Ontology-based Representation and Reasoning about the History of Science

04/19/23 1

Ontology-based Representation and Reasoning about the History of Science

Ilaria CordaMsc by research studentSchool of ComputingUniversity of Leeds

Supervisors:

Dr. Vania DimitrovaDr. Brandon Bennett

04/19/23 2

Outline

•Thesis’s goal

•Research questions

•Motivating scenario

•Research methodology and approach

•Reasoning examples

•Achievements and limitations

•Further work


04/19/23 3

Thesis's goal

“The main goal of this research is to conceptualise (part of) the History of Science by focusing on modeling time and reasoning about time dependencies”


04/19/23 4

Research questions

Can ontological structures appropriately represent temporal specifications in History of Science?

Which methodological principles can be followed for the developing of a History of Science ontology?

What time concepts should be integrated? How can time specifications be added to relations and descriptions of events?

How can an ontology-based representation efficiently support reasoning on historical domains?


04/19/23 5

Motivating scenario

Main scientific models: Which theories extended the Copernican theory during the sixteen century? Which theories explained the phenomenon of the tides?

Main scientific events: Where was the inventor of the telescope from? By whom was the supernova observed during the Scientific Revolution?

Major contributors and their research activities: What did Galileo write? What did he invent?

Social network relationships: Who influenced Brahe? Who worked with Kepler?


04/19/23 6

Initial stage: search for ontology

Existing ontologies in which temporal concepts

area taken into account Upper ontologies (Cyc, Sowa)

Time ontologies (OWL time, KSL ontology)

Existing ontologies for historical domains Vicodi ontology, SWHI, HICO and TELOS

We need to build an ontology for the domain of History of Science


04/19/23 7

Modeling challenges addressed

General coverage of the domain

Main relations and common rules for History of Science exemplified in a specific area (Scientific Revolution in Europe)

Time-space dependence

When and where did a particular event happen? What happened in historical period H?

Social dependence

Who did influence X? Who did work with X?


04/19/23 8

Methodology for aHistory of Science ontology

Characteristics:

The domain expert acted as ontologist as well Time and expertise constraints

Existing methodologies and method reviewed:

METHONTOLOGY (Gomez-Perez, Fernandez-Lopez and Corcho, 2003). Gruninger and Fox’s methodology (Gruninger and Fox, 1995). Ordnance Survey’s methodology (Kovacs et al., 2006). Uschold and King’s method (Uschold and King, 1995).

Developing of a 3-phases methodology:

Pre-conceptualization Conceptualization Logical representation and coding


04/19/23 9

Pre-conceptualization phase

Providing a high level description of the domain and its characteristics

Investigating existing projects involving the use of ontologies in historical domains

Building a scenario

Identifying a range of potential informal competency questions to be addressed


04/19/23 10

Conceptualization phase

Activity 1: identifying main concepts and relations

Activity 2: Identifying time concepts and including time dimensions in relations

Activity 1:

Search for related ontologies and re-usable ontologies

Acquiring knowledge from different sources

Drafting a concept tree and relations in the form of triples

Building separated tables for concepts and relations

Competency questions throughout all ontology design process


04/19/23 11

Logical representation and coding

Choosing the ontology language(OWL, RDF, Prolog, etc)

Converting glossary knowledge into formal representation

Querying and reasoning about the domain


04/19/23 12

Coding the domain: Prolog

Prolog as ontology language.

Separation between data model (how knowledge is represented) and data description (actual data).

Classesclass(model). subclass(theory, model).

Upper relations relation_type(explain, theory, phenomenon).

Instantiated relationsfact_relation(explain, 'galilean theory of tides' tides).

Instancesfact_instance_of('galilean theory of tides', theory).


04/19/23 13

Adding time: initial approach

By identifying two forms of time occurring at the relation level:

Repeatable (using time interval)

relation_type(extend, theory, theory, 'time interval').relation_type(explain, model, phenomenon, 'time interval').

Non-repetable (using time point)

relation_type('was born at', person, 'time point').relation_type(invent, person, invention).

What happen if we need to add different refinements (e.g. Location)?

Do we need to consider different relations?


04/19/23 14

Adding time: type/token distinction

Type: abstract entities (e.g. invention)

Token: particular physical manifestation of types (Galilean telescope)

Type-Type (parameters are only types)relation_type(relate, model, 'field of study').

relation_type(extend, theory, theory).

Token-Type (a token and a type)relation_type(invent, person, invention).

relation_type(observe, person, phenomenon).

Token-Token (parameters are only token)relation_type(influence, person, person).

relation_type('work with somebody', person, person).


