D08.3 Domain Extension Central Ontology.v3

7/29/2019 D08.3 Domain Extension Central Ontology.v3

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Domain extension of the central

ontology - final

DELIVERABLE 8.3

Version 111/Feb/11

FINAL

Amanda Hicks, BBAWPiek Vossen, VU University Amsterdam

Egoitz Laparra, EHUGerman Rigau, EHU

Knowledge Yielding Ontologies for Transition-based Organization

ICT 211423

Co-funded by EU - FP7 ICT Work Programme 2007 under Challenge 4 - Digital libraries and Content

Objective ICT-2007.4.2 (ICT-2007.4.4): Intelligent Content and Semantics (challenge 4.2).


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Grant Agreement No. ICT 211423

Project Acronym KYOTO

Project full title Knowledge Yielding Ontologies forTransition-based Organization

Technologies

Funding Scheme FP7 ICT

Date latest version Annex I 19-12-2007

Project Coordinator Prof. Dr. Piek T.J.M. VossenVU University AmsterdamTel. + 31 (0) 20 5986466Fax. + 31 (0) 20 5986500

Email: [email protected]

Project website http://www.kyoto-project.eu/

Deliverable Document Number D8.3

Status Final

Security (distribution level) Public

Contractual date of delivery August, 31, 2009

Actual date of delivery October, 10, 2009

Type report

WP contributing to the deliverable WP8

WP responsible ECNC, Lawrence Jones-WaltersAuthors Amanda Hicks

Piek Vossen

Egoitz LaparraGerman Rigau

EC project officer Werner Janusch

Keywords Conceptual modeling, domain

wordnets, domain ontologies

Abstract This document describes theextension of the central ontology tomake it adaptable for domains.
mailto:[email protected]:[email protected]


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Table of Contents

1Introduction....................................................................................................................................42The KYOTO knowledge architecture............................................................................................ 63The KYOTO ontology................................................................................................................... 9

3.1Modeling Antonyms.............................................................................................................103.1.1Modeling Complementaries......................................................................................... 103.1.2Modeling Scalar Antonyms ......................................................................................... 113.1.3New qualities and quality regions................................................................................ 11

3.2Incorporating Verbs and Adjectives or Modeling perdurant and qualities........................... 113.2.1New relations (object properties) in Kyoto 3............................................................... 113.2.2Lexicographic Files...................................................................................................... 123.2.3Arranging Verbs and Adjectives in Perdurant and Quality Hierarchies....................... 12

4WordNet to ontology mappings................................................................................................... 144.1Introduction.......................................................................................................................... 144.2Connecting Nouns and Verbs............................................................................................... 14

4.2.1agent............................................................................................................................. 164.2.2body-part ......................................................................................................................174.2.3by-means-of..................................................................................................................17

4.2.4Destination ...................................................................................................................194.2.5event ............................................................................................................................ 204.2.6instrument ....................................................................................................................204.2.7location ........................................................................................................................ 204.2.8material ........................................................................................................................ 214.2.9property ........................................................................................................................224.2.10result .......................................................................................................................... 224.2.11state ............................................................................................................................234.2.12undergoer ...................................................................................................................244.2.13Uses............................................................................................................................ 244.2.14Vehicle........................................................................................................................ 26

4.3Connecting Adjectives......................................................................................................... 265Procedure for adaptation to a domain.......................................................................................... 30

5.1Building a domain hierarchy of concepts.............................................................................305.2How to decide where to start................................................................................................375.3Mapping the domain wordnet to the ontology..................................................................... 395.4Integration of the domain wordnet into the KYOTO system...............................................46

6Conclusions..................................................................................................................................477References....................................................................................................................................48


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1 Introduction

The goal of the KYOTO project (ICT-211423) is to develop an information and knowledgesharing system that relates text in various languages to a shared ontology in such a waythat it enables the extraction of deep semantic relations and facts from text in a domain.The system should establish communication and interpretation across languages andcultures, and it should support building and maintaining the system by groups of people in a

shared domain and area of interest.

Most domain acquisition systems in the semantic web community model each domainseparately and restrict the system to a single language or a limited set of languages. Theyalso require knowledge engineers and language-technology experts to do the modelling. TheKYOTO system, on the other hand, is specifically designed to build global and cross-culturalconsensus on the meaning and interpretation of language. As such it is an open system thatcan be extended and maintained by the users themselves without requiring skills inknowledge engineering or language technology.

The system behaves similarly to Wikipedia - it allows specific social groups to agree on theinterpretation and meaning of the concepts that matter for them. Nevertheless, theinterpretation and meaning definitions are formalized in such a way that computer programscan use these definitions to mine the text provided by the same groups on the samematters. The process of acquiring this knowledge is further supported by automatic miningof terms and concepts from text documents that are provided by the users. A special editingenvironment helps the users to select and define the terms that are relevant withoutneeding to know the formal knowledge structures that underlie these definitions. The resultis a domain wordnet for their domain.

In order to further formalize the meaning of these terms and to share these concepts acrosslanguages and cultures, the editor also prompts these users for more formal constraints andrelations. Again, the editor uses suggestions coming from the automatic acquisition andhides the complex knowledge structures. This leads to a domain model that is available toother participants, e.g. defining terms in another language, possibly based on acquisition

from documents in these other languages. Cross-lingual and cross-cultural validation isestablished through agreeing and sharing the domain terms that are mapped to a centralontology.

Once the domain wordnet is established, users in the community can specify the types ofrelations and facts that they would like to extract from text documents in the domain. Theyselect examples of facts from documents or abstract relations from the ontology to specify aconceptual pattern. Text mining then involves processing the documents in terms of theirlinguistic structure and scanning the linguistic patterns for detecting the terms and relationsas they are modeled in the domain wordnet and the ontology.

The conceptual knowledge of the domain (both the wordnets and the shared ontology) isanchored to generic wordnets and a generic ontology. This helps building up the definitions

and relations for the domain resources, i.e. not all knowledge needs to be re-defined fromscratch, but this also makes it possible to share the knowledge with people outside thecommunity. The conceptual knowledge built up in this way is thus shared across languageswithin the social groups and with other social groups. This is only possible if the domainknowledge is anchored to a general language and knowledge repository.


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The modeling of a domain is done through building a domain wordnet and not by extendingthe ontology as such. The main motivation for this is as follows:

1. maintaining the ontology itself is considered to be too complex for domain expertsthat are not trained in knowledge engineering

2. the ontology does not need to store formal definitions of all domain concepts in orderto facilitate the extraction of facts and knowledge from text

With respect to the latter point, we defined a 3-layered knowledge model that connects

background vocabularies, term databases and the domain wordnets to generic wordnetswhich are mapped to the central ontology. This model allows us to define the relations andproperties that matter for mining facts in the ontology, rather than populating the ontologywith large class hierarchies.

Instead of extending the ontology to the domain, we adapted the ontology to modelrelations and properties that are expressed in facts in general. Furthermore, we developedheuristics for extending the mapping relations from the English Wordnet to the ontology.Currently, all the synsets in the English WordNet have been mapped to the ontology,guaranteeing large coverage of concepts. In combination with the option to automaticallyderive a term database for any domain, this means that the KYOTO system can now beapplied to any domain. The WordNet to ontology mapping system also generated many newrole relations. These are important to interpret the roles of entities as expressed in text.Again, this adds expressive power to KYOTO as a generic system.

The modeling of a specific domain can now be done through the building of a domainwordnet from the automatically derived term database, where especially the so-called non-rigid concepts need to be mapped to the relations and properties in the central ontology.There is no need to adapt the ontology as such.

In this deliverable, we describe the work carried out to make the knowledge-databaseusable for any domain. In the next section, we give an explanation of the 3-layeredknowledge structure and the motivation. In section 3, we describe the adaptations to theontology to make it more clear and complete for detecting participant relations andprocesses in text mining. In section 4, we describe how we mapped WordNet 3.0 to the

central ontology. In section 5, we describe what steps are needed to use the genericarchitecture for any domain.


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2 The KYOTO knowledge architecture

As was explained in the KYOTO deliverable D08.1/D08.2, the design of the knowledgerepository combines large volumes of background knowledge and term databases withdomain wordnets that are linked to a central ontology. The semantic load of the databases islikewise spread over three layers of knowledge. The details of this model and the motivationare explained in (Vossen and Rigau 2010). We will give here a small excerpt from this paper.

The following knowledge repositories are relevant for building up the domain model:

Wikipedia: by September 2009 it has more than 3 million articles in English andlarge volumes in other languages: http://www.wikipedia.org/.

DBPedia: by September 2009 it has 2.6 million things, including at least 213,000persons, 328,000 places, 57,000 music albums, 36,000 films, 20,000 companies.The knowledge base consists of 274 million pieces of information (RDF triples):http://dbpedia.org/About.

