The Semantic Web

Lecture 6 Ontology and the Semantic Web

The problem of ontology human beings can integrate highly heterogeneous information inside their heads

Consider how the human mind copes with complex phenomena in the social realm (e.g. speech acts of promising) which involve: experiences (speaking, perceiving) intentions (including potentially conflicting or disguised intentions) language action (and tendencies to action) deontic powers, obligations, claims, authority … background habits mental competences records and representations

understanding how computers can effect the same sort of integration is a difficult problem

The Semantic Web designed to integrate the vast amounts of heterogeneous online data and services via dramatically better support at the level of metadata that will yield the ability to query and integrate across different conceptual systems

Tim Berners-Lee, inventor of the internet “ sees a more powerful Web emerging, one where documents and data will be annotated with special codes allowing computers to search and analyze the Web automatically. The codes … are designed to add meaning to the global network in ways that make sense to computers”

Tim Berners-Lee: hyperlinked vocabularies, called ‘ontologies’ will be used by Web authors “to explicitly define their words and concepts as they post their stuff online. “ The idea is the codes would let software ‘agents’ analyze the Web on our behalf, making smart inferences that go far beyond the simple linguistic analyses performed by today's search engines.”

Exploiting tools such as: XML (Extensible Markup Language) RDF (Resource Descriptor Framework) OWL (Ontology Web Language – a fragment of First Order Logic with nice computational properties) Often, in Semantic Web circles, an ontology is identified as any artifact that is formulated using the OWL language

Ebiquity Publication Ontology http://ebiquity.umbc.edu/ontology/publication.owl - <owl:ObjectProperty rdf:ID=" author "> <rdfs:label>Resource Author</rdfs:label> <rdfs:domain rdf:resource="#Resource" /> <rdfs:range rdf:resource="http://ebiquity.umbc.edu/ontology/person.owl#P erson " /> - <owl:DatatypeProperty rdf:ID=" chapter "> <rdfs:label>Publication Chapter</rdfs:label> <rdfs:domain rdf:resource="#Publication" /> <rdfs:range rdf:resource="http://www.w3.org/2001/XMLSchema# string " /> what sort of string is a chapter?

Example: The Enterprise Ontology A Sale is an agreement between two Legal-Entities for the exchange of a Product for a Sale-Price. A Strategy is a Plan to Achieve a high-level Purpose. A Market is all Sales and Potential Sales within a scope of interest.

Assumptions Communication / compatibility problems should be solved automatically (by machine) Hence ontologies must be applications running in real time

Computational tractability Semantic Web Ontologies are computer artifacts subject to severe constraints on expressive OWL DL (for Description Logic) a maximum fragment of first order logic for which a complete inference procedure and a decision procedure are known to exist Brings considerable benefits in building ontologies – you can check your ontology for consistency Good for capturing static combinatorial information (pizzas, family relations …); less good when it comes to dealing with time and instances

Philosophical issues about classes and instances SARS is NOT Severe Acute Respiratory Syndrome it is THIS collection of instances of Severe Acute Respiratory Syndrome associated with THIS coronavirus and ITS mutations

Clay Shirky The Semantic Web is a machine for creating syllogisms. Humans are mortal Greeks are human Therefore, Greeks are mortal

Lewis Carroll - No interesting poems are unpopular among people of real taste - No modern poetry is free from affectation - All your poems are on the subject of soap-bubbles - No affected poetry is popular among people of real taste - No ancient poetry is on the subject of soap-bubbles Therefore: All your poems are bad.

the promise of the Semantic Web it will improve all the areas of your life where you currently use syllogisms

most of the data we use in our everyday lives is not amenable to recombination in syllogistic form because it is partial, inconclusive, context-sensitive So we guess, extrapolate, intuit, we do what we did last time, we do what we think our friends would do … but we almost never use syllogistic logic.

