ontology engineering: a view from the trenches - wop 2015 keynote
TRANSCRIPT
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Ontology Engineering:
A View from the Trenches
WOP 2015 Keynote
Krzysztof JanowiczSTKO Lab, University of California, Santa Barbara, USA
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
The Big Picture
The Big Picture∗
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
The Big Picture
The Even Bigger Picture
Ontology Engineers
Oscar Corcho’s EKAW2014 Keynote: Ontology engineering for and by themasses: are we already there? (http://goo.gl/loYAta)
Ontology
The next 60+ minutes
Ontology Engineering
Pascal Hitzler’s Diversity++ 2015 Keynote: Ontology modeling withdomain experts (see.it/tomorrow/9am)
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Ontology Engineering Challenges
Ontology Engineering
Challengesby Example
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Ontology Reuse
Vocabulary/Ontology Reuse
A typical statement:’Reuse external vocabularies whenever possible.’
<http://dbpedia.org/resource/Copernicus_(lunar_crater)>...
geo:lat"9.7"^^xsd:decimal;
geo:long"20.0"^^xsd:decimal;
...adbpedia-owl:Crater,...ns5:Place,
...
Concerns:Most ontologies are under-specific, the intended meaning or scope remainsunclear, versioning /evolution strategies are unclear, contact persons areoften not available, potential legal issues, lack of proper documentation,different community-based approaches and styles,...
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Ontology Reuse
Reuse Difficulties Example
The Fluidops interface renders the DBpedia RDF data from the Copernicuscrater and places it on the surface of the Earth instead of realizing that thegiven coordinates are selenographic coordinates.
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Standardizing Meaning
‘Standardized Meaning’ Approaches to Ontology Engineering
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Standardizing Meaning
‘Standardized Meaning’ Approaches to Ontology Engineering
California:City ≡ Town
Utah:Town ≡< (population, 1000)
Pennsylvania:Town ≡ {Bloomsburg}
We will revisit the cities & towns example and the local nature of meaning later.
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Styles and Committments
Modeling Practive, Styles, and Level of Detail
With growing size, complexity, and abstraction-level, different modeling styles,ontological choices, levels of detail becomes increasingly difficult to handle.
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
The Cause of the Problem
Is There a Common Cause to These Problems?
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
The Cause of the Problem
Is There a Common Cause to These Problems?
We will revisit the age example at the end of this talk.
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Architecturefrom above
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Pattern-based Arcitecture
Envisioned, Pattern-based Arcitecture (Horizontal)
Patterns act as fallback level that ensures minimal interoperability whilepreserving heterogeneity (i.e., local, repository-specific ontologies can differ).
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Pattern-based Arcitecture
Envisioned, Pattern-based Arcitecture (Horizontal)
Views as virtual ontologies. ‘All’ provider- and user-perspectives agree on acommon core; more specific results can differ.
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Pattern-based Arcitecture
Envisioned, Pattern-based Arcitecture (Horizontal)
All users can query for data that correspond to the pattern, using view A one canretrieve data on human trajectories but these data will only come from RA and RB.
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Pattern-based Arcitecture
Envisioned, Pattern-based Arcitecture (Vertical)
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Pattern-based Arcitecture
Envisioned, Pattern-based Arcitecture (Vertical)
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Pattern-based Arcitecture
Envisioned, Pattern-based Arcitecture (Vertical)
Cons... speak now or foreverhold your peace
ProsDefers the introduction of classes that areheavy on ontological commitments (e.g.,‘vulnerability’)No need for (community-wide) agreementwhich is a key (social) challenge for otherapproaches. Preserves heterogeneityMines ontological primitives out of realobservation dataAssists domain experts in becomingknowledge engineers by developingreusable patternsMoves ontology reuse to the layer whereit belongs (and avoid Frankenontologies)Is driven by publishing, discovery, reuse,and integration needs.
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Patternsand views
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Semantic Trajectory Pattern
A Semantic Trajectory Pattern
A pattern for discrete trajectories of people, wildlife, vessels, and so forth.
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Semantic Trajectory Pattern
A Semantic Trajectory Pattern
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Semantic Trajectory Pattern
Ontology Design Patterns Can Be Specialized
Trajectories that model the research cruises of scientific vesselsIs this still an ontology design pattern?
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Semantic Trajectory Pattern
AMicro-Ontology for Cruises
Combining the InformationObject, Agent, Event, Vessel, and Trajectory patterns
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Semantic Trajectory Pattern
Idea Behind Semantic Trajectory Pattern
Can cover a wide range of domainsCan be easily extendedSupports multiple granularitiesAxiomatization beyond mere surface semanticsHas various hooks to well-known ontologies / patterns.Only partially self-contained §
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Typecasting and Views
Typecasting – Dealing with Styles
Typecasting Individual to Class and Back
ClassName v ∃hasType.{classname} (1)∃hasType.{classname} v ClassName (2)
Rolification: Typecasting from Classes to Properties...Reification: Typecasting Properties into Classes...
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Typecasting and Views
Views – Dealing with Granularity and Perspectives
Vessel ≡ ∃RVessel.SelfCruise ≡ ∃RCruise.Self
Trajectory ≡ ∃RTrajectory.SelfSegment ≡ ∃RSegment.SelfRSegment ◦ hasSegment− ◦ RTrajectory ◦
◦ hasTrajectory− ◦ RCruise ◦ isUndertakenBy v isTraversedBy
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Typecasting and Views
SignaturesSemantic
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Sensors and Signatures
Observatories and Their Sensors
Whether on land or in space, observatories and their sensors servedifferent purposes and are most useful when they work together.
