Web Ontologies: Lessons Learned from Conceptual Modeling at Scale

Professur für ALLGEMEINE BWL insbesondere E-BUSINESS

Web Ontologies: Lessons Learned from Conceptual Modeling at Scale Martin Hepp, @mfhepp

[email protected]


Ontology in Philosophy

•  “…that branch of philosophy which deals with the nature and the organisation of reality.”

Guarino and Giaretta (1995): Ontologies and Knowledge Bases

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Example: Part-Whole Theories

• “Mereology” • E.g. proper parts vs. other forms of part-whole relations

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Sounds familiar?

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Here is the full section:

Gruber, T. R. (1995): Toward principles for the design of ontologies used for knowledge sharing.

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Gruber (1995): Ontologies in Computer Science

The term is „borrowed from philosophy“

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Ontologies Approximate the Intended Models

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N. Guarino (1998): Formal Ontology in Information Systems


Ontologies for Systems Interoperability

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Problem

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Ideal Ontology


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However,…

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§  Computer-based Information Systems are not close to be full knowledge-based systems

§  They are unable to operate solely on axiomatic theories (“rule sets”)


This reduces the practical scope of ontologies to…

•  Providind a shared type system that is useful across systems

•  Improving the reliability of type information for entities

•  Providing some rules that hold implicit facts (e.g. transitivity of a property)

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Improve the Reliability of Type Information

Type:Hotel

Hotel 1

Hotel 2

Hotel 3

System 1 System 2

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Acceptable Ontology

Prof. Dr. Martin Hepp, Universität der Bundeswehr München 14

Two users / systems


Bad Ontology


Two users / systems


Reliability of Type Membership (Sketch)

Some dimension

Agents who agree

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Historically, Ontologies aimed at deterministic behavior



The community was small



And there were social ties beyond HTTP between the humans


EKAW 1987

Researcher 1

Researcher 3

Researcher 2


And then came the Web



And people borrowed the term again...


Ontologies for the Web


The Semantic Web (2001)


Ontologies.... §  for increasing automated information

processing at Web scale


However, if the number of human users gets large,...



...the whole thing turns into a probabilistic system


§  Agents Ai, A set of all agents

§  Sender S element of A

§  Receiver R element of A

§  O set of objects / phenomena

§  f(Oi,Sj) = 0 or 1

§  f(Oi, Ri) = 0 or 1

§  Etc.


Reliability of Type Information


2 Agents, 85 % Agreement


The Fallacy of Raw Consumption of Web Data

http://www.heppresearch.com 29

Naïve Type Membership Interpretation: SPARQL

# Find former people who are professors

PREFIX dbpedia-owl: <http://dbpedia.org/ontology/>

SELECT * {?s a dbpedia-owl:Professor} LIMIT 100

Naïve Type Membership Interpretation: SPARQL


Find all professors from Web markup <html prefix="schema: http://schema.org/ ! dbpedia: http://dbpedia.org/ontology/"> !!<div typeOf="schema:Person dbpedia:Professor" about="#person"> ! <span property="schema:honorificPrefix">Prof. Dr.</span> ! <span property="schema:givenName">Zaphod</span> ! <span property="schema:familyname">Beeblebrox</span> !</div> !</html>


Which properties determine the probabilities?


§  Number of individuals of that type

–  Familiarity: Banana vs. Papaya

§  Cognitive skills of involved humans

§  Etc.

§  We do not know yet ;-)


Fallacy 1: Formal specifications would guarantee correct usage


§  Axiomatic definitions

§  In fact, no serious evidence


Fallacy 2: Web Ontologies allow for the automated processing of data


§  Raw data on the Web can rarely be used directly for any meaningful purposes

§  Data cleansing, entity consolidation, etc.

§  Navigation on raw, linked data questionable


Recommended Reading



The Role of Schemas between Men and Machines


Dat

abas

e Sc

hem

as

Web

Ont

olog

ies


Test: How well can the target audience understand and apply the conceptual distinctions in the ontology?


§  Examples:

–  Book copy vs. book title

–  Web page vs. entity described by the page

–  Legal entity vs. shop location

§  Turning ontology engineering from an art into an empirical science.


Optimize: Compare the discriminative value of your conceptual elements with their effect on conformant ontology usage



Monitor: Capture live feedback on the quality of the membership functions of your ontology



Challenges

• Goal 1: Improve the reliability of type memberships of entities in information processing

• Goal 2: Find types that provide sufficient distinctions for an algorithmic information processing, i,e. minimize reclassification tasks at the recipient’s side

• Goal 3: Ditto for data granularity • Goal 4: Find types that can be populated from existing

datasources without reclassification tasks at the publisher side.