The Semantic Web Initiative The Web is a vast edifice of heterogeneous data sources Needs the ability to query and integrate across different data systems

How resolve incompatibilities? The Semantic Web idea: create terminological compatibility via standardized term hierarchies, with standardized definitions of terms, which 1. satisfy the needed logical constraints 2. are applied as meta-tags to the content of websites (Tim Berners-Lee: we need to do this without losing the sorts of freedoms exemplified by the existing html-based web)

Merging Databases In the brave new world of the Semantic Web, “ Merging databases simply becomes a matter of recording in RDF somewhere that "Person Name" in your database is equivalent to "Name" in my database, and then throwing all of the information together and getting a processor to think about it.” [ http://infomesh.net/2001/swintro/ ] Is your "Person Name = John Smith" the same person as my "Name = John Q. Smith"? Who knows? Not the Semantic Web

XML-syntax does not help <BUSINESS-CARD> <FIRSTNAME>Jules</FIRSTNAME> <LASTNAME>Deryck</LASTNAME> <COMPANY>Newco</COMPANY> <MEMBEROF>XTC Group</MEMBEROF> <JOBTITLE>Business Manager</JOBTITLE> <TEL>+32(0)3.471.99.60</TEL> <FAX>+32(0)3.891.99.65</FAX> <GSM>+32(0)465.23.04.34</GSM> <WEBSITE>www.newco.com</WEBSITE> <ADDRESS> <STREET>Dendersesteenweg 17</STREET> <ZIP>2630</ZIP> <CITY>Aartselaar</CITY> <COUNTRY>Belgium</COUNTRY> </ADDRESS> </BUSINESS-CARD>

XML-syntax does not help <BUSINESS-CARD> <FIRSTNAME>Jules</FIRSTNAME> <LASTNAME>Deryck</LASTNAME> <COMPANY>Newco</COMPANY> <MEMBEROF>XTC Group</MEMBEROF> <JOBTITLE>Business Manager</JOBTITLE> <TEL>+32(0)3.471.99.60</TEL> <FAX>+32(0)3.891.99.65</FAX> <GSM>+32(0)465.23.04.34</GSM> <WEBSITE>www.newco.com</WEBSITE> <ADDRESS> <STREET>Dendersesteenweg 17 </STREET>

even with correct XML-syntax: <BUSINESS-CARD> <FIRSTNAME>Jules</FIRSTNAME> <LASTNAME>Deryck</LASTNAME> <COMPANY>Newco</COMPANY> <MEMBEROF>XTC Group</MEMBEROF> <JOBTITLE>Business Manager</JOBTITLE> <TEL>+32(0)3.471.99.60</TEL> <FAX>+32(0)3.891.99.65</FAX> <GSM>+32(0)465.23.04.34</GSM> <WEBSITE>www.newco.com</WEBSITE> <ADDRESS> <STREET>Dendersesteenweg 17</STREET> <ZIP>2630</ZIP> <CITY>Aartselaar</CITY> <COUNTRY>Belgium</COUNTRY> </ADDRESS> </BUSINESS-CARD> Is "Jules" the first name of the person, or of the business-card?

even with correct XML-syntax: <BUSINESS-CARD> <FIRSTNAME>Jules</FIRSTNAME> <LASTNAME>Deryck</LASTNAME> <COMPANY>Newco</COMPANY> <MEMBEROF>XTC Group</MEMBEROF> <JOBTITLE>Business Manager</JOBTITLE> <TEL>+32(0)3.471.99.60</TEL> <FAX>+32(0)3.891.99.65</FAX> <GSM>+32(0)465.23.04.34</GSM> <WEBSITE>www.newco.com</WEBSITE> <ADDRESS> <STREET>Dendersesteenweg 17</STREET> <ZIP>2630</ZIP> <CITY>Aartselaar</CITY> <COUNTRY>Belgium</COUNTRY> </ADDRESS> </BUSINESS-CARD> Is Jules or Newco the member of XTC Group?

even with correct XML-syntax: <BUSINESS-CARD> <FIRSTNAME>Jules</FIRSTNAME> <LASTNAME>Deryck</LASTNAME> <COMPANY>Newco</COMPANY> <MEMBEROF>XTC Group</MEMBEROF> <JOBTITLE>Business Manager</JOBTITLE> <TEL>+32(0)3.471.99.60</TEL> <FAX>+32(0)3.891.99.65</FAX> <GSM>+32(0)465.23.04.34</GSM> <WEBSITE>www.newco.com</WEBSITE> <ADDRESS> <STREET>Dendersesteenweg 17</STREET> <ZIP>2630</ZIP> <CITY>Aartselaar</CITY> <COUNTRY>Belgium</COUNTRY> </ADDRESS> </BUSINESS-CARD> use of OWL-DL syntax in many cases similar Do the phone numbers and address belong to Jules or to the business?