GeoNames: by September 2009 it has eight million geographical names and consistsof 6.5 million unique features whereof 2.2 million populated places and 1.8 millionalternate names. All features are categorized into one out of nine feature classes and

further subcategorized into one out of 645 feature codes:http://www.geonames.org/about.html. The Species 2000 database with 2.1 million species, having taxonomic relations and

labels in many different languages: http://www.sp2000.org/. A term databases with about 500,000 terms per 1,000 documents in each language Generic wordnets in each language ranging from 50,000 to 120,000 synsets Existing ontologies such as the EuroWordNet top-ontology (Vossen 1998), SUMO

(Niles and Pease 2002) and DOLCE (Masolo et al 2003).

The amount and complexity of the knowledge repository is enormous. The initial Kyotoarchitecture suggests that the wordnets extended with the domain vocabulary will becomeanchored through the domain extension of the ontology. In practice this means, that theontology needs to be extended with millions of new concepts. For example, the ontologymakes a distinction between taxonomic groups and organisms. Instances of species aremembers of a taxonomic group and instances of an organism. Likewise, we can predict thatif an instance ceases to exist, it is not implied that the taxonomic groups ceases to exist.The latter is only the case when all members cease to exist. As a consequence, the ontologythat represents all concepts in this domain should include all 2.1 million species twice!

Such a model leads to various practical problems. First of all, ontologies of that size cannotbe loaded in any existing inferencing system. Inferences as the above can thus not be madebecause of the size of such an ontology. Another problem is that the vocabularies arelinguistically too diverse. Whereas the species can be considered as rigid concepts, asdefined by Welty and Guarino (2002), this is not the case for most of the terms that arelearned from the document collection. In the environment domain, the documents typically

include terms for roles of species rather than the species as such, e.g. invasive species,migratory species, threatened species. For mining facts from documents, these non-rigidrole terms have more information value than the defining properties of the species.

For a knowledge sharing system it is thus more important to precisely define what the rolesand processes are in which species participate than to provide the defining properties of thespecies as such. Likewise, we propose a model of division of knowledge labor along the linesof the division of linguistic labor defined by Putnam (1975). Putnam argues that linguisticcommunities rely on the fact that experts know the defining properties of natural kind termssuch as goldand can thus determine which instances of matter are gold and which are not.Most natural language users therefore have a shallow definition of what gold is and can stilluse this definition to communicate valuable information about gold in order to accomplish


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specific tasks, such as trading gold or buying jewellery.

Along the same lines, we propose a digital version of this principle, where we state that acomputer does not need to know the defining properties of each rigid term but can rely onthe capacity of the domain expert to determine what the instances are of, for example, aparticular species. Vast amounts of words for rigid concepts can likewise remain in thevocabularies as long as we indicate their status as rigid concepts. It is more useful toproperly represent the roles and processes in which the rigid things participate. These needto be represented both in the vocabularies and to some extent in the ontology to be able to

process information in a proper way and to carry out the necessary inferencing.

In Figure-1, we show an example of the three layers in the model. We will thus have vastquantities of species represented as a SKOS hierarchy in Virtuoso. The species hierarchy ispartially linked to the generic wordnet. SPARQL queries can be used to extract thehierarchical relations to find the most specific specific matching wordnet synset. Thewordnet synset hierarchy can be traversed to find the most specific base concept that ismatched to the ontology. In this way, we can infer for all species in Virtuoso that they are akind oforganism. This in turn allows us to infer additional information that is located in theontology about specific organism, for example, that it has a gender and an age.

In addition, terms from the term database are mapped to the most specific synset as well.In this example, we see typical role concepts as terms. For these role concepts, we inferthat they do not represent subtypes but can be used to refer to instances of concepts thatplay a specific role. The role relation to the process needs to be defined more specificallythrough a mapping relation with the ontology.

Figure-1: 3-layered division of knowledge


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The model assumes that rigid concepts in a domain can be linked to their proper ontologyclasses through a mapping to the generic wordnet and do not need to be defined formally.The model expresses that these concepts are proper subclasses of the ontological class andare differentiated by the subclass hierarchy in the vocabulary and in the generic wordnet. Ifwe come across the terms Eleutherodactylus atrabracus and Eleutherodactylus augusti in atext, the system only needs to know that they are subclasses of frogs, where the latter is aconcept in the generic wordnet, which is a rigid subtype of the ontology class organism.

Knowing the ontology class and the fact that they are proper (rigid) subtypes defines theway we extract knowledge.

This is different for non-rigid concepts such as endangered frogs. This non-rigid conceptapplies to instances of the class frogs but the role in the process of getting extinct is whatmatters. Likewise, it is sufficient to link them to the class of frogs as instances and indicatethe implication of the role.

This design of the knowledge repository has important consequences for modeling theknowledge in any domain:

1. Background vocabularies can be automatically aligned to given wordnets;2. Large volumes of concepts can remain in the background repositories and do not

need to be added to the domain wordnet by editing;3. The domain wordnet can be built up by making selections of concepts from any

repository that matters for the modeling and mining of knowledge from text;4. Modeling the processes and states in which species are involved is more important

than modeling all species in the ontology;5. The non-rigid concepts in the domain wordnet need to be mapped to the relevant

processes and states in the ontology rather than extending the ontology as such;

In the next sections, we explain how we created a generic knowledge layer that willfacilitate the use in any domain through light-weight customization.


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3 The KYOTO ontology

KYOTO should be able to accommodate changes in scientific theories as both the world andour knowledge of the world change. We, therefore, require an ontology that is notidiosyncratic but rather one that can accommodate (1) a variety of languages and theirwordnets, (2) a variety of scientific domains other than ecology, (3) a variety of researchcommunities, (4) future research in these domains, and can (4) serve as the basis of sound,

formal reasoning. This means that the major role of the ontology in the KYOTO project is toprovide a coherent, unified, stable frame of reference for different cultural and linguisticcommunities as well as different research communities. In order to do this, the ontology willdeal with very general and abstract concepts in a clear, consistent manner that followsrigorous and explicit criteria.

The KYOTO ontology has three distinct levels and is based on the DOLCE-Lite-Plus (DLP).DLP is a top-level or upper ontology, which means that it models highly abstract conceptssuch as objects, events, and qualities. In a previous stage of the project we modified DLP tocreate a top-level ontology suitable for KYOTO. The middle level contains concepts thatconnect domain specific terms to concepts in the upper ontology. This includes:

Base Concepts Units of measurement, and other qualities Perdurant terms (processes and states)

often more general than domain terms often identified with a synset from WordNet 3.0, not exhaustive of terms that would appear in a domain neutral ontology.

Endurant terms (objects and substances) Regions and qualities that model adjectives

There are a few concepts in the middle level ontology that are slanted towards the ecologydomain, e.g., things-organisms-do. These are present in the middle level ontology for tworeasons: 1) we do not want the users to be able to edit these concepts. 2) a small numberof domain specific concepts can serve as illustrations or templates for modeling choices.

The least abstract level of the ontology is the domain specific level. This contains terms andconcepts that are pertinent to ecology. KYOTO-3 contains domain specific concepts thatcorrespond to nouns, verbs, and adjectives found in the literature for ecology. Most of theseterms were extracted from user documents or provided from the users themselves. Someother terms were added in the process of developing the ontology for the sake of enrichingrelations amongst concepts.

The KYOTO-1 Core ontology focussed mainly on mapping endurants. At the ontological level,we focussed on the distinction between types and roles. We extended DLP with BaseConcepts taken from the noun portion of WordNet and likewise, the domain relevant termswere endurants: namely, species and regions.

The work done for extending KYOTO-1 to produce KYOTO-2 focussed on modellingprocesses and events (perdurants, in DOLCE lingo) as well as qualities. In this stage ofontology development, we have taken a bottom up approach, focusing on the concepts thathave been identified by the users to be key concepts pertaining to estuaries. With the aim ofmaking modelling choices that are both ontologically precise as well as relevant to domainand application concerns, we have focused on adding concepts that serve as a bridgebetween the domain specific concepts that ecologists have identified as key and the highlygeneral concepts in DLP. While portions of the resulting ontology are specific to the domainof ecology, other portions are still general enough to be domain neutral. In this phase ofontology development we have separated the ontology into a top, middle and domain levelin order to isolate those parts of the ontology that are too domain specific to be of generaluse.


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The work to extend the KYOTO-2 ontology to produce KYOTO-3 focused on enriching theway we model processes, events and qualities. This phase has three distinct parts. The firstis to determine how to model antonyms amongst the adjectives that are represented in theontology. The second is to determine how to model relations between verbs and adjectivesthat have been added to the ontology, and the third is to determine how to arrange theverbs and adjectives in a hierarchy. The ontology is freely available from the KYOTOwebsite.