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Sensors and Signatures
Spectral Signatures, Bands, and Remote Sensing
Spectral signatures are the combination of emitted, reflected, or absorbedelectromagnetic radiation at varying wavelengths (bands) that uniquelyidentify a feature type.Spectral libraries, the idea of sharing spectral signatures, hasrevolutionized remote sensing.
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Semantic Signatures and Bands
Semantic Signatures As Analogy To Spectral Signatures
Geospatial bandsbased on geographic location
ANNDRipley’s K BinsJ MeasureDzero
Temporal bandsbased on geo-social check-ins
24 Hours7 DaysSeasons
Thematic bandsbased on venue tips and reviews
LDA topicsTF-IDF
Makes use of dataheterogeneity
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Semantic Signatures and Bands
Semantic Signatures and Bands
Semantic signature
Geographic feature
Feature type
Spatial band
Temporal band
Thematic band
rdfs:subClassOf
rdfs:subClassOfrdfs:subClassOf
signifies
hasObservationResulthasType
BandconsistsOf
Spatial signature Temporal signature
Thematic signature
rdfs:subClassOf rdfs:subClassOf rdfs:subClassOf
LDA topic vectorrdf:type
hasSignature
Semantic signatures and bands are an analogy to spectral signatures.So far, we have mined and modeled hundreds of bands for hundreds ofdifferent geographic features on the micro, meso, and macro-scale.Applied them to categorization, deduplication, semantic enrichment, cleansing,visualization, exploration, reverse geocoding, ontology alignment,...
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Thematic Bands
Thematic Bands & Geo-Indicativeness
Places at geographic location 34.43, -119.71 are:of types city, county seat,...at the coastline, near the mountains, have Mediterranean climate,...described in terms of urban area, economy, tourism, government, employment,...
Interesting observation: some of these terms will co-occur by type, others per region.Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Thematic Bands
Thematic Bands & Geo-Indicativeness
A thematic band can becomputed out of unstructuredtext from sources such asWikipedia, travel blogs, newsarticles, and so forth.Non-georeferenced plain textis often still geo-indicativeDifferent types of geographicfeatures have different,diagnostic topics associated tothem (out of 500 topics)Indicative topics and be lifted tothe type-level.Here, we modeled topics usinglatent Dirichlet allocation (LDA)
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Thematic Bands
Thematic Bands & Geographic Feature Types
City topics: 204>450>104>282>267>497>443>484>277>97>...Town topics: 425>450>419>367>104>429>266>69>204>308>...Mountain topics: 27>110>5>172>208>459>232>398>453>183>...
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Temporal Bands
Temporal Bands
Study geo-socialcheck-in data tolocation-based socialnetworks.Aggregate them to thefeature type level andclean them.Intuitively, people visitwineries in theafter-noon and eveningand bakeries in themornings.Combining weekly andhourly bands to createplace type signatures.
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Spatial Bands
Spatial Bands
POI plotted by similarity to bar and post office in OpenStreetMap data (London)Similarity measured as association strength in OSM change historyBars (and similar features) tend to clump togetherPost Offices (and similar features) are rather uniformly distributed
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Spatial Bands
Spatial Bands
Dzero measures the likelihood of features of a certain type to co-occurwithin a specific semantic and spatial range.General idea: generate recommendations and clean up data based ontype likelihood. ’How likely is a post office directly next to an existing one?’
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Sensor Resolution & Social Sensing
Sensor Resolution & Social Sensing
(Remote sensing) sensors can be characterized by their resolution
Spatial resolution: smallest feature that can be detected, i.e., the pixel size.Temporal resolution: smallest time interval between a repeated observation.Spectral resolution: number, position, and width of spectral bands.Radiometric resolution: small distinguishable differences in radiation magnitude.
Analogous social sensor resolutions, e.g., types of bands, number of topics.Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Sensor Resolution & Social Sensing
Place-based Resolution of Termporal Signatures
Circular temporal signatures histograms for Theme Park (a,b,c) andDrugstore (d,e,f).About 50% of ≈ 400 Point Of Interest (POI) types are regionally invariant in the USA.
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Sensor Resolution & Social Sensing
Temporal Resolution of Termporal Signatures
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
The ‘Foursquare-day’How and when do people check-in at places, manually, automatically?Do they check-out? If not, after what time are they checked-out automatically?
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Sensor Resolution & Social Sensing
Distinguishable Feature Types For Thematic Signatures From 500-Topics
Which place types can be meaningfully distinguished (in DBpedia)?
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
InterfacesOntologies as
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Exploring Linked Data
Ontologies as Interfaces
Our completely client-based JS explorer can be connected to any triple store
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Exploring Linked Data
Ontologies as Interfaces
Allows users to explore Linked Data; constructs filters (e.g., >) based on probing
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Exploring Linked Data
Ontologies as Interfaces
Allows users to explore Linked Data; constructs filters (e.g., >) based on probing
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Exploring Linked Data
Ontologies as Interfaces
How does it know which data can be mapped and how to do this?
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
Exploring Linked Data
Ontologies as Interfaces
Our explorer can even combine data from multiple sources as layers (here cruisesfrom R2R in green and gazetteer features in pink)
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
What about Time?
What about Time and Age?
Google’s Knowledge Graph in 2012
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
What about Time?
What about Time and Age?
Google’s Knowledge Graph in 2013
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
What about Time?
What about Time and Age?
Google’s Knowledge Graph in 2014
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
What about Time?
What about Time and Age?
Google’s Knowledge Graph in 2015
Ontology Engineering: A View from the Trenches K. Janowicz
The Big Picture Challenges Architecture Patterns Semantic Signatures Interfaces
What about Time?
What about Time and Age?
Google’s Knowledge Graph in 2015
Ontology Engineering: A View from the Trenches K. Janowicz