• Goal 5: Find conceptual distinctions that can be reliable mastered by the human stakeholders of the systems

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Challenges






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Challenges






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Challenges






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Challenges






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What is schema.org? What is GoodRelations?

1. Official Characterization 2. Purpose: §  Focus on information extraction on the Web §  Other uses as a by-product

3. Knowledge Representation Perspective §  Entity Types §  Relationship Types §  Weak Domain / Range Semantics §  Syntax-independent Meta-Model

And how are they related?

Questions? Suggestions? Contact me at @mfhepp! 47


Official Characterization from http://schema.org


This site provides a collection of schemas that webmasters can use to markup HTML pages in ways recognized by major search providers, and that can also be used for structured data interoperability (e.g. in JSON). Search engines including Bing, Google, Yahoo! and Yandex rely on this markup to improve the display of search results, making it easier for people to find the right Web pages.

Many sites are generated from structured data, which is often stored in databases. When this data is formatted into HTML, it becomes very difficult to recover the original structured data. Many applications, especially search engines, can benefit greatly from direct access to this structured data. On-page markup enables search engines to understand the information on web pages and provide richer search results in order to make it easier for users to find

relevant information on the web. Markup can also enable new tools and applications that make use of the structure.


There is REAL Momentum


A lot of data

§ Since 2011, schema.org has been added to >25% of top-ranked e-commerce sites product detail pages.

§ RDF-based representations are specified.

Table: Random sample of n=73 product detail pages from high-ranking Google results.

Note that these numbers have a strong bias towards popular, professionally operated sites.


Schema.org: A Data Publication Ontology


Not designed for raw data consumption (only as a by-product) §  Historically, ontologies in computer

science aimed at harmonizing the conceptualization and representation of data for publishers and consumers of the data.

§  Implicit goal of the traditional Semantic Web stack: More or less, consumption of raw data.

§  This requires detailed consensus on the level of data granularity and data semantics at scale, and high data quality.

§  Schema.org does not make this assumption, since its sponsors have the power to work on semi-structured data at Web scale.

Ontology schema.org


Schema.org: The Semantic Web Vision Come True?

1. No OWL. Not even an ontology in the narrow sense. 2. Direct consumption difficult §  Crawling §  Cleansing §  Lifting

3. No broad use of Linked Data principles §  Mostly no global entity identifiers §  Page = Entity (vs. httpRange-14) §  No vocabulary reuse (*)

Likely not what the Semantic Web community had hoped for.



From Ontology to Ontologies: The First Passage

•  From Philosophy to Computer Science

•  Exchange of Knowledge Bases

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The Web as an Ecosystem for Shared Schemata

•  Stakeholders

–  Schema Designer (a few)

–  Data Consumer (set the incentives)

–  Data Publisher / Site Owner (many, often limited set of skills and understanding)

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Web Ontology ≠ Ontology

•  Huge, unknown community of users

–  You speak only via your Web resources to them

•  Economic factors

–  Incentives vs. Costs, Spam, …

•  Data Quality issues

•  Huge amount of data

•  Limitations of deployment environments

–  Access to server configuration

–  Skills

–  Corporate policies

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The challenges on the Web are the same. Just at a bigger scale.






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Ontology Perspicuity

How well can the ontology be understood through the imperfect channel of communication between the designers and the users of an ontology?

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Ontology Economics

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Maintenance and Dynamics

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Requirements on Web Ontologies

•  Broad user base (>500) •  Understandable and usable just from the Web presence •  Decoupling the ontology from the evolution of enumerations

•  Dynamic Data Granularity and Incremental Refinement •  Deferred Consensus •  HTML documentation

•  Proper deployment •  Extensions should work with clients that understand only the basics if they have

minimal RDFS reasoning support

•  Minimize namespace traversals, i.e., locally define most concepts and link to external ontologies than simply reuse

•  Minimize number of conceptual elements

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Types of Community Involvement

•  Centralized Ontology Engineering

•  Community-driven Ontology Engineering

•  Community-inspired Ontology Engineering

•  Social Functionality (as in GoodRelations)

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Web Ontology Techniques

•  Dynamic Degree of Disambiguation

•  Dynamic Data Granularity and Lexical Carry-Over

•  Deferred Consensus

•  Incremental Refinement

•  Optional Nodes for N-Ary Relations

•  Social Functionality

•  Externalize Enumerations

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(Hepp 2015, forthcoming)