The KYOTO 1 Core ontology focussed mainly on mapping endurants. At the ontologicallevel, we focussed on the distinction between types and roles. We extended DLP with BaseConcepts taken from the noun portion of WordNet and likewise, the domain relevant termswere endurants: namely, species and regions.

The work done for extending KYOTO 1 to produce KYOTO 2 focussed on modelling processesand events (perdurants, in DOLCE lingo) as well as qualities. In this stage of ontologydevelopment, we have taken a bottom up approach, focusing on the concepts that havebeen identified by the users to be key concepts pertaining to estuaries. With the aim ofmaking modelling choices that are both ontologically precise as well as relevant to domainand application concerns, we have focused on adding concepts that serve as a bridgebetween the domain specific concepts that ecologists have identified as key and the highlygeneral concepts in DLP. While portions of the resulting ontology are specific to the domainof ecology, other portions are still general enough to be domain neutral. In this phase ofontology development we have separated the ontology into a top, middle and domain levelin order to isolate those parts of the ontology that are too domain specific to be of generaluse.

The work to extend the KYOTO 2 ontology to produce KYOTO 3 focused on enriching theway we model processes, events and qualities. This phase has three distinct parts. Thefirst is to determine how to model antonyms amongst the adjectives that are represented inthe ontology. The second is to determine how to model relations between verbs andadjectives that have been added to the ontology, and the third is to determine how toarrange the verbs and adjectives in a hierarchy.

3.1 Modeling Antonyms

We distinguish between two kinds of antonyms in the KYOTO-Ontology, scalar antonyms andcomplementaries. Both types of antonyms are represented without introducing newrelations into the ontology.

3.1.1 Modeling Complementaries

Complementaries such as true and false are modeled using a disjoint axiom and a coveringaxiom.

true disjoint falsetruth_value superclass true or false

While complementaries are typically binary, according to this way of modeling them, theydon't have to be. The crucial feature is that the covering axiom needs to exhaust all of thepossibilities for qualities of a particular type, in this case, that truth_value is exhausted bytrue and false.

The two axioms, then, expresses that nothing is both true and false and that true and falseare exhaustive of all truth values.


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Terms that were previously modeled as binary qualities in KYOTO 2 are now modeledaccording to this method of modeling complementaries. Consequently, binary_qualityandthe associated classes have been removed from the ontology.

3.1.2 Modeling Scalar Antonyms

Scalar antonyms admit of degrees and the terms are not exhaustive; they are modeled withthe following kind of assertion:

long disjoint short

which expresses that nothing is both long and short (of course, this assumes that this isrelative to a single point of reference for each instance.)

According to this method of modeling scalar antonyms, they are not modeled as polaritieson a scale. Instead they are modeled as non-overlapping and regions in the same region-space; between these regions (e.g., long and short) lies a region that does not overlapeither of the two regions. For example, if we arrange specimens of beetles according to size,instead of regarding "large" as a point at the end of a scale "size" and saying that the sizessome of the beetles is nearer to "large" than others, we regard "large" as an extendedregion on the scale so that the sizes of some of the beetles actually occupy that region, i.e.,

the beetles are large. The motivation for conceiving of scalar antonyms in this way issimply that it is in keeping with the unique way that DOLCE models qualities using qualityregions (Masolog et al. 2002).

3.1.3 New qualities and quality regions

33 new regions were added to the ontology in order to cover more cases of antonyms. Themajority of these were added to the ontology as opposites of adjectives that were alreadyrepresented in the ontology. Some were also added to impose structure on the regionhierarchy as well as to have a bearer for the covering axioms used in modelingcomplementaries.

3.2 Incorporating Verbs and Adjectives or Modelingperdurant and qualities

The second task in developing KYOTO 3 was to relate perdurants and qualities/regions.

3.2.1 New relations (object properties) in Kyoto 3

Five new relations were added to the KYOTO 3 ontology in order to accommodate

adjectives, and model qualities in the KYOTO 3 ontology and exhibit their relation toperdurants.

Every genuine change is the change of some quality that inheres in an entity. Accordingly,we have added relations that express that a quality has a particular value before a particularchange occurs and conversely, and also that a quality has a particular quality after a changeoccurs. For example, death has the initial-quality alive and the end-quality dead.Although the names of the properties suggest that it relates qualities such as color orheight, they actually relate quality regions such as red and 2 feet.

The relation pertinent-quality was added to relate perdurants to qualities proper. Everychange has a pertinent-quality whose value change, though sometimes the possible values


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are not explicit in the ontology. For example, lengthen involves a change of the qualitylength, but we could not encode all possible lengths in the ontology. Instead we representthat lengthening involves a change of length using the relation pertinent-quality.

Finally we introduce the use of the DLP relation r-location for the first time in the KYOTOontology. Scalar antonyms are modeled in the KYOTO 3 ontology, not as end points on acontinuum, but instead as regions on the continuum. For example, "tiny" is modeled as asub-region of "small".

As shown in Table 1, to date, 65 explicit axioms have been added to model adjectives andtheir relations to verbs. These 65 axioms generate 394 additional restrictions based oninheritance relations.

Domain Range Example Number of

ExplicitClass

Axioms

Number of

InheritedCases

initial-

quality

change region death initial-quality somealive

17 164

initial-

quality-ofregion change not yet in use 0 0

end-quality change region death end-quality somedead

26 173

end-quality-

of

region change dissolvedend-quality-ofsome change

7 0

pertinent-

quality

perdurant quality lengthenpertinent-quality some

length

12 57

r-location region region tiny r-locationssome small

3 0

Total 65 394

Table 1: axioms added to model adjectives and their relations to verbs.

3.2.2 Lexicographic Files

The Lexicographic files from the verb portion of WordNet 3.0 were added to the ontologyunder perdurant. Approximately, 115 verbal Base Concepts have additional axioms. TheLexicographic files from the verb portion of WordNet 3.0 were added to the ontology under

perdurant. Then each Verbal Base Concept was assigned a more specific superclasswherever possible.

3.2.3 Arranging Verbs and Adjectives in Perdurant and QualityHierarchies

The current state of ontology development does not contain much literature regarding


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perdurant hierarchies. Much of the literature in ontology regarding processes and events isnot geared toward hieararchies in particular. For example, Sowa discusses different kinds ofprocesses; the various properties of processes that he describes are more easily applied toindividual events and processes than to process and event kinds

Similarly, Galton and Mizoguchi discusses choices and considerations in modeling perdurantsat the most general level; however, they ultimately propose a specific kind of top-levelontology rather than proposing relevant criteria for articulating specfic perdurants at themiddle and domain levels.

Given the state of the art, we extended the perdurant and quality hierarchies by exploitingthe information already contained in the quality and endurant hierarchies. In particular,qualities are organized according to the kind of entity in which it inheres. Changes andstates are organized according to their pertinent quality.

All change involves change of a quality, e.g., change of color, change of size, change oflocation. Accordingly, we classify changes according to the kind of quality that undergoesthe change. For example, cleanse is subsumed under physical-change since what is alteredin any instance of cleansing are physical qualities. Approximately 120 out 135 descendentsof change were reclassified in the new hierarchy.

Similarly, states typically involve the permanence of some quality or other. Accordingly, weclassify states according to the kind of quality that is pertinent to a given class of states.For example, consist is subsumed by physical-object-state since any instance of consisting,such as a tables consisting of wood, is such an instance because of a quality that inheres ina physical object, e.g., the woodenness of the table. Dispositional states and relationalstates were also added to the hierarchy. Dispositional states, such as accommodate, arestates that an object is in by virtue of having a dispositional property, i.e., a property that islatent in the object but becomes manifest under certain conditions. Fragile is a dispositionalproperty since a glass is fragile even when it does not break. A room that accommodates100 people can be said to accommodate 100 people even when it is empty, so it is classifiedunder dispositional_state. Another class that was created in the state hierarchy isrelational_state, which subsumes classes such as adjoin or separate. Relational states arestates that an object is in due to its relation to some other object or objects. Approximately

100 out of 123 states were reclassified in the new hierarchy.Finally the quality hierarchy was extended according to the kind of entity a quality inheresin. For example, organism-quality subsumes all qualities that are inherent in only organism,such as age and gender. organism-quality, itself is subsumed by physical-object-qualitysince organisms are physical objects. Evaluative and dispositional were also added as directdescendents of quality. Evaluative subsumes qualities that refer to some kind of value suchas importance or goodness. Dispositional subsumes dispositional qualities such as ability (aperson may have the ability to sing even when she is not singing). Approximately 80 out of90 descendent classes of quality were reclassified in the new hierarchy, and approximately250 out 297 descendent classes of region were reclassified in the new hierarchy.

In total approximately 28% of the classes KYOTO 3.2 has been reclassified in light of the

extended hierarchies.


