Information Extraction, Language Technology and the Semantic Web

Information Extraction, Language Technology and the Semantic Web

Thierry Declerck & Paul Buitelaar (DFKI GmbH)

T. Declerck, P. Buitelaar 2

We present collaborative research work on the combination of language technology (LT) and technologies for encoding (domain) knowledge in ontologies, supporting the emergence of the Semantic Web (SW), or maybe more appropriate: Semantic Webs


Semantic Web Applications of LT

Supporting accurate ontology-based semantic annotation of multilingual web documents (Knowledge Markup)

Supporting Ontology Learning/Construction from linguistically/semantically annotated multilingual text (Knowledge Extraction)


Knowledge Markup and Knowledge Extraction

Text/SpeechText/Speech

Text/Speech Mining

Concepts, Relations, EventsConcepts, Relations, Events

Linguistic AnalysisMorpho-Syntactic Analysis and Tagging,

Semantic Class Tagging, Term/NE Recognition, Grammatical Function Tagging, Dependency Structure Analysis

Linguistic and Semantic Annotations


Knowledge Markup and Knowledge Extraction (2)

Text/Speech/Image-VideoText/Speech/Image-Video

Text/Speech/Media Mining

Concepts, Relations, EventsConcepts, Relations, Events

Linguistic and Media Analysis

Linguistic, Low-level Image and Semantic Annotations


Integration of Language Technology and Domain Knowledge


Linguistic Analysis

Language technology tools are needed to support the upgrade of the actual web to the Semantic Web (SW) by providing an automatic analysis of the linguistic structure of textual documents. Free text documents undergoing linguistic analysis become available as semi-structured documents, from which meaningful units can be extracted automatically (information extraction) and organized through clustering or classification (text mining). Here we focus on the following linguistic analysis steps that underlie the extraction tasks: morphological analysis, part-of-speech tagging, chunking, dependency structure analysis, semantic tagging.


Morphological Analysis

Morphological analysis is concerned with the inflectional, derivational, and compounding processes in word formation in order to determine properties such as stem and inflectional information. Together with part-of-speech (PoS) information this process delivers the morpho-syntactic properties of a word.

While processing the German word Häusern (houses) the following morphological information should be analysed:

[PoS=N NUM=PL CASE=DAT GEN=NEUT STEM=HAUS]


Part-of-Speech Tagging

Part-of-Speech (PoS) tagging is the process of determining the correct syntactic class (a part-of-speech, e.g. noun, verb, etc.) for a particular word given its current context. The word “works” in the following sentences will be either a verb or a noun:

He works [N,V] the whole day for nothing.His works [N,V] have all been sold abroad.

PoS tagging involves disambiguation between multiple part-of-speech tags, next to guessing of the correct part-of-speech tag for unknown words on the basis of context information.


ChunkingFollowing Abney: chunks as the non-recursive parts of core phrases, such as nominal, prepositional, adjectival and adverbial phrases and verb groups.

Chunk parsing is an important step towards making natural language processing robust, since the goal of chunk parsing is not to deliver a full analysis of sentences, but to extract just the linguistic fragments that can be surely identified. However, even if this strategy fails to produce an analysis for the whole sentence, the partial linguistic information gained so far will still be useful for many applications, such as information extraction and text mining.


Named Entities detectionRelated to chunking is the recognition of so-called named entities (names of institutions and companies, date expressions, etc.). The extraction of named entities is mostly based on a strategy that combines look up in gazetteers (lists of companies, cities, etc.) with the definition of regular expression patterns. Named entity recognition can be included as part of the linguistic chunking procedure and the following sentence fragment: “…the secretary-general of the United Nations, Kofi Annan,…”will be annotated as a nominal phrase, including two named entities: United Nations with named entity class: organization, and Kofi Annan with named entity class: person


Dependency Structure Analysis

A dependency structure consists of two or more linguistic units that immediately dominate each other in a syntax tree. The detection of such structures is generally not provided by chunking but is building on the top of it.There are two main types of dependencies that are relevant for our purposes: On the one hand, the internal dependency structure of phrasal units or chunks and on the other hand the so-called grammatical functions (like subject and direct object).


Internal Dependency Structure

.

In linguistic analysis, for this we use the terms head, complements and modifiers, where the head is the dominating node in the syntax tree of a phrase (chunk), complements are necessary qualifiers thereof, and modifiers are optional qualifiers. Consider the following example:

“The shot by Christian Ziege goes over the goal.”

The prepositional phrase “by Christian Ziege” (containing the named entity Christian Ziege) depends on (and modifies) the head noun “shot”.


Grammatical FunctionsDetermine the role (function) of each of the linguistic chunks in the sentence and allow to identify the actors involved in certain events. So for example in the following sentence, the syntactic (and also the semantic) subject is the NP constituent “The shot by Christian Ziege”:

“The shot by Christian Ziege goes over the goal.”

This nominal phrase depends on (and complements) the verb “goes”, whereas the Noun “shot” is the head of the NP (it this the shot going over the goal, and not Christian Ziege!)


Semantic Tagging

Automatic semantic annotation has developed within language technology in recent years in connection with more integrated tasks like information extraction, which require a certain level of semantic analysis. Semantic tagging consists in the annotation of each content word in a document with a semantic category. Semantic categories are assigned on the basis of a semantic resources like WordNet for English or EuroWordNet, which links words between many European languages through a common inter-lingua of concepts.