Dynamic Degree of Disambiguation

ProductOrService

Service Product

Agent

Person Organisation

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Dynamic Data Granularity

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Example for Deferred Consensus

gr:namegr:descriptionschema:geo schema:GeoCoordinatesacco:size gr:QuantitativeValueacco:occupancy gr:QuantitativeValueacco:occupancyAdults gr:QuantitativeValueacco:occupancyInfants gr:QuantitativeValueacco:petsAllowed xsd:boolean

acco:Accommodation

acco:feature acco:propertyID (*) xsd:stringgr:name (for raw values/text)gr:descriptionacco:min (gr:hasMinValue) gr:QuantitativeValueacco:max (gr:hasMaxValue) gr:QuantitativeValueacco:value (gr:hasValue) gr:QuantitativeValueacco:unit textacco:unitCode xsd:string (gr:hasUnitOfMeasurement)

acco:AccommodationFeature

PropertyIDsstarRating:HOTRECstarRating:DEHOGAstarRating:WHRstarRating:Hotelstars

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GoodRelations: Language Reference

http://purl.org/goodrelations/v1.html Examples

Links and References

Range and Domain

Social Media Community of Experts

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UML Class Diagram

http://www.heppnetz.de/ontologies/goodrelations/v1#uml

The GoodRelations Ontology for E-CommerceLanguage Overview - UML Class Diagram

http://purl.org/goodrelations/Version 1, Release 2011-10-01

Martin Hepp. [email protected]

gr:name rdfs:Literalgr:description rdfs:Literalgr:hasEAN_UCC-13 xsd:string gr:hasGTIN-14 xsd:stringgr:hasGTIN-8 xsd:stringgr:hasStockKeepingUnit xsd:stringgr:hasMPN xsd:stringgr:condition rdfs:Literalgr:serialNumber xsd:stringgr:eligibleRegions xsd:stringgr:validFrom xsd:dateTimegr:validThrough xsd:dateTimegr:availabilityStarts xsd:dateTimegr:availabilityEnds xsd:dateTimegr:category rdfs:Literal

gr:Offering

gr:name rdfs:Literalgr:description rdfs:Literalgr:datatypeProductOrServiceProperty anygr:hasEAN_UCC-13 xsd:string gr:hasGTIN-14 xsd:stringgr::hasGTIN-8 xsd:stringgr:hasStockKeepingUnit xsd:stringgr:hasMPN xsd:stringgr:condition rdfs:Literalgr:category rdfs:Literalgr:color rdfs:Literal

gr:ProductOrService

gr:priceType xsd:stringgr:hasUnitOfMeasurement xsd:stringgr:billingIncrement xsd:float

gr:UnitPriceSpecification

gr:eligibleRegions xsd:stringgr:DeliveryChargeSpecification

gr:name rdfs:Literalgr:description rdfs:Literalgr:legalName rdfs:Literalgr:hasNAICS xsd:intgr:hasISICv4 xsd:intgr:hasDUNS xsd:stringgr:hasGlobalLocationNumber xsd:stringgr:category rdfs:Literal

gr:BusinessEntity

gr:WarrantyScope

gr:PaymentMethod

gr:ProductOrServiceModel

gr:hasUnitOfMeasurement xsd:stringgr:hasMinValue rdfs:Literalgr:hasMaxValue rdfs:Literalgr:hasValue rdfs:Literal

gr:QuantitativeValue

gr:durationOfWarrantyInMonths xsd:intgr:WarrantyPromise

gr:DayOfWeek

gr:DeliveryMethod

gr:QualitativeValue

gr:amountOfThisGood xsd:floatgr:hasUnitOfMeasurement xsd:string

gr:TypeAndQuantityNode

gr:BusinessFunction

gr:name rdfs:Literalgr:description rdfs:Literalgr:hasGlobalLocationNumber xsd:stringgr:hasISICv4 xsd:intgr:category rdfs:Literal

gr:Location

gr:PaymentMethodCreditCard

gr:BusinessEntityType

gr:hasMaxValueInteger xsd:intgr:hasMinValueInteger xsd:intgr:hasValueInteger xsd:int

gr:QuantitativeValueInteger

gr:SomeItems

gr:opens xsd:timegr:closes xsd:timegr:validFrom xsd:dateTimegr:validThrough xsd:dateTime