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4 WordNet to ontology mappings

4.1 Introduction

KYOTO released new WordNet to Ontology mappings. This release now includes new tables

of the synset to Base Concept mappings (96.328 records), synset to ontology mappings(179.797 records), and explicit ontology mappings (27.983 records). The release is freelyavailable from the Kyoto ftp site.

With this new release, a substantial improvement on the WordNet to Ontology mapping hasbeen carried out. Past versions of the mapping were centered on nouns only. The newversion of the mapping now includes new mappings for all WordNet verbs and adjectives.That is, the current mapping provides a complete mapping for all nominal, verbal andadjectival parts of WordNet.

Together with the KYOTO ontology, the English WordNet mappings provide a extremely richand powerful basis for semantic processing of text in any domain. Through the equivalencerelations of wordnets in other languages to the English WordNet, this semantic frameworkcan also be applied to the other languages. This provides a common framework for semanticprocessing of text for all the languages.

In order to establish appropriate connections to the Kyoto ontology for adjectives, we alsouse the Princeton semantic relations to connect adjectives to its more appropriate noun orverb concepts. We map each adjective as sc_qualityOf to the type corresponding to theselected noun or verb, and we also map each adjective as sc_subclassOf to the"Kyoto#quality-eng-3.0-04723816-n" type.

4.2 Connecting Nouns and Verbs

As in previous versions we followed a semi-automatic approach to create a new version ofthe Ontology and to create a new WordNet to Ontology mapping. Firstly, we derived thecomplete mapping by exploiting the Base Concepts1 (Izquierdo et al. 20073). This time, wealso used the verbal 578 Base Concepts (BCs) which have been obtained by using allrelations and having at least 50 descendants. These 578 BCs have been incorporated asnew types into the Kyoto ontology below its corresponding WordNet lexicographer's files.The 15 lexicographer's files have been included as classes in the Kyoto ontology assubclasses of perdurant. Secondly, all verbal WordNet concepts have been mapped to theircorresponding ontological types. All verbal concepts corresponding to a verbal Base Concepthave been aligned through the sc_equivalentOf relation. The remaining verbal conceptshave been mapped as sc_subclassOf to the verbal class in the ontology corresponding tothe its verbal Base Concept.

We also created additional mappings for those nominal concepts connected by morpho-semantic links to events. We used the WordNet morpho-semantic database2 to create a setof rule-based heuristics in order to derive additional connections among nominal conceptsfrom WordNet and event types in the Kyoto ontology. To distinguish among potentialcandidate connections, these rules use as a background knowledge the EuroWordNet TopOntology3 (lvez et al. 2008). We use the EuroWordNet Top Ontology (TO) and the WordNethierarchy to test for basic semantic properties for the nouns and verbs.

WordNet 3.0 contains derivational links connecting noun and verb senses, e.g. between

1 http://adimen.si.ehu.es/web/BLC2 http://wordnetcode.princeton.edu/standoff-files/morphosemantic-links.xls3 http://adimen.si.ehu.es/web/TCO


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cannibal#n#1 and cannibalise#v#2. WordNet morpho-semantic database also includes thesemantic type of the relationship (e.g. agent). The database uses 14 semantic relationsbetween a verb and a noun. We provide below an example and relative frequency for eachof them.

agent (3,043 occurrences)cannibalise%2:34:00:: 01162291-v eat human fleshcannibal%1:18:00:: 09891079-n a person who eats human flesh

body-part (43 occurrences):abduct%2:35:00:: 01449427-v pull away from the bodyabductor%1:08:00:: 05291010-n a muscle that draws a body part away from ...

by-means-of (1,273 occurrences):approbate%2:32:00:: 00803815-v approve or sanction officiallyapproval%1:10:00:: 06686736-n a message expressing a favorable opinion ...

destination (17 occurrences):patent%2:41:00:: 02445356-v grant rights to; grant a patent forpatentee%1:18:00:: 10405410-n the inventor to whom a patent is issue

event (8,158 occurrences):intensify%2:30:01:: 00227165-v make more intense, stronger, or more ...intensification%1:04:00:: 00374224-n action that makes something stronger or ...

instrument (813 occurrences):accelerate%2:30:01:: 00439343-v cause to move faster accelerator%1:06:02:: 02670683-n a pedal that controls the throttle valve...

location (288 occurrences):hospitalize%2:40:00:: 02348927-v admit into a hospitalhospital%1:14:00:: 08054417-n a medical institution where sick or ...

material (114 occurrences):

rubberize%2:30:00::00136254-v coat or impregnate with rubberrubber%1:27:00:: 15006258-n an elastic material obtained from the ...

property (318 occurrences):beautify%2:42:00:: 02748927-v be beautiful to look atbeauty%1:07:00:: 04683814-n the qualities that give pleasure to the ...

result (1,439 occurrences):acquit%2:32:00:: 00904046-v pronounce not guilty of criminal charges ...acquittal%1:04:00:: 01193886-n a judgment of not guilty

state (528 occurrences):survive%2:42:02:: 02619924-v continue in existence after (an ...

survival%1:26:00:: 13962166-n a state of surviving; remaining alive

undergoer (878 occurrences):remit%2:40:00:: 02255081-v send (money) in paymentremittal%1:21:00:: 13299453-n a payment of money sent to a person in a ...

uses (740 occurrences):signalize%2:40:00:: 02296984-v provide with traffic signalssignal%1:10:00:: 06791372-n any nonverbal action or gesture that ...

vehicle (87 occurrences):fight%2:33:01:: 01091427-v fight against or resist strongly


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fighter%1:06:00:: 03335030-n a high-speed military or naval airplane

For each semantic relation we created an heuristic to establish a connection for each synsetto its corresponding ontological type. The heuristics perform a validation test regardingsemantic properties for both nominal and verbal synsets. These properties are obtainedfrom available semantic resources such as the EuroWordNet Top Ontology (TO-test) orWordNet 3.0 (WN-test). We use the EuroWordNet Top Ontology to test for their static ofdynamic attributes. We also use the hierarchical WordNet structure to test for endurant orperdurant properties4.

Following (Verdezoto and Vieu 2011) we used the following equivalences:

endurant = physical_entity%1:03:00 \ process%1:03:00perdurant = process%1:03:00 U event%1:03:00 U state%1:03:00

We also add the following equivalences:

static = state%1:03:00::dynamic = process%1:03:00:: U event%1:03:00::instrument = instrumentation%1:06:00::object = object%1:03:00::substance = substance%1:03:00::person = person%1:03:00::

4.2.1 agent

Condition: the verb synset of the relation has to be dynamic (TO).

Example:01162291-v cannibalise --agent--> 09891079-n cannibal01162291-v cannibalise --BCverb--> 01156834-v consume09891079-n cannibal --BCnoun--> 00007846-n person

Direct connections to its Base Concept types:

eng-30-01162291-v sc_subClassOfKyoto#consume__ingest__take_in__take__have-eng-3.0-01156834-v

eng-30-09891079-n sc_subClassOfKyoto#person__individual__someone__somebody__mortal__soul-eng-3.0-

00007846-n

Additional inferred relations to its Base Concept types:

eng-30-01162291-v sc_hasParticipantKyoto#person__individual__someone__somebody__mortal__soul-eng-3.0-

00007846-neng-30-01162291-v sc_hasRole done-byeng-30-09891079-n sc_participantOf

Kyoto#consume__ingest__take_in__take__have-eng-3.0-01156834-veng-30-09891079-n sc_playRole done-by

That is, the verb cannibalise has a typeperson as a participant and the the verb also has adone-by role (agent). Conversely, the noun cannibal is participant of a type consumeplaying the done-by role (agent).

From a total of 3,043 agent relations, 573 relations (19%) do not pass the test and are not

4 In some cases we could also use the KYOTO ontology itself.


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connected to the ontology.

4.2.2 body-part

Condition: No test.

Example:01449427-v abduct --body-part--> 05291010-n abductor

01449427-v abduct --BCverb--> 01850315-v move05291010-n abductor --BCnoun--> 05289861-n skeletal_muscle


eng-30-01449427-v sc_subClassOfKyoto#move__displace-eng-3.0-01850315-v

eng-30-05291010-n sc_subClassOfKyoto#skeletal_muscle__striated_muscle-eng-3.0-05289861-n


eng-30-01449427-v sc_hasParticipant

Kyoto#skeletal_muscle__striated_muscle-eng-3.0-05289861-neng-30-01449427-v sc_hasRole use-ofeng-30-05291010-n sc_participantOf

Kyoto#move__displace-eng-3.0-01850315-veng-30-05291010-n sc_playRole use-of

That is, the verb abducthas a type skeletal_muscle as a participant and the verb also has ause-of role. Conversely, the noun abductoris participant of a type move playing the use_ofrole.