04/19/23 15

Adding time: Davidson-based approach

Adapting the concept of event by D. Davidson in order totreat time-embedded relations.

class(d_e). % Davidson event: time, place, begin, end, durationclass(time). % for all time related concepts

relation_type(invent, person, invention).

fact_relation(invent, 'Galileo', 'thermometer', d_galileo_invent_thermometer).event_property(begin, d_galileo_invent_thermometer, 1593-00-00).event_property(end, d_galileo_invent_thermometer, 1593-00-00).

fact_relation(invent, 'Galileo', telescope, d_galileo_invent_telescope).event_property(begin, d_galileo_invent_telescope, 1609-07-00).event_property(end, d_galileo_invent_telescope, 1609-11-00).


04/19/23 16

Davidson’s approach: further examples

Treating historical periods as events:fact_relation(hold, 'scientific revolution', d_e_scientific_revolution).event_property(begin, d_e_scientific_revolution, 1543-00-00).event_property(end, d_e_scientific_revolution, 1750-00-00).

Representing two related events:fact_relation(investigate, 'Galileo', sunspot, d_galileo_investigate_sunspot).

event_property(begin, d_galileo_investigate_sunspot, 1612-04-00).event_property(end, d_galileo_investigate_sunspot, 1636-00-00).event_property(location, d_galileo_investigate_sunspot, 'Italy').

fact_relation(observe, 'Galileo', sunspot, d_galileo_observe_sunspot).

event_property(begin, d_galileo_observe_sunspot, 1611-03-12).event_property(end, d_galileo_observe_sunspot, 1611-03-12).event_property(location, d_galileo_observe_sunspot, 'Rome').

event_relation(sub_event, d_galileo_observe_sunspot, d_galileo_investigate_sunspot)


04/19/23 17

Queries and domain specific questions

Query modes (abstract rules)

Concept-based(direct, inferred and indirect relationships).

Relation based(transitive, symmetrical and inverse closures).

Time event-based(inferring and comparing events properties).

Domain specific questions

(exemplification of query modes) Who

(Who influenced P?) What

(What happened between two time points?)

Where(Where was a phenomenon Ph observed?)

When(When did P1 and P2 collaborate?)

Combined(When, where and by whom was

phenomenon Ph observed?)


04/19/23 18

Concept-based mode: instances examples

Queries

a) Direct instances?-fact instance_of(‘Kepler’, C). astronomer mathematician

b) Inferred instances?-inferred_instance(‘Galileo’, C). person

c) Indirect instances?-indirect_instance(microscope, C). event

Inference Rules

a) Directly encoded

b) inferred_instance(X,C):- fact_instance_of(X,C).

inferred_instance(X,C):-

fact_instance_of(X,C1),inferred_subclass(C1,C).

c) indirect_instance(X,C):-

inferred_instance(X,C),\+fact_instance_of(X,C).


04/19/23 19

Relation-based mode: transitive relation example

Queries

?-inferred_transitive_relation(extend, X, Y).

X='galilean theory',Y='kepler theory';X='galilean theory',Y='copernican theory‘; X='copernican theory',Y='brahe theory' ;X='brahe theory',Y='ptolemy theory' ;X='galilean theory',Y='brahe theory' ;X='galilean theory',Y='ptolemy theory‘; X='copernican theory',Y='ptolemy theory‘

?-inferred_transitive_relation(influence, X, Y).

X='Hipparcus',Y='Ptolemy'; X='Kepler',Y='Brahe';X='Copernicus',Y='Kepler';X='Copernicus',Y='Galileo';X='Copernicus',Y='Brahe‘

Indicators (encoded in the ontology)

transitive_relation(influence)

Inference rules(to unify 3 an 4 place relations)

get_relation(R,X,Y):- fact_relation(R,X,Y).

get_relation(R,X,Y):-fact_relation(R,X,Y,_).

(to recursively derive inferred relations)inferred_transitive_relation(R,X,Y):- transitive_relation(R), get relation(R,X,Y).

inferred_transitive_relation(R,X,Y):- transitive_relation(R), get_relation(R,X,Y1),

inferred_transitive_relation(R,Y1,Y).


04/19/23 20

Time-event based mode:arithmetical operators

Queries

Time point before

?-timepoint_before(1633-07-14, 1636-06-18).

?-timepoint_before(1609-07-00, 1609-11-00)

?-timepoint_before(1609-07-19, 1609-07-22).

Time point same

?-timepoint_same(1543-05-10, 1543-05-10).

?-timepoint_same(1543-05-00, 1543-05-10).

?-timepoint_same(1543-0-0, 1543-10-10).