Clay Shirkey: “ The Semantic Web’s philosophical argument – the world should make more sense than it does – is hard to argue with. The Semantic Web, with its neat ontologies and its syllogistic logic, is a nice vision. However, like many visions that project future benefits but ignore present costs, it requires too much coordination and too much energy to be effective in the real world … “ A world of exhaustive, reliable metadata would be a utopia.”

Problem 1: People lie Meta-utopia is a world of reliable metadata. But poisoning the well can confer benefits to the poisoners Metadata exists in a competitive world. Some people are crooks. Some people are cranks. Some people are French philosophers. Who will police the coding?

Problem 2: People are lazy How many pages on the web are titled: “Please title this page”

Problem 3: People are stupid The vast majority of the Internet's users (even those who are native speakers of English) cannot spell or punctuate Will internet users learn to accurately tag their information with whatever DL-hierarchy they're supposed to be using?

Problem 4: Multiple descriptions “ Requiring everyone to use the same vocabulary denudes the cognitive landscape, enforces homogeneity in ideas.” ( Cary Doctorow)

Problem 5: Ontology Impedance = semantic mismatch between ontologies being merged Solution 1: treat it as inevitable, and learn to find ways to cope with the disturbance which it brings* Solution 2:r esolve the impedance problem by hand on a case-by-case basis

Both solutions fail 1. treating mismatches as ‘impedance’ ignores the problem of error propagation (and is inappropriate in critical areas like medicine or finance) 2. resolving impedance on a case-by-case basis defeats the very purpose of the Semantic Web

Clay Shirkey: Let a million lite ontologies bloom “ Much of the proposed value of the Semantic Web is coming, but it is not coming because of the Semantic Web. The amount of meta-data we generate is increasing dramatically, and it is being exposed for consumption by machines as well as, or instead of, people. But it is being designed a bit at a time, out of self-interest and without regard for global ontology.”

Schemaweb ontologies (originally at http://www.w3.org/) Early Days of the Web (2002) MusicBrainz Metadata Vocabulary Musical Baton Vocabulary Beer Ontology Kissology Pet Profile Ontology …

they often do not generalize … repeat work already done by others are not gluable together (expensive to map, hard to keep mappings up-to-date) resist progressive improvement reproduce the silo problems which ontology was designed to solve are often used in sloppy ways blooming lite ontologies good for some things; but

RetailPrice hasA Denomination InstanceOf Dollar (p. 101) SI-Unit instanceof System-of-Units (p. 40) from Handbook of Ontology ( Semantic Web approach)

from: Ontological Engineering (Semantic Web approach) location =def. a spatial point identified by a name (p. 12) arrivalPlace =def. a journey ends at a location (p. 13) facet =def. ternary relation that holds between a frame, a slot, and the facet (p. 51)

We will be able to use ontologies to help us share data only if they are ontologically coherent (intelligible to a human user) and logically coherent and computationally tractable and work well together – evolve together – created according to the tested rules

A new approach prospective standardization based on objective measures of what works bring together selected groups to agree on and commit to good terminology / annotation habits (traffic laws) preemptively

Compare science scientific theories must be common resources (cannot be bought or sold) must be intelligible to a human being they must use open publishing venues they must constantly evolve to reflect results of scientific experiments (“evidence-based”) must be synchronized use common SI system of units common mathematical theories (built by adults)

Semantic Web: moving in the right direction recognition that creating many local ad hoc (‘lite’) ontologies will not somehow magically meld into an intelligent end result Schemaweb Musicbeanz OWL ontologies now removed from W3C website; gradually being supplanted by serious science-driven efforts, for example in the healthcare domain* (some) recognition of the need for coordination (the end of html-inspired anarchy?) *see e.g. http://tinyurl.com/ydc5l4o

Goal: where OWL-DL constraints ontology developers in their use of ‘is_a’ to inhibit ontological impedance the Semantic Web needs to foster use of a rigorously tested common upper level ontology which goes much further than this

the needed upper level ontology will be not just a system of categories but a formal theory with definitions, axioms, theorems designed to allow building of ontologies for specific domains by populating downwards from a shared common core the latter should be of sufficient richness that terminological incompatibilities can be resolved intelligently rather than by brute force

alternative frameworks OBO Format http://oboedit.org/ OWL DL http://www.co-ode.org/resources/papers/OBO2OWL.pdf Common Logic http://cl.tamu.edu/ http://www.berkeleybop.org/people/cjm/Mungall-bib.html#mungall_experiences_2009

the goal is to reach a situation where it is not arbitrary how entities of a given type are to be treated – there is very little discretion / freedom of choice on the part of the ontology builder as concerns use of part_of , located_at , earlier_than …

