations
to corpus elements
✦ Each frame can be interpreted as a class of situations
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15. An example of frame
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16. Using Semion for reengineering and
refactoring FrameNet’s frame
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2&"&(
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17. FrameNet as LOD
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18. FrameNet as KPs
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19. KP discovery from Wikipedia links
• Hypothesis
✦ the types of linked resources that occur most often for a certain type of
resource constitute its KP
✦ since we expect that any cognitive invariance in explaining/describing things
is reflected in the wikilink graph, discovered KPs are cognitively sound
• Contribution
✦ an EKP discovery procedure
✦ 184 EKPs published in OWL2
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20. Collecting paths from wikilinks
dbpedia:
dbpo:Person owl:Thing owl:Thing
Organisation
Path
dbpo: dbpedia:
db:Minnie_Mouse db:The_Walt_Disney_Company Company
FictionalCharacter
dbpo:wikiPageWikiLink Path
rdf:type
dbpo: db:Mickey_Mouse
FictionalCharacter
rdfs:subClassOf
dbpo:
owl:Thing
FictionalCharacter
dbpo:Person
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21. Path popularity
Jackson_5
Dave_Grohl Michael_Jackson
Jackie_Jackson
Nirvana
Madonna
Prince
Charlie_Parker Keith_Jarrett
Foo Fighters Beatles
nSubjectRes(Pi,j)/nRes(Si)
John_Lennon
Paul_McCartney
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22. Boundaries of KPs
• An KP(Si) is a set of paths, such that
Pi,j ∈ KP(Si) ! pathPopularity(Pi,j, Si) ≥ t
• t is a threshold, under which a path is not included in an KP
• How to get a good value for t?
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23. Boundary induction
Step Description
1 For each path, calculate the path popularity
For each subject type, get the 40 top-ranked path popularity
2
values*
Apply multiple correlation (Pearson ρ) between the paths of all
3 subject types by rank, and check for homogeneity of ranks
across subject types
For each of the 40 path popularity ranks, calculate its mean
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across all subject types
5 Apply k-means clustering on the 40 ranks
Decide threshold(s) based on k-means as well as other
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indicators (e.g. FrameNet roles distribution)
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24. Boundary induction
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25. How can be KPs evaluated and used?
• The evaluation of KPs should be performed in terms of their
capability to be cognitively sound in capturing and representing
knowledge
• A scenario that can be used as for evaluating the efficacy of KPs
is the exploratory search combined with user studies.
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26. Why exploratory search?
• Exploratory search is characterized “by uncertainty about the space
being searched and the nature of the problem that motivates the
search” [White Et Al., 2005]
• KPs can be used for supporting exploratory search
✦ They can be used in order to filter knowledge by drawing a meaningful
boundary around the retrieved data
✦ They allow to suggest exploratory paths based on cognitive criteria of
relevance
• We can investigate how KPs help users in exploratory search
tasks
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27. Aemoo: KP-based exploratory search
• A Web application that supports exploratory search on the Web
based on KPs extracted from Wikipedia links
• It aggregates knowledge from Linked Data, Wikipedia, Twitter and
Google News by applying KPs as knowledge lenses over data
• It provides an effective summary of knowledge about an entity,
including explanations
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28. Exploring knowledge with Aemoo (1)
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29. Exploring knowledge with Aemoo (2)
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30. Conclusions
• We want to contribute to the realization of the Semantic Web as
an empirical science by providing a methodology for KP
extraction
• Our methodology for extracting KPs is based on two approaches
✦ a top-down approach
✦ a bottom-up approach
• We have seen our experience in KP extraction so far
✦ KPs from FrameNet’s frames
✦ KPs from Wikipedia links
• The evaluation we have in mind should be performed by means of
exploratory search tasks
✦ Aemoo
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31. Thanks
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