All these 43 body-part relations have been connected to the ontology.

4.2.3 by-means-of

Condiction 1: If the verb is dynamic (TO) and the noun is an endurant-instrument (WN).

Example:00789448-v telephone --by-means-of--> 04401088-n telephone00789448-v telephone --BCnoun--> 00740577-v communicate04401088-n telephone --BCverb--> 03294048-n equipment


eng-30-00789448-v sc_subClassOf

Kyoto#communicate__intercommunicate-eng-3.0-00740577-veng-30-04401088-n sc_subClassOfKyoto#equipment-eng-3.0-03294048-n


eng-30-00789448-v sc_hasParticipantKyoto#equipment-eng-3.0-03294048-n

eng-30-00789448-v sc_hasRole instrumenteng-30-04401088-n sc_participantOf

Kyoto#communicate__intercommunicate-eng-3.0-00740577-veng-30-04401088-n sc_playRole instrument


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That is, the verb telephone has a type equipmentas a participant and the verb also has ainstrument role. Conversely, the noun telephone is participant of a type communicateplaying the instrument role.

Condition 2: if the verb is dynamic (TO) and the noun is an endurant-object (notinstrument) or substance (WN):

Example:

00493259-v contaminate --by-means-of--> 14821984-n contamination00287258-v contaminate --BCverb--> 00126264-v change14989820-n contamination --BCnoun--> 14580897-n material


eng-30-00493259-v sc_subClassOfKyoto#change__alter__modify-eng-3.0-00126264-v

eng-30-14821984-n sc_subClassOfKyoto#material__stuff-eng-3.0-14580897-n


eng-30-00493259-v sc_hasParticipantKyoto#material__stuff-eng-3.0-14580897-n

eng-30-00493259-v sc_hasRole use-ofeng-30-14821984-n sc_participantOf

Kyoto#change__alter__modify-eng-3.0-00126264-veng-30-14821984-n sc_playRole use-of

That is, the verb contaminate has a type materialas a participant and the verb also has ause-of role. Conversely, the noun contamination is participant of a type change playing theuse-of role.

Condition 3: if the verb is dynamic (TO) and the noun is an endurant-person (WN):

Example:02550516-v mother --by-means-of--> 10332385-n mother02550516-v mother --BCverb--> 02556126-v support10332385-n mother --BCnoun--> 00007846-n person


eng-30-02550516-v sc_subClassOf Kyoto#support__back_up-eng-3.0-02556126-veng-30-10332385-n sc_subClassOfKyoto#person__individual__someone__somebody__mortal__soul-eng-3.0-00007846-n



00007846-neng-30-02550516-v sc_hasRole done-byeng-30-10332385-n sc_participantOf

Kyoto#support__back_up-eng-3.0-02556126-veng-30-10332385-n sc_playRole done-by

That is, the verb motherhas a typeperson as a participant and the verb also has a done-byrole. Conversely, the noun motheris participant of a type supportplaying the done-by role.


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Condition 4: if the verb is dynamic (TO) and the noun is a perdurant (WN).

Example:01854679-v suction --by-means-of--> 11517494-n suction01854679-v suction --BCverb--> 01854679-v suction11517494-n suction --BCnoun--> 11419404-n physical_phenomenon


eng-30-01854679-v sc_equivalentOfKyoto#suction-eng-3.0-01854679-veng-30-11517494-n sc_subClassOf

Kyoto#physical_phenomenon-eng-3.0-11419404-n


eng-30-01854679-v sc_simpleCauseOfKyoto#physical_phenomenon-eng-3.0-11419404-n

That is, the verb suction is the cause of aphysical_phenomenon.

From a total of 1,273 by-means-of relations, 705 relations (55%) do not pass the test andare not connected to the ontology.

4.2.4 Destination

Condition: No test.

Example:02445356-v patent --destination--> 10405410-n patentee02445356-v patent --BCverb--> 02203362-v have10405410-n patentee --BCnoun--> 10214637-n person


eng-30-02445356-v sc_subClassOfKyoto#have__have_got__hold-eng-3.0-02203362-v

eng-30-10405410-n sc_subClassOfKyoto#person__individual__someone__somebody__mortal__soul-eng-3.0-

00007846-n



00007846-neng-30-02445356-v sc_hasRole participanteng-30-10405410-n sc_participantOf

Kyoto#have__have_got__hold-eng-3.0-02203362-veng-30-10405410-n sc_playRole participant

That is, the verb patent has a type person as a participant and the verb also has aparticipant role. Conversely, the noun patentee is participant of a type have playing theparticipant role.

All these 17 destination relations have been connected to the ontology.


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4.2.5 event

Condition: No test.

Example:00227165-v intensify --event--> 00374224-n intensification00227165-v intensify --BCverb--> 00156601-v increase00374224-n intensification --BCnoun--> 00363260-n change


eng-30-00227165-v sc_subClassOf Kyoto#increase-eng-3.0-00156601-veng-30-00374224-n sc_subClassOf Kyoto#change-eng-3.0-00191142-n


eng-30-00227165-v sc_subClassOf Kyoto#change-eng-3.0-00191142-neng-30-00374224-n sc_subClassOf Kyoto#increase-eng-3.0-00156601-v

That is, being nominalisations of the same events, we also establish additional relations toits related Base Concept types.

All these 8158 event relations have been connected to the ontology.

4.2.6 instrument

Condition: No test.

Example:00439343-v accelerate --instrument--> 02670683-n accelerator00439343-v accelerate --BCverb--> 00126264-v change02670683-n accelerator --BCnoun--> 03563967-n implement


eng-30-00439343-v sc_subClassOf Kyoto#change__alter__modify-eng-3.0-00126264-veng-30-02670683-n sc_subClassOf Kyoto#implement-eng-3.0-03563967-n


eng-30-00439343-v sc_hasParticipantKyoto#implement-eng-3.0-03563967-n

eng-30-00439343-v sc_hasRole instrumenteng-30-02670683-n sc_participantOf

Kyoto#change__alter__modify-eng-3.0-00126264-v

eng-30-02670683-n sc_playRole instrument

That is, the verb accelerate has a type implementas a participant and the verb also has aninstrument role. Conversely, the noun acceleratoris participant of a type change playing theinstrument role.

All these 813 instrument relations have been connected to the ontology.

4.2.7 location

Condition: No test.


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Example:02348927-v hospitalize --location--> 08054417-n hospital02348927-v hospitalize --BCverb--> 01850315-v move08054417-n hospital --BCnoun--> 08008335-n organization


eng-30-02348927-v sc_subClassOf

Kyoto#move__displace-eng-3.0-01850315-veng-30-08054417-n sc_subClassOfKyoto#organization__organisation-eng-3.0-08008335-n


eng-30-02348927-v sc_hasParticipantKyoto#organization__organisation-eng-3.0-08008335-n

eng-30-02348927-v sc_hasRole generic-locationeng-30-08054417-n sc_participantOf

Kyoto#move__displace-eng-3.0-01850315-veng-30-08054417-n sc_playRole generic-location

That is, the verb hospitalize has a type organization as a participant and the verb also hasan generic-location role. Conversely, the noun hospitalis participant of a type move playingthe generic-location role.

All these 288 location relations have been connected to the ontology.

4.2.8 material

Condition: No test.

Example:

00136254-v rubber --material--> 15006258-n rubber00136254-v rubber --BCverb--> 01332730-v cover15006258-n rubber --BCnoun--> 14580897-n material


eng-30-00136254-v sc_subClassOfKyoto#cover-eng-3.0-01332730-v

eng-30-15006258-n sc_subClassOfKyoto#material__stuff-eng-3.0-14580897-n


eng-30-00136254-v sc_hasParticipantKyoto#material__stuff-eng-3.0-14580897-neng-30-00136254-v sc_hasRole use-ofeng-30-15006258-n sc_participantOf

Kyoto#cover-eng-3.0-01332730-veng-30-15006258-n sc_playRole use-of

That is, the verb rubberhas a type materialas a participant and the verb also has an use-ofrole. Conversely, the noun rubberis participant of a type coverplaying the use-of role.

All these 114 material relations have been connected to the ontology.


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4.2.9 property

Condition: if the noun is static (TO)

Example:00618878-v identify --property--> 14577046-n identification00618878-v identify --BCverb--> 00699815-v determine14577046-n identification --BCnoun--> 13920835-n condition


eng-30-00618878-v sc_subClassOfKyoto#determine__set-eng-3.0-00699815-v

eng-30-14577046-n sc_subClassOfKyoto#condition__status-eng-3.0-13920835-n


eng-30-00618878-v sc_simpleCauseOfKyoto#condition__status-eng-3.0-13920835-n

eng-30-14577046-n sc_resultOf

Kyoto#determine__set-eng-3.0-00699815-v

That is, the verb identifyis the cause of a type condition, and the noun identification is theresult of a type determine.

