From General Ontology to SpeFrom General Ontology to Specific Ontology:cific Ontology:

Study of Shu-Shi Poems Study of Shu-Shi Poems

Ru-Yng Chang, Sue-ming Chang,

Feng-ju Luo*, Chu-Ren Huang

Academia Sinica, Yuan-Ze University*

Knowledge and Knowledge and Knowledge Structure VariationKnowledge Structure Variation

Knowledge is Structured Information

Most salient factors dictating variations in knowledge structures are time, space, and domain

Language is both the product and conduit of the conceptual structure of its speakers

Accessing Knowledge StructureAccessing Knowledge Structure

In order to become sharable and reusable knowledge, all extracted information must first be correctly situated in a knowledge structure

The situated information must be allowed to transfer from knowledge structure to knowledge structure without losing its meaningful content

Research GoalResearch Goal

Knowledge Structure Discovery– Knowledge as situated information– Language endows information with structure

Text-based and Lexicon-driven Knowledge Structure Discovery– General Ontology: the upper ontology shared

by all domains (such as SUMO)– Specific Ontology: a ontology specific to a

domain, historical period, an author etc.

Research MethodologyResearch Methodology

The Mental Lexicon Approach

The Shakespearean-garden Approach

The Ontology-merging as Ontology-discovery Approach

The Mental Lexicon ApproachThe Mental Lexicon Approach

Concepts are stored in the mental lexicon

The basic unit of mental lexicon organization and access is lexical entry

A complete list of lexical entries covers the complete list of conceptual atoms

Lexical semantic relations mirror conceptual relations

Each Word is a Conceptual Atom

The Shakespearean-garden ApproachThe Shakespearean-garden Approach

A Shakespearean garden collects all the plants referred to in Shakespearean texts.– The garden is used to illustrate the flora of the

Shakespearean England and gives scholars a context in which to interpret his work.

There is a knowledge structure behind each corpus (i.e. a collection of texts with design criteria)

Lexicon as a Structured Inventory of Conceptual Atoms– For instance, complete set of texts by an author, from

a certain period, or in a certain domain

The Ontology-merging as The Ontology-merging as Ontology-discovery Approach IOntology-discovery Approach I

Ontology provides a structure for knowledge to be situatedHowever, there is a dilemma for the construction of a new ontology– If no existing ontology is referred to:

reinventing the wheel, difficult to start a structure from scratch without rules

– If existing ontology is referred to: mislead by existing structure, mismatched or erroneous

The Ontology-merging as The Ontology-merging as Ontology-discovery Approach IIOntology-discovery Approach II

The SolutionMap conceptual atoms to two (or more) reference ontologiesMerge the two resultant ontologies– Matched Mapping: Confirmation of knowledge

structure– Mismatched Mapping: Only one or neither is c

orrect. Possibly lead to discovery of new knowledge structure

– Complimentary Mapping: Increases coverage

Resources usedResources used

WordNethttp://www.cogsci.princeton.edu/~wn/

SUMO Ontologyhttp://www.ontologyportal.org

Academia Sinica Bilingual Ontological Wordnet (Sinica BOW) : SUMO + WordNethttp://bow.sinica.edu.tw

Segmentation Program etc.http://LingAnchor.sinica.edu.tw/

Domain Lexicon Management System:Segmentation, New Word DetectionLexical Database

The information of Sinica BOWThe information of Sinica BOW

EX: fish

Sense

Domain

POS

Definition

Translation

Semantic relation

SUMO

Example

SUMO: SUMO: Suggested Upper Merged OntologySuggested Upper Merged Ontology

SUMO AtomsConcepts: around 1000Note that concepts are not necessarily linguistically realizedRelations (ISA): See SUMO Graph

Axioms: for inference

Open resource created under an initiative from IEEE Standard Upper Ontology Working Group

MethodologyMethodology

From lexicon to ontology (from items to structure)

Ontology discovery through ontology merging

WHY?WHY?

We do not have the knowledge structure (ontology) of a new domain (historical period, field etc.)

But typical ontology discovery needs a framework to be mapped to

To solve the dilemma we map the conceptual atoms to both SUMO and WN (as a linguistic ontology)

How to build a domain ontologyHow to build a domain ontology

Word segmentationWord segmentation

Match WordNet synset and SUMO concept automatically Match WordNet synset and SUMO concept automatically

WordNet

SUMOUse WordNet information to check results and extend concept

Use WordNet information to check results and extend concept

Transform into ontology browser format

Transform into ontology browser format

Distribution of Shu Shi lexiconDistribution of Shu Shi lexicon

98,430 words in NO.1-45 volume

Distribution of concepts in Shu Shi'spoems

plant(1.7393%) animal(1.4294%)

artifact(1.4467%) other(95.3845%)

The distribution of animal, plant, and The distribution of animal, plant, and artifact concepts in Shu Shi’s poems artifact concepts in Shu Shi’s poems

Distribution of plant concepts in ShuShi's poems

body part(3.2126%) fungus(0.0584%)

uncertain(2.278%) alga(0.1168%)moss(1.1682%) reproductive body(0.6425%)

fern(0.2336%) flowering plant (92.2897%)

Distribution of animal concepts in Shu Shi'spoems

arachnid(0.1421%) invertebrate(0.2132%)myriapod(0.0711%) larval(1.2082%)feline(2.7008%) uncertain(0.2843%)canine(2.5586%) carnivore(0.0711%)amphibian(1.6347%) crustacean(1.2793%)insect(8.742%) reptile(5.4726%)hoofed mammal(22.6724%) mammal(1.0661%)worm(0.924%) fish(7.8181%)rodent(2.5586%) bird(37.1002%)aquatic mammal(0.6397%) mollusk(0.7107%)monkey(2.1322%)