Rules (to compare time points)

timepoint_before(Y1-_-_, Y2-_-_):-Y1<Y2,!.

timepoint_before(Y-M1-_, Y-M2-_):-M1<M2,!.

timepoint_before(Y-M-D1,Y-M-D2):-D1<D2,!.

timepoint_same(Y-M-D,Y-M-D).timepoint_same(Y-M-0, Y-M-_).timepoint_same(Y-0-0, Y-_-_).

O/00= date unknown_= variable which can be instantiate to any

value


04/19/23 21

Time-event based mode:Allen relations To compare events based on

their start and end points

?-happen_before(d_brahe_publish_novastella, E2 ).

E2=d_galileo_write_discourse;E2=d_galileo_publish_assayer;E2=d_galileo_invent_thermometeE2=d_galileo_read_elements;

?-happen_during(d_kepler_publish_harmonice, E2).

E2=d_galileo_investigate_sunspot;

E2=d_e_scientific_revolution

happen_before(E1, E2 ) :- event_property(end, E1, T_E1e ),

event_property(begin, E2, T_E2s ),timepoint_before( T_E1e, T_E2s).

happen_during(E1,E2):- event_property(begin, E1,

T_E1s ),event_property(end, E1, T_E1e), event_property(begin, E2, T_E2s), event_property(end, E2, T_E2e), timepoint_before(T_E2s, T_E1s), timepoint_before(T_E1e, T_E2e).


04/19/23 22

Domain oriented questions: who and what

Queries

Who questions

?-who_influenced('Brahe').['Kepler', 'Copernicus']

?-who_worked_with('Kepler').['Brahe', 'Wallenstein']

• What questions

?-which_theory_extend('galilean theory').

['brahe theory','ptolemy theory']

Rules

(application of inferred_transitive_relation)

who_influenced(P):-

setof(P1,inferred_transitive_relation(influence,P1,P),All),

showlist( All ).

which_theory_extend(T):-setof(X,inferred_transitive_relation(extend, T, X), All ),

showlist( All ).


04/19/23 23

Domain oriented questions: combined examples Involve more than one

type of questions

(when/where)

?-when_where_was_born('Ptolemy').[was born, Ptolemy, [85-00-00,

85-00-00, Alexandria]]

(who/when/where)

?-who_when_where_observed (supernova).

[[observe, 'Brahe', supernova, [1572-00-00, 1572-00-00, Germany]],

[observe, 'Bevis', supernova, [1731-00-00, 1731-00-00, England]]]

Rules

when_where_was_born(P):- fact_relation('was born',

P, E), event_properties_detail(E,P_e), showlist(P_e).

who_when_where_observed(Ph):- findall(P_e,

(fact_relation(observe,P,Ph,E), event_properties_detail(E,P_e)

), All), showlist(All).


04/19/23 24

Use of domain oriented questions

To exemplify the query modes

To verify and expand the ontology:

Syntax errors(mispelled words, inconsistencies in naming relations)

Hierarchical inconsistencies(inconsistent inheritance behaviour)

Ontology population for answer the queries(unpopulated or insufficiently populated part of the ontology e.g. symmetrical relations)


04/19/23 25

Achievements

Development of a three-phase methodology

Development of a framework for adding temporal specification (Davidson's approach)

Development of a corpus of rules for reasoning on the domain (Allen's predicates)


04/19/23 26

Limitations

Methodology scope(applicable in different case studies)

Ontology population(further facts and relations)

Reasoning limitations(additional query modes combining specific rules automatically, systematic approach for identifying domain questions)

Ontology validation(missing validation performed by external experts)


04/19/23 27

Possible enhancements

Application of the ontology scenario (prototype)

Dissemination and interoperability of the data model (e.g. OWL and SPARQL)

Expand time representation Applying interval approach for historical period Improving accuracy in dating mechanism Defining geopolitical naming convention


04/19/23 28

Future work

Extend in a PhD project :

Focus on subjectivity and vagueness in historical domains

Link to ontology-based search in digital libraries


04/19/23 29

Summary The problem

How the use of ontologies can improve access through digital resources in historical domains

Approach and methodology Developing a history ontology by considering existing ontologies,

methodologies and formal representation of temporal information (e.g. Davidson’s and Allen’s approaches).

Outcomes Elaborating a methodology taking into account the double role of domain

expert and ontologist. Temporal framework for adding temporal concepts and reasoning on

events. Limitations

Limited methodology scope Ontology population and validation Reasoning limitations

Future work Vagueness and subjectivity in historical domains Digital library scenario


Documents

Ontology-based Representation and Reasoning about the History of Science