From a total of 318 property relations, 285 relations (90%) do not pass the test and are notconnected to the ontology.

4.2.10 result

Condition 1: If the verb is dynamic (TO) and the noun is an endurant (WN):

Example:01384638-v archive --result--> 02735086-n archive01384638-v archive --BCverb--> 02203362-v have02735086-n archive --BCnoun--> 00021939-n artifact


eng-30-01384638-v sc_subClassOfKyoto#have__have_got__hold-eng-3.0-02203362-v

eng-30-02735086-n sc_subClassOfKyoto#artifact__artefact-eng-3.0-00021939-n


eng-30-01384638-v sc_hasParticipantKyoto#artifact__artefact-eng-3.0-00021939-n

eng-30-01384638-v sc_hasRole product-ofeng-30-02735086-n sc_participantOf

Kyoto#have__have_got__hold-eng-3.0-02203362-veng-30-02735086-n sc_playRole product-of

That is, the verb archive has a type artifactas a participant and the verb also has a product-of role. Conversely, the noun archive is participant of a type have playing the product-ofrole.


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Condition 2: if the verb is dynamic (TO) and the noun is a perdurant (WN):

Example:00366275-v condense --result--> 11446242-n condensation00366275-v condense --BCverb--> 00109660-v change11446242-n condensation --BCverb--> 11419404-n physical


eng-30-00366275-v sc_subClassOfKyoto#change-eng-3.0-00109660-v

eng-30-11446242-n sc_subClassOfKyoto#physical_phenomenon-eng-3.0-11419404-n


eng-30-00366275-v sc_subClassOfKyoto#physical_phenomenon-eng-3.0-11419404-n

That is, the verb condense is also a subclass of the type physical-phenomenon.

From a total of 1,439 result relations, 515 relations (36%) do not pass the tests and are notconnected to the ontology.

4.2.11 state

Condition 1: if the verb is static (TO):

Example:02619924-v survive --state--> 13962166-n survival02619924-v survive --BCverb--> 01108148-v overcome13962166-n survival --BCnoun--> 00024720-n state


eng-30-02619924-v sc_subClassOfKyoto#get_the_better_of__overcome__defeat-eng-3.0-01108148-v

eng-30-13962166-n sc_subClassOfKyoto#state-eng-3.0-00024720-n


eng-30-02619924-v sc_subClassOfKyoto#state-eng-3.0-00024720-n

eng-30-13962166-n sc_subClassOf

Kyoto#get_the_better_of__overcome__defeat-eng-3.0-01108148-v

That is, the verb survivalis subclass of has a type state, and the noun survivalis subclass ofa type overcome.

Condition 2: if the verb is dynamic (TO):

Example:01787106-v anger --state--> 14036539-n anger01787106-v anger --BCverb--> 01771535-v feel14036539-n anger --BCnoun--> 13920835-n condition


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eng-30-01787106-v sc_subClassOfKyoto#feel__experience-eng-3.0-01771535-v

eng-30-14036539-n sc_subClassOfKyoto#condition__status-eng-3.0-13920835-n


eng-30-01787106-v sc_simpleCauseOfKyoto#condition__status-eng-3.0-13920835-neng-30-14036539-n sc_resultOf

Kyoto#feel__experience-eng-3.0-01771535-v

That is, the verb angeris the cause of a type condition, and the noun angeris the result ofa type feel.

From a total of 528 result relations, 135 relations (26%) do not pass the tests and are notconnected to the ontology.

4.2.12 undergoer

Condition: No test.

Example:02255081-v remit --undergoer--> 13299453-n remittal02255081-v remit --BCverb--> 02251743-v pay13299453-n remittal --BCnoun--> 13278375-n payment


eng-30-02255081-v sc_subClassOfKyoto#pay-eng-3.0-02251743-v

eng-30-13299453-n sc_subClassOfKyoto#payment-eng-3.0-13278375-n


eng-30-02255081-v sc_subClassOfKyoto#payment-eng-3.0-13278375-n

eng-30-13299453-n sc_subClassOfKyoto#pay-eng-3.0-02251743-v

That is, the verb remitis subclass of a typepay, and the noun remittalis subclass of a typepayment.

All these 878 undergoer relations have been connected to the ontology.

4.2.13 Uses

Condition 1: if the verb is dynamic (TO-test) and the noun is endurant (WN-test):

Example:01269008-v seal --uses--> 04159545-n seal01269008-v seal --BCverb--> 01332730-v cover04159545-n seal --BCnoun--> 00021939-n artifact


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eng-30-01269008-v sc_subClassOfKyoto#cover-eng-3.0-01332730-v



eng-30-01269008-v sc_hasParticipantKyoto#artifact__artefact-eng-3.0-00021939-neng-30-01269008-v sc_hasRole Kyoto#use-ofeng-30-04159545-n sc_participantOf

Kyoto#cover-eng-3.0-01332730-veng-30-04159545-n sc_playRole Kyoto#use-of

That is, the verb sealhas a type artifact as a participant and the verb also has an use-ofrole. Conversely, the noun sealis participant of a type coverplaying the use-of role.

Condition 2: if the verb is dynamic (TO-test) and the noun is dynamic (WN-test):

Example:00612042-v memorialize --uses--> 06688522-n memorial00612042-v memorize --BLCverb--> 00610538-v remind06688522-n memorial --BLCnoun--> 06598915-n message


eng-30-00612042-v sc_subClassOfKyoto#remind-eng-3.0-00610538-v

eng-30-06688522-n sc_subClassOfKyoto#message__content__subject_matter__substance-eng-3.0-06598915-n


eng-30-00612042-v sc_subClassOfKyoto#message__content__subject_matter__substance-eng-3.0-06598915-n

eng-30-06688522-n sc_subClassOfKyoto#remind-eng-3.0-00610538-v

That is, the verb memorize is subclass of a type message, and the noun memorial issubclass of a type remind.

Condition 3: if the verb is dynamic (TO-test) and the noun is static (WN-test)

Example:01780941-v terrify --uses--> 07520612-n terror

01780941-v terrify --BCverb--> 01617192-v make07520612-n terror --BCnoun--> 00024720-n state


eng-30-01780941-v sc_subClassOfKyoto#make__create-eng-3.0-01617192-v

eng-30-07520612-n sc_subClassOfKyoto#state-eng-3.0-00024720-n



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eng-30-01780941-v sc_resultOfKyoto#state-eng-3.0-00024720-n

eng-30-07520612-n sc_simpleCauseOfKyoto#make__create-eng-3.0-01617192-v

That is, the verb terrifyis the result of a type state, and the noun terroris the cause of atype make.

From a total of 740 uses relations, 186 relations (25%) do not pass the tests and are not

connected to the ontology.

4.2.14 Vehicle

Condition: No test.

Example:01949110-v bus --vehicle--> 02924116-n bus01949110-v bus --BCverb--> 01835496-v travel02924116-n bus --BCnoun--> 00021939-n artifact


eng-30-01847582-v sc_subClassOfKyoto#travel__go__move__locomote-eng-3.0-01835496-v



eng-30-01949110-v sc_subClassOfKyoto#travel__go__move__locomote-eng-3.0-01835496-v

eng-30-01949110-v sc_hasRole Kyoto#instrumenteng-30-02924116-n sc_participantOf

Kyoto#move__displace-eng-3.0-01850315-veng-30-02924116-n sc_playRole Kyoto#instrument

That is, the verb bus is subclass of a type traveland the verb also has an instrument role.Conversely, the noun bus is participant of a type move playing the instrument role.

All these 87 vehicle relations have been connected to the ontology.

Summing up, a total of 17,739 relations have been used to establish additional connectionsto the ontology. That is, for more than 86% of the morpho-syntactic relations provided byWordNet have been used to establish additional connections to the KYOTO ontology.

4.3 Connecting Adjectives

In order to establish appropriate connections to the Kyoto ontology for adjectives, we alsouse the Princeton semantic relations to connect adjectives to its more appropriate noun orverb concepts. Then, through their hypernymy chain we also map each adjective as

sc_qualityOf to the type corresponding to the Base Concept of the noun or verb. We alsomap each adjective as sc_subclassOf to the "Kyoto#quality-eng-3.0-04723816-n" type.

The total number of adjectival synsets is 18,156. There are 10,693 synsets which aresatellites of 7,464 kernel synsets. However, not all kernels have antonym relations to otherkernels. There are 3,618 kernels without antonymy relation. They could have satellite


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synsets, but they do not constitute a complete cluster. We call them semi-clusters. Then,there are two types of clusters: complete cluster or semi-cluster. If a kernel has anantonymy relationship with another kernel then it forms a complete cluster. If a kernel hasno antonymy relation it forms a semi-cluster.