gr:OpeningHoursSpecification

gr:PaymentChargeSpecification

gr:DeliveryModeParcelService

gr:hasCurrency xsd:stringgr:valueAddedTaxIncluded xsd:booleangr:hasMaxCurrencyValue xsd:floatgr:hasMinCurrencyValue xsd:floatgr:hasCurrencyValue xsd:floatgr:validFrom xsd:dateTimegr:validThrough xsd:dateTime

gr:PriceSpecification

gr:hasMinValueFloat xsd:floatgr:hasMaxValueFloat xsd:floatgr:hasValueFloat xsd:float

gr:QuantitativeValueFloat

gr:seeks gr:includesObject

gr:typeOfGood

gr:hasMakeAndModel

gr:hasMakeAndModel

gr:hasManufacturer

gr:hasPOS

gr:hasOpeningHoursSpecification

gr:availableAtOrFrom

gr:hasEligibleQuantity

gr:hasWarrantyPromise

gr:appliesToPaymentMethod

gr:hasWarrantyScopegr:qualitativeProductOrServiceProperty

gr:quantitativeProductOrServiceProperty

gr:appliesToDeliveryMethod

gr:hasBusinessFunction

gr:availableDeliveryMethods

gr:eligibleCustomerTypes

gr:acceptedPaymentMethods

gr:hasPriceSpecification

gr:hasOpeningHoursDayOfWeek

gr:isConsumableFor

gr:isVariantOf

gr:isSimilarTogr:isAccessoryOrSparePartFor

gr:offers

gr:hasInventoryLevel

gr:typeOfGoodgr:includes

gr:includes

Notes: 1. The following GoodRelations elements are only shortcuts for simpler annotation or querying. See the documentation at http://purl.org/goodrelations/ for details: gr:hasValue (shortcut for setting both hasMinValue and hasMaxValue properties to the same value in one turn) gr:hasValueFloat (shortcut for setting both hasMinValueFloat and hasMaxValueFloat properties to the same value in one turn) gr:hasValueInteger (shortcut for setting both hasMinValueInteger and hasMaxValueInteger properties to the same value in one turn)2. The following elements are now deprecated, but you can still use them, e.g. for staying compatible with older data consumers (e.g. Yahoo SearchMonkey): gr:ActualProductOrServiceInstance (now gr:Individual) gr:ProductOrServicesSomeInstancesPlaceholder (now gr:SomeItems) gr:LocationOfSalesOrServiceProvisioning (now gr:Location3. For the recommended cardinality of attributes, see the GoodRelations Language Reference at http://purl.org/goodrelations/ v1.html.4. gr:valueReference links can also exist between a gr:QualitativeValue and a gr:QuantitativeValue and vice versa, but this rare case is not shown for readability.5. gr:name and gr:description can now be attached to any GoodRelations type, but this is not shown here for readability.

gr:eligibleDuration

gr:advanceBookingRequirement

gr:owns

gr:greatergr:lesser

gr:lesserOrEqualgr:greaterOrEqual

gr:equalgr:nonEqual

gr:hasPreviousgr:hasNext

gr:successorOfgr:predecessorOf

gr:eligibleTransactionVolume

gr:eligibleTransactionVolume

gr:deliveryLeadTime

gr:serialNumber xsd:stringgr:Individual

Red highlighting indicates elements added or changed in this release.

gr:valueReference

gr:valueReference

gr:hasEligibleQuantity

gr:includes

gr:hasInventoryLevel

gr:weightgr:widthgr:heightgr:depth

gr:Brand

gr:hasBrand

gr:hasBrand

gr:hasBusinessFunction

gr:eligibleRegions xsd:stringgr:validFrom xsd:dateTimegr:validThrough xsd:dateTime

gr:License

gr:eligibleDuration

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GoodRelations User‘s Guide

http://wiki.goodrelations-vocabulary.org/Documentation

Will be updated soon!

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Community of Experts

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Albert Einstein on Ontology Engineering ☺

"Make everything as simple as possible, but not simpler.“

Albert Einstein

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Let’s Do Science, not Cult!


§  Challenge paradigms and approaches

§  Use hard data, not beliefs and assumptions (neither your own ones nor the ones inherited from the old folks)

CC BY-SA 3.0 / Nicor / https://en.wikipedia.org/wiki/North_Korea's_cult_of_personality#/media/File:Mansudae_Grand_Monument_08.JPG


Thank you! @mfhepp

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Web Ontologies: Lessons Learned from Conceptual Modeling at Scale

Technology

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