Candidate Upper-Level Integrating Ontologies (Upper) CYC SUMO DOLCE BFO

The Background of Cyc axiomatic representation of the entirety of human common sense gigantic investment 25 years “ a large and broad ontology, knowledge base, and inference engine” ontology = a representation of knowledge assumed to allow contradictory information thus: use of microtheories to screen against contradictions

CLASSIFICATION OF HUMAN-TYPE-BY-CUP-SIZE cup size a = instance of human type by cup size instance of partially tangible type by non-numeric size subtype of homo sapiens disjoint with cup size b the collection of people with female breast cup size a human type by cup size is an instance of collection with an event-like order Some problems with Cyc

A collection of collections. Each instance of CollectionWithAnEventLikeOrder is a collection whose instances are conventionally regarded as being ordered by some relation RELN, where RELN orders the members of COL in the manner in which events are ordered in linear time. For example, the instances of Distance are conventionally regarded as being ordered by the relation greater than, and this ordering is event-like. So Distance is a collection with an event-like order . Some problems with Cyc

biology microtheory is an instance of general microtheory general micortheory is an instance of microtheory type microtheory type is an instance of second order collection second order collection is an instance of collection type type collection type type is an instance of collection type collection type is an instance of collection type by disjointness collection type by disjointness is an instance of collection type [!] collection type type subsumed by collection type [!] collection type subsumed by collection [!] collection is an instance of collection type Some problems with Cyc

plant is an instance of biological kingdom plant is a subset of vegetable matter plant is the collection of all individual plants cell is an instance of clarifying collection type cell is an instance of biology (topic) flower (botanical part) is the collection of all reproductive organs of angiosperm plants. may or may not look like conventional 'flowers' Some problems with Cyc

#$Configuration A specialization of both #$StaticSituation and #$SpatialThing-Localized. Each instance of #$Configuration is a static situation consisting of two or more #$PartiallyTangible things of certain types standing in a certain type of spatial relationship (or set of relationships). This (set of) spatial relationship(s) characterizes the #$Configuration's _type_ in the sense that any group of objects of the appropriate types standing in that relationship (or those relationships) correspond to a #$Configuration of that type; and each of these objects, in turn, is said to be configured (see #$objectConfigured) in the (individual) #$Configuration. Some problems with Cyc upper level

it has no progressive cumulation from an established core it has too little concern for consistency with basic science (common sense should not wear the trousers) it has no perspicuous policies for updating it is largely unintelligible to outsiders why Cyc cannot do the job of providing a shared upper level

SUMO: Suggested Upper Merged Ontology with thanks to Adam Pease [email_address] http://www.articulatesoftware.com

Suggested Upper Merged Ontology 1000 terms, 4000 axioms, 750 rules Associated domain ontologies totalling 20,000 terms and 60,000 axioms http://www.ontologyportal.org

SUMO Structure Structural Ontology Base Ontology Set/Class Theory Numeric Temporal Mereotopology Graph Measure Processes Objects Qualities

SUMO+Domain Ontologies Total Terms Total Axioms Rules 20399 67108 2500 Structural Ontology Base Ontology Set/Class Theory Numeric Temporal Mereotopology Graph Measure Processes Objects Qualities SUMO Mid-Level Military Geography Elements Terrorist Attack Types Communications People Transnational Issues Financial Ontology Terrorist Economy NAICS Terrorist Attacks … France Afghanistan UnitedStates Distributed Computing Biological Viruses WMD ECommerce Services Government Transportation World Airports

entity physical abstract quantity number real number rational number irrational number nonnegative real number negative real number binary number imaginary number complex number physical quantity attribute set or class relation proposition graph graph element

entity physical object process dual object process intentional process intentional psychological process recreation or exercise organizational process guiding keeping maintaining repairing poking content development making constructing manufacture cooking searching social interaction maneuver motion internal change shape change abstract

corpuscular object = def. A SelfConnectedObject whose parts have properties that are not shared by the whole. Superclass(es) entity physical object self-connected object Subclass(es) organic object artifact Coordinate term(s) content bearing object food substance Axiom: corpuscular object is disjoint from substance. substance = def . An Object in which every part is similar to every other in every relevant respect. entity physical process intentional process intentional psychological process recreation or exercise organizational process guiding keeping maintaining repairing poking content development