For instance, an example of a complete cluster, that is, a kernel adjective with one or moreantonyms are the following:

00031974-a active 00033574-a inactive 01105620-a nonspecific 01103021-a specific 01105620-a nonspecific 01105233-a specific

For instance, an example of a semi-cluster, that is, a kernel adjective without Antonyms

01380267-a aerial

There are 1,897 complete clusters with two or more semi-clusters. There are 1,849complete clusters with two semi-clusters, 44 clusters with three semi-clusters and 4 clusterswith four semi-clusters.

We process each cluster and semi-cluster to establish appropriate connections to nouns and

verbs depending to the available semantic relations in WordNet 3.0 relating the adjective sto nouns or verbs.

We processed the adjectives depending on the structure and the adjective relationscontained in the clusters to nouns and verbs. Thus, we have nine different sets.

Our heuristics considers the following WN relations between adjectives and nouns or verbs(no priority is given to none):

Related form Participle of verb Pertainym (pertains to noun) Attribute

1) CC stands for "Complete Square". There are 334 complete clusters with antonym kernelswith its corresponding antonym noun or verb. For instance:

00031974-a active 00033574-a inactive04635104-n activeness 04635631-n inactiveness00031974-a active 04635104-n activeness00033574-a inactive 04635631-n inactiveness04635104-n activeness --BCnoun--> 04616059-n trait04635631-n inactiveness --BCnoun--> 04616059-n trait

We use these relations to derive the Base Concepts of the adjectives (kernel synsets and its

satellites) by using the Base Concepts of the nouns and verbs.

00031974-a active --BCadj--> 04616059-n trait00033574-a inactive --BCadj--> 04616059-n trait

2) CI stands for "Incomplete Square". There are 117 incomplete clusters with some missingconnection to their antonyms.

02510446-a violable 02510604-a inviolable02668523-v violate 02668956-v conform_to02510446-a violable 02668523-v violate02510604-a inviolable ??


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02668523-v violate --BCverb--> 02668523-v violate02668956-v conform_to --BCverb--> 00109660-v change

We use these relations to derive the Base Concepts of the adjectives (kernel synsets and itssatellites) by using the Base Concepts of the nouns and verbs.

02510446-a violable --BCadj--> 02668523-v violate

We also assign the Base Concept to the incomplete semi-cluster.

02510604-a inviolable --BCadj--> 00109660-v change

3) CT stands for "Complete Triangle". There are 273 adjective kernels having a commonconnection to a noun or verb. For instance:

01660267-a operational 01660857-a nonoperational01660267-a operational 08199025-n military01660857-a nonoperational 08199025-n military08199025-n military --BCnoun--> 00031264-n group


01660267-a operational --BCadj--> 00031264-n group01660857-a nonoperational --BCadj--> 00031264-n group

4) CM stands for "Combined Connections". There are 471 kernels having no commonconnections to a noun or verb, neither antonymy relations to the corresponding nouns andverbs. In this case, we select the most common nominal or verbal connection to the cluster:

01583040-a neurotic 01586009-a unneurotic01583040-a neurotic 14393161-n neurosis14393161-n neurosis --BCnoun--> 13920835-n condition


01583040-a neurotic --BCadj--> 13920835-n condition01586009-a unneurotic --BCadj--> 13920835-n condition

5) ST stands for "Satellites". There are 290 kernels having no connections to nouns andverbs. In this case, we select the most common nominal or verbal connection to the clusterthrough their adjectival satellites:

00410078-a cismontane 00410449-a tramontane00410668-a transalpine -satellite--> 00410449-a tramontane00410668-a transalpine 10723731-n transalpine

10723731-n transalpine --BCnoun--> 00007846-n person


00410078-a cismontane --BCadj--> 00007846-n person00410449-a tramontane --BCadj--> 00007846-n person

6) SC stands for "Semi Cluster". There are 3,585 semi-clusters having a unique kernel andthe kernel and satellites have multiple relations to nouns and verbs. In this case, we selectthe most common nominal or verbal connection to the cluster:


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02941957-a East African 08699426-n East Africa08699426-n East Africa --BCnoun--> 00002684-n object


02941957-a East African --BCadj--> 00002684-n object

7) MA stands for "Multi-Antonymy". There are 51 clusters with more than two kernels. In

this case, we select the most common nominal or verbal connection to the cluster. Forinstance:

01105620-a nonspecific 01103021-a specific01105620-a nonspecific 01105233-a specific01105620-a nonspecific 06060845-n pathology01105233-a specific 06060845-n pathology06060845-n pathology --BCnoun--> 05996646-n discipline


01105620-a nonspecific --BCadj--> 05996646-n discipline01103021-a specific --BCadj--> 05996646-n discipline01105233-a specific --BCadj--> 05996646-n discipline

8) GL stands for "Gloss". There are 383 clusters with no direct connections to other nounsand verbs. In this case, we select the most common nominal or verbal connection using thegloss relation. For instance:

02087723-a unwrapped 02087594-a wrapped02087723-a unwrapped -gloss-> 14974264-n paper02087594-a wrapped -gloss-> 14974264-n paper14974264-n paper --BCnoun--> 14580897-n material


02087723-a unwrapped --BCadj--> 14580897-n material02087594-a wrapped --BCadj--> 14580897-n material

Obviously, some sets provides more confident connections. For instance, we think thatconfidence follows:

CC > CI ~ TC >> CM ~ ST ~ SC ~ MA >> GL

Furthermore, method CI founds missing connections (deduced from the cluster structure).CM and ST produce new relations for the kernels, SC and MA can produce new relations in

some cases, and GL suggests some potential connections for the kernel that should berevised.

The good news is that only 12 clusters have no candidates. So, in order to improve theresource, we suggest to to assign manually a nominal or verbal connection for these 12clusters and revise the GL set (383 connections).

Obviously, CM, ST, SC and MA sets can also be manually revised.


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5 Procedure for adaptation to a domain

Given the above design and data sources, we define the work for domain modeling asfollows:

1. Sources for background knowledge are collected and converted to a common SKOSformat;

2. The background knowledge in SKOS is automatically aligned to wordnet and madeavailable in the Wikyoto editor;3. A small text collection for the domain is built that represents the types of documents

from which facts need to be extracted. We estimate that about 100 to 500documents are sufficient, depending on the richness and variety of concepts.

4. A term database is created from the text collection and made available in theWikyoto editor;

5. In the Wikiyoto editor domain experts can do the following:1. Select and terms from the term database or the background vocabulary that they

deem important;2. Create a domain wordnet from these terms, where at least the root concepts are

mapped to the closest generic wordnet. Through the word-sense-disambiguationthis is already done but may be changed by the user;

3. Determine for each concept whether it is rigid or non-rigid;4. Map the non-rigid concepts and any process-concepts to processes in the

ontology using the TMEKO procedure in Wikyoto (Segers and Vossen 2010).

The manual of the Wikyoto system describes the options and possibilities to edit the domainwordnet in general. Here we go into a little bit more detail how the domain user shouldproceed conceptually.

As explained above, the generic ontology is only needed to provide a framework to definerelations between participants and events (perdurants) or qualities. The basic classificationof concepts is provided through WordNet3.0, which is fully mapped to the ontology andprovides a rich basis. Consequently, the domain user needs to provide an anchoring of the

domain concepts to the generic wordnet. After this is done, the ontological typing isautomatically derived. Next, only non-rigid concepts and terms for processes need to bemapped to the ontology, which is helped through the TMEKO procedure provided in Wikyoto.

The building of the domain wordnet proceeds in two steps:

1. build a hierarchy of domain terms and anchor the root concepts to the generic Wordnet;2. create a mapping for processes and for so-called non-rigid things to the ontology

We will explain how this works in more detail below.

5.1 Building a domain hierarchy of conceptsWhen you start, you will at least have a generic wordnet and a term database available. Inprinciple the term database represents a hierarchy in which specific terms are linked tomore general term using parent relations. Likewise Canadian snow crab is linked to snowcrab, which is linked to crab. A typical hierarchy can be seen in the next screen dumptaken from Wikyoto in Figure-2. The top of such a hierarchy is often matched with awordnet concept, which is indicated by the W icon in the display. At the bottom of thescreen, you can see the actual list of wordnet concepts that have been associated with theterm crab by the system in the text. You can consider the generic wordnet concepts byconsulting the generic Wordnet database (shown as a tab). Figure-3, shows what thegeneric Wordnet gives for one of the meanings of crab in English. The hierarchy downwardgives you a good idea about the meaning of the concept, as well as the gloss given.


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You can select another meaning from Wordnet or indicate that your domain crab is not thesame as any of the Wordnet meanings. In the latter case, you can select any other concept

from the generic Wordnet and either indicate that it is equivalent or a more specific concept.It is very important that you do anchor your terms to the generic wordnet. The more termsare anchored, the better the mining of facts will work and the better the terminology can beshared with other languages and even other domains.