Distribution of artifact concepts in ShuShi's poems

transportation device(16.6433%) artifact(32.5140%)fabric(6.1096%) uncertain(6.1798%)icon(0.4916%) mineral(0.9831%)musical instrument(0.6320%) device(10.6039%)engineering component(0.2107%) clothing(20.1545%)currency measure(1.7556%) substance(0.4916%)ordering(0.0702%) weapon(2.5983%)art work(0.4916%) law(0.0702%)

Concepts found in Shu Shi's Concepts found in Shu Shi's but not in Tang 300but not in Tang 300

aquatic mammal (whale 鯨 *)

amphibian (frog 蛙 *、 toad 蟾蜍 *、 salamander鯢 )

mollusk (clam蛤 *、 gastropod螺 *、 oyster蠔、 snail蝸牛 *、 earthworm蚯蚓 *)

crustacean (crab蟹 *、 shrimp蝦 ) Guangdong and Hainan Island

Words stand for multiple concepWords stand for multiple concepts in the Shu Shi Poemsts in the Shu Shi Poems

Source Word SUMO English Example sentence

Shu Shi 杜鵑 bird cuckoo 子規＞和孔郎中荊林馬上見寄＞春山聞子規。啼鴃＞次韻劉景文登介亭＞朝先啼鴃起，

flowering plant

azalea 杜鵑＞菩提寺南漪堂杜鵑花＞南漪杜鵑天下無，

葛 flowering plant

kudzu vine

白葛＞和文與可洋川園池三十首：湖橋＞白葛烏紗曳履行。

fabric kudzu vine

白葛＞病中遊祖塔院＞烏紗白葛道衣涼。

What We Learned about Specific What We Learned about Specific OntologyOntology

Constructing ontology from a larger corpus and comparison of two specific ontologiesLocal information can be effectively mappedGlobal information offers deeper insights into the knowledge structure– Human conceptualization of animals and plants has b

een relatively stable. But NOT artifacts.• Regardless of the criteria for classification, genetically deter

mined features (behaviors, appearances etc.) do not vary greatly

• However, human technology is highly fluid. Our conceptualization of artifacts is highly dependent on the development of engineering and by our varying societal needs.

http://bow.sinica.edu.tw/ont/ShuShi_ont.htmlhttp://bow.sinica.edu.tw/ont/ShuShi_ont.html

Example of SUMO conceptExample of SUMO concept

Axiom in SUMOAxiom in SUMO(instance GeorgeBush Human) – GeorgeBush is an instance of the cla

ss of humans

(exists (?X) (parent ?X GeorgeBush)) – there exists something of which George B

ush is the parent

(instance parent BinaryPredicate) – the relation of parent is a binary relation

(domain parent 1 Organism) – the first argument to the parent relation must be an instance of the class Organism

(domain parent 2 Organism) – similarly for the second argument

Example of WordNet lexical relatiExample of WordNet lexical relationon

CuckooCuckoo

cuculiform_birdcuculiform_bird

aniani roadrunnerroadrunner coucalcoucal

birdbird

pheasant_coucalpheasant_coucalCentropus_sinensisCentropus_sinensis

azaleaazalea

rhododendronrhododendron

shrubshrub bushbush

杜鵑DuJuan

SUMO + WordNetSUMO + WordNet

SUMO WordNet

CuckooCuckoo

cuculiform_birdcuculiform_bird

aniani roadrunnerroadrunner coucalcoucal

birdbird

pheasant_coucalpheasant_coucalCentropus_sinensisCentropus_sinensis

azaleaazalea

rhododendronrhododendron

shrubshrub bushbush

organismorganism

animalanimal

vertebratevertebrate

birdbird

plantplant

invertebrateinvertebrate

warm blooded vertebratewarm blooded vertebrate

Flowering plantFlowering plant

mammalmammal

杜鵑DuJuan

Summary and Future WorkSummary and Future Work

Ontologies represent the knowledge structure of a domain or historical period

We have provided an online interface to browse ontologies and lexica

In the future, we will complete the online ontology editor and browser, which will

– Map lexicon, WordNet and SUMO.– Integrate ontologies based on different texts.– Facilitate comparative studies of various domain ont

ologies.

Towards a Workbench for Specific Towards a Workbench for Specific Ontology: BrowserOntology: Browser and Editor and Editor

User loginUser login

Function menu(Personal ontologies list)

Function menu(Personal ontologies list)

Browse an ontologyBrowse an ontology Edit an ontologyEdit an ontology Add an ontologyAdd an ontology

1. SUMO2. SUMO + WordNet +concept map with l

exicon

1. SUMO2. SUMO + WordNet +concept map with l

exicon

LogoutLogout

1. Update lexical concepts

2. Update mapping between WordNet synset and lexicon

3. Edit other information in lexicon

1. Update lexical concepts

2. Update mapping between WordNet synset and lexicon

3. Edit other information in lexicon

Import textImport text Import lexiconImport lexicon

Word segmentationWord segmentation

Match concept and synset automatically

Match concept and synset automatically

1. Suggestion list2. Missing list

1. Suggestion list2. Missing list

Constructing a Specific Constructing a Specific OntologyOntology

Import text, or domain lexicon – Select style of writing– Select category of word list for word segmentation– Select reference ontologies to match SUMO and lexi

con

Information of suggestion list– Candidate synset– Candidate synset synonyms– Explanation of candidate synset– Concept of candidate synset