Subclass Hierarchy Tree entity physical object self connected object substance corpuscular object organic object organism plant flowering plant non flowering plant fungus moss fern animal microorganism toxic organism

Subclass Hierarchy Tree entity physical object self connected object substance corpuscular object organic object organism plant flowering plant non flowering plant fungus moss fern animal microorganism toxic organism Corpuscular Object =def. A Self Connected Object whose parts have properties that are not shared by the whole.")

advantages of SUMO Advantages of SUMO fully axiomatized in First Order Logic (FOL) clear logical infrastructure: too expressive for decidability, more intuitive (human friendly) than e.g. OWL much more coherent than e.g. CYC upper level good web support open source

problems with SUMO as Upper-Level Problems with SUMO it contains its own tiny biology (‘protein’, ‘crustacean’, ‘body-covering’, ‘fruit-Or-vegetable’ ...) this causes problems for use of SUMO as an integrating top-level for the life sciences, since biologists would need to adjust their ontologies to fit (and would need to update their ontologies each time SUMO makes changes) THIS SLIDE CORRECTED FROM ORIGINAL VERSION

DOLCE: Descriptive Ontology for Linguistic and Cognitive Engineering Strong cognitive/linguistic bias: descriptive (as opposite to prescriptive ) attitude Categories mirror cognition, common sense, and the lexical structure of natural language. Categories as conceptual containers : no “deep” metaphysical implications Rich axiomatization 37 basic categories 7 basic relations 80 axioms, 100 definitions, 20 theorems Rigorous quality criteria and extensive documentation

DOLCE taxonomy Q Quality PQ Physical Quality AQ Abstract Quality TQ Temporal Quality PD Perdurant EV Event STV Stative ACH Achievement ACC Accomplishment ST State PRO Process PT Particular R Region PR Physical Region AR Abstract Region TR Temporal Region T Time Interval S Space Region AB Abstract Set Fact … … … … TL Temporal Location SL Spatial Location … … … ASO Agentive Social Object NASO Non-agentive Social Object SC Society MOB Mental Object SOB Social Object F Feature POB Physical Object NPOB Non-physical Object PED Physical Endurant NPED Non-physical Endurant ED Endurant SAG Social Agent APO Agentive Physical Object NAPO Non-agentive Physical Object … AS Arbitrary Sum M Amount of Matter … … … …

1 - The physical view (= one of many views) Basic qualities ascribed to atomic spacetime regions (e.g., mass, electric charge…) physical processes are spatiotemporal distributions of qualities

2 - The cognitive view Humans isolate relevant invariances on the basis of: Perception (as resulting from evolution) Cognition and cultural experience Language A set of atomic percepts is associated to each situation is this consistent with common sense?

DOLCE’s Multiplicative Ontology The statue and the lump of clay here on my desk The human being and the collection of molecules here behind my desk They have different histories Based on DOLCE’s Linguistic View

Substitutivity Tests I am talking here *This bunch of molecules is talking *What’s here now is talking This statue is looking at me *This piece of marble is looking at me This statue has a strange nose *This piece of marble has a strange nose

DOLCE embraces abstract (non-physical) entities = entities with no inherent spatial localization Dependent on agents mental (depending on singular agents) social (depending on communities of agents) Agentive: a company, an institution Non-agentive: a law, the Divine Comedy, a linguistic system

Advantages of DOLCE clear logical infrastructure (FOL) – beyond computability much more coherent than e.g. CYC upper level successful applications in a number of research projects

Problem with DOLCE not sure if it is an ontology of reality or an ontology of concepts ontology of molecules, light, sexual dimorphism in plants, etc. or: ontology of concepts = part of the ontology of psychology, of language (etc.)

Basic Formal Ontology as alternative (as subset of DOLCE and SUMO)? Advantages of BFO a true upper level ontology no interference with domain ontologies no interference with physics / biology / cognition / mathematics no abstracta a small subset of DOLCE but with more adequate treatment of instances, types, relations and qualities

BFO Continuant Occurrent (Process) Independent Continuant Dependent Continuant ..... ..... ........

BFO Continuant Occurrent (Process) Independent Continuant ( molecule, cell, organ, organism ) Dependent Continuant ( quality, function, disease ) Functioning Side-Effect, Stochastic Process, ... ..... ..... .... .....

The Semantic Web

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