Figure-2: Typically KYOTO term hierarchy learned from a text collection


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Here we assume that the meaning of crab shown in Figure-3 is appropriate. When you dragthe term crab to the domain wordnet, a copy of the concept from the term database isadded to the domain wordnet together with a link to the term and the highest scoring link tothe generic Wordnet. You can always change the mapping to Wordnet and remove any linkat a later stage. This concept of crab in the domain wordnet will be the anchor for the termsthat you add. It is the bridge between the domain wordnet and the generic wordnet, asindicated by the W-icon in the domain wordnet.

Now you can drag terms to the anchor and add them as children. In fact, you can select awhole list of terms by clicking the tick-boxes and add them all to the anchor, as shown inpop-up of Figure-4. You should select any concepts that express important properties ofcrabs. You can also add types of crabs if they occur although the generic Wordnet or otherbackground databases probably provide richer sets of types. For the KYOTO system it isimportant that you focus on terms that express properties of crabs or processes in which

Figure-3: Hierarchy downwards for crab in the generic Wordnet

Figure-4: Adding terms to the domain wordnet


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they are involved.

The complete term database thus consists of a list of root nodes with specific terms belowit. Most of the top nodes having anchors to the generic wordnet. A typical term databasecan have a few thousand tops and hundreds of thousands terms below these roots. Buildinga domain wordnet basically consists of dragging terms from the term database to thedomain wordnet window, where you typically copy the root-node of a term as well which willrepresent the anchor of the selected terms to the generic wordnet. There is no need to dragall terms from the database to the domain wordnet. The domain wordnet that you create,

also will consist of roots and more-specific concepts below it. In fact, Wikyoto has an optionto show the list of root nodes in the domain wordnet, as well as the root-nodes that are not(yet) linked to the generic Wordnet or to the ontology (see Figure-5). From these lists youcan select any concept to start editing.

Figure-5 shows also the so-called external relations that are created for this concept. It islinked to the term in the term database, to a synset in the generic Wordnet and to theontology class of decapoda. Figure-6 shows the hierarchy upward in the generic Wordnet.The domain wordnet concept crab is now thus connected this wordnet structure.

Figure-6: Hierarchy upward for crab in the generic Wordnet

Figure-5: Options to inspect the root concepts in Wikyoto


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The synsets in wordnet themselves are also already connected to the ontology. A long thechain in Figure-6, there are various relations to the ontology from which ontologicalproperties of the concept a re predicted, e.g. decapod, anthropod, animal, organism,physical-entity and entity. This can be seen from the next screen dump of the KYOTOontology, which shows the hierarchy from decapoda all the way up to endurant.

When editing the domain wordnet, you do not have to worry about these relations. Theanchoring is established automatically through the mapping of the root concepts to thegeneric Wordnet. The strategy is therefore to anchor all the root concepts to the genericWordnet. For the rest you are free to add any terms to these roots or create sub-hierarchiesbelow these as you like. Note that it is not necessary to duplicate any child-concepts thatare also child-concepts in the generic wordnet. For example, stone crab is already in the

generic Wordnet as child of crab. Duplicating this in the domain wordnet is unnecessary.For the system, the domain wordnet and the generic wordnet represent a single combinedresource.

Figure-7: KYOTO ontology hierarchy for decapoda


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5.2 How to decide where to start

For the domain user it may be difficult to decide where to start. In order to get a good feeling ofwhat concepts are important and how they are currently related to Wordnet, we created a termgraph browser. The term graph browser combines the hierarchy of the term database with thehierarchy of Wordnet and provides a flexible and easy to use browsing functionality. It is alsocreates a list of the terms in order of importance, which are shown as a drop-down menu throughwhich you can expand the term structure. Figure-8 on the next page shows a screen dump, where

the concept bird is expanded 4 levels deep. The grey boxes are terms from the documents thathave no mapping to Wordnet, the blue boxes are terms that occur in the documents and alsooccur in Wordnet. The red boxes are the parents of the latter in Wordnet but the words did notoccur. First of all, we observe the richness of the structure and the mixture of new terms andknown terms. Secondly, the pop-up menu shows the terms sorted for relevance. The top conceptsare: area, system, species, community, bird, information, habitatand management.

The Term Graph tool gives a good impression of what occurs in the database and can be used astype of mind-map to organize the work to be done in Wikyoto on the domain wordnet. Oursuggestion is therefore, to first use the term graph tool to explore the term database and define alist of terms and their roots that is relevant to you. Next, these terms can be added to the domainwordnet, while anchoring the roots to the generic Wordnet (if not already provided).


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5.3 Mapping the domain wordnet to the ontology

As explained above, it is strictly speaking not necessary to extend the ontology for a domain.However, it is beneficiary to map certain concepts from the domain wordnet to the ontology, andespecially to create relations between events and participants or to relate individuals toproperties. To facilitate this work, we created the TMEKO procedure (Tutoring Methodology forEnriching the Kyoto Ontology) that has been integrated in the Wikyoto system.

Large type-hierarchies of rigid classes, such as for example the millions of species, do not needto be represented in the domain wordnet nor in the ontology as long as these resources aresomehow mapped to Wordnet. If additionl species are found in the term database, such as theCanadian snow crab, it is sufficient to include them in the domain wordnet hierarchy below aroot concepts such as crab that is anchored to the generic Wordnet. Since the concept crab isalready mapped to the ontology as a rigid subclass ofdecapoda, any new concept added is also arigid subclass by default.

The TMEKO procedure is for all other terms and in fact only relevant for nouns, since we hardlyencounter new verbs and adjectives. Typical examples of concepts that need to be mapped arethe perdurants juvenile crab and edible crab. In addition, we also need to map terms thatrefer to processes or properties, such as: crab exploitation, abundance of crab.

The TMEKO-procedure implements the minimal steps to match non-rigid endurants andperdurant concepts to the ontology.

1 Starting point

The starting point is a sub-hierarchy in the domain wordnet such as shown here for soil:

2 Preparatory work

2.1 Anchoring the root node

The root-node of the sub-hierarchy needs to be connected to the generic wordnet (see above). Forsoil this is not the case, so the users need to make a mapping for the top node by dragging thecorrect WordNet synset over the top node. Now the WordNet icon appears:


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Whenever there is an anchoring or an anchoring is provided, Wikyoto looks up the synset in thetable to see if it is aperdurant, quality-region or an endurant.

So at this point, we assume that Wikyoto knows for each top-node whether it is a perdurant,

quality-region or an endurant. This is indicated through a label after the POS.

This is shown in the next screen dump which shows a fragment for the domain wordnethierarchy for crab.

The top-node crab is labeled as endurantand this classification should apply to all the childconcepts below it.


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2.2 Providing a definition

When the user selects a child concept, e.g. acid soil, they need to provide a definition. You cantype it or get it from Wikipedia throught the Wikyoto interface.

3 Determine the rigidity.

If the top-node of a wordnet hierarchy is labelled as an endurant, we need to establish therigidity of the concepts. This is determined through a rigidity interview. Such an interview can bestarted in Wikyoto by clicking with the right-mouse button on a concept. A pop-up is shownwhich shows the menu for starting a Rigidity interview among others. Note that it also shows theoption to start a TMEKO interview, discussed below:


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The rigidity interview tries to determine if the properties of a concept apply to all instancesduring their life-time. If there can be a moment in time that a concept does not apply and anothermoment in time that it does apply, then the concept is non-rigid. This is determined by answeringtwo simple questions, as shown here for alien invasie crab:

Is it possible that there are 'alien invasive crab(s)' that are not 'crab(s)'?

When a 'crab' is an 'alien crab' is it an 'alien invasive crab' during the complete time of itsexistence?

If the users answers yes to the first question and no to the second question, then the system infersthat alien invasive crab is non-rigid, which is correct since alien invasive crabs are not born asalien invasive crabs and they can become non-alien if migrating back to their place of origin.

Consider the following example for mitten crab:

Is it possible that there are 'mitten crab(s)' that are not 'crab(s)'?

When a 'crab' is a 'mitten crab' is it a 'mitten crab' during the complete time of itsexistence?

Most likely, the answers is yes to both questions, which defines mitten crab as a rigid subtype ofcrabs.

Determining rigidity should be determined top-down, starting from the top-node going down tothe children and proceed to the children of the children. We can then apply the following rules toany concepts below concepts labeled as endurants:

If the parent concept is rigid, then the child concepts and be either rigid or non-rigid.Rigidity needs to be determined for each child concept;

If the parent concept is non-rigid, then all the child concepts should be non-rigid aswell by definition

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D08.3 Domain Extension Central Ontology.v3

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