ontology engineering: a view from the trenches - wop 2015 keynote

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

The Big Picture∗



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)


http://goo.gl/loYAta

see.it/tomorrow/9am


Ontology Engineering Challenges

Ontology Engineering

Challengesby Example



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 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.



Standardizing Meaning

‘Standardized Meaning’ Approaches to Ontology Engineering



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.



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.



The Cause of the Problem

Is There a Common Cause to These Problems?



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.



Architecturefrom above



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).





Views as virtual ontologies. ‘All’ provider- and user-perspectives agree on acommon core; more specific results can differ.





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.




Envisioned, Pattern-based Arcitecture (Vertical)




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.



Patternsand views



Semantic Trajectory Pattern

A Semantic Trajectory Pattern

A pattern for discrete trajectories of people, wildlife, vessels, and so forth.




A Semantic Trajectory Pattern




Ontology Design Patterns Can Be Specialized

Trajectories that model the research cruises of scientific vesselsIs this still an ontology design pattern?




AMicro-Ontology for Cruises

Combining the InformationObject, Agent, Event, Vessel, and Trajectory patterns




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 §



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...




Views – Dealing with Granularity and Perspectives

Vessel ≡ ∃RVessel.SelfCruise ≡ ∃RCruise.Self

Trajectory ≡ ∃RTrajectory.SelfSegment ≡ ∃RSegment.SelfRSegment ◦ hasSegment− ◦ RTrajectory ◦

◦ hasTrajectory− ◦ RCruise ◦ isUndertakenBy v isTraversedBy




SignaturesSemantic



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.



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.



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





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,...



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


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)



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>...



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.



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



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?’



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



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.




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?




Distinguishable Feature Types For Thematic Signatures From 500-Topics

Which place types can be meaningfully distinguished (in DBpedia)?



InterfacesOntologies as



Exploring Linked Data

Ontologies as Interfaces

Our completely client-based JS explorer can be connected to any triple store





Allows users to explore Linked Data; constructs filters (e.g., >) based on probing





How does it know which data can be mapped and how to do this?





Our explorer can even combine data from multiple sources as layers (here cruisesfrom R2R in green and gazetteer features in pink)



What about Time?

What about Time and Age?

Google’s Knowledge Graph in 2012



What about Time?




ontology engineering: a view from the trenches - wop 2015 keynote

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