Semantic ResourcesSemantic resources are captured in dictionaries, thesauri, and semantic networks, all of which express, either implicitly or explicitly, an ontology of the world in general or of more specific domains, such as medicine. They can be roughly distinguished into the following three groups:

Thesauri: Semantic resources that group together similar words or terms according to a standard set of relations, including broader term, narrower term, sibling, etc. (like Roget)

Semantic Lexicons: Semantic resources that group together words (or more complex lexical items) according to lexical semantic relations like synonymy, hyponymy, meronymy, and antonymy (like WordNet)

Semantic Networks: Semantic resources that group together objects denoted by natural language expressions (terms) according to a set of relations that originate in the nature of the domain of application (like UMLS in the medical domain)


The MeSH ThesaurusMeSH (Medical Subject Headings) is a thesaurus for indexing articles and books in the medical domain, which may then be used for searching MeSH-indexed databases. MeSH provides for each term a number of term variants that refer to the same concept. It currently includes a vocabulary of over 250,000 terms. The following is a sample entry for the term gene library (MH is the term itself, ENTRY are term variants):

MH = Gene LibraryENTRY = Bank, GeneENTRY = Banks, GeneENTRY = DNA LibrariesENTRY = Gene Bank

etc.


The WordNet Semantic Lexicon

WordNet has primarily been designed as a computational account of the human capacity of linguistic categorization and covers an extensive set of semantic classes (called synsets). Synsets are collections of synonyms, grouping together lexical items according to meaning similarity. Synsets are actually not made up of lexical items, but rather of lexical meanings (i.e. senses)


WordNet: An example

The word 'tree' has two meanings that roughly correspond to the classes of plants and that of diagrams, each with their own hierarchy of classes that are included in more general super-classes:

09396070 tree 0 09395329 woody_plant 0 ligneous_plant 0 09378438 vascular_plant 0 tracheophyte 0 00008864 plant 0 flora 0 plant_life 0 00002086 life_form 0 organism 0 being 0 living_thing 0 00001740 entity 0 something 010025462 tree 0 tree_diagram 0 09987563 plane_figure 0 two-dimensional_figure 0 09987377 figure 0 00015185 shape 0 form 0 00018604 attribute 0 00013018 abstraction 0


CyC: A Semantic NetworkCYC is a semantic network of over 1,000,000 manually defined rules that cover a large part of common sense knowledge about the world . For example, CYC knows that trees are usually outdoors, or that people who died stop buying things. Each concept in this semantic network is defined as a constant, which can represent a collection (e.g. the set of all people), an individual object (e.g. a particular person), a word (e.g. the English word person), a quantifier (e.g. there exist), or a relation (e.g. a predicate, function, slot, attribute). The entry for the predicate #$mother: #$mother : (#$mother ANIM FEM) isa: #$FamilyRelationSlot #$BinaryPredicate

This says that the predicate #$mother takes two arguments, the first of which must be an element of the collection #$Animal, and the second of which must be an element of the collection #$FemaleAnimal.


Word Sense Disambiguation

Words mostly have more than one interpretation, or sense. If natural language were completely unambiguous, there would be a one-to-one relationship between words and senses. In fact, things are much more complicated, because for most words not even a fixed number of senses can be given. Therefore, only in certain circumstances and depending on what we mean exactly with sense, can we give restricted solutions to the problem of Word Sense Disambiguation (WSD)


A simplified Example of a Domain Ontology

Ontology_1: Movies Title: String Date: mm/dd/yyyy Duration: minutes Type: (action, drama,..) Director: String Main Actors: Name_1: Role: Name_2: Role: Name_3: Role: ……

Ontology_1: Movies Title: Lord of the Rings Date: Duration: Type: Director: Peter Jackson Main Actors: Name_1: Role: Name_2: Role: Name_3: Role: ……

Instances


Example of RDF Schema forthe Movie Ontology

etc…

<rdf:RDF xmlns:rdf='http://www.w3.org/1999/02/22-rdf-syntax-ns#' xmlns:rdfs='http://www.w3.org/2000/01/rdf-schema#' xmlns:NS0='http://webode.dia.fi.upm.es/RDFS/MovieOntology#' ><rdf:Description rdf:about='http://webode.dia.fi.upm.es/RDFS/MovieOntology#SpecialEffectsCompanyActing'> <rdf:type rdf:resource='http://www.w3.org/2000/01/rdf-schema#Class'/> <rdfs:comment>Details of company that created special effects in this movie</rdfs:comment> <rdfs:subClassOf rdf:resource='http://webode.dia.fi.upm.es/RDFS/MovieOntology#CompanyActing'/></rdf:Description> <rdf:Description rdf:about='http://webode.dia.fi.upm.es/RDFS/MovieOntology#Police'> <rdf:type rdf:resource='http://www.w3.org/2000/01/rdf-schema#Class'/> <rdfs:comment>Films that deal solely with police activity</rdfs:comment> <rdfs:subClassOf rdf:resource='http://webode.dia.fi.upm.es/RDFS/MovieOntology#Crime'/> </rdf:Description>


Multilingual terminological lexicon, attached to a

domain ontology (MUMIS)

<lex-element id="ID" concept="Shot-on-goal"> <... lang="DE" type="main">Torschuss</term> <... lang="EN" type="main">shot on goal</term> <... lang="NL" type="main">schot op doel</term> <definition>ein Angriffsspieler kickt den Ball zu den gegnerischen Tor</definition> <... lang="DE" type="synonym">Distanzschuss</term> <... lang="DE" type="synonym">Nachschuss</term> <... lang="DE" type="synonym">Schuss</term> <... lang="DE" type="synonym">abzieh</term></lex-element>


Extension and Formalization of the multilingual terminological lexicon, including

syncategorematic information. Supporting WSD.

<lex-element id="ID" concept="Shot-on-goal"> <...lang = "DE" type = "main„ pos = „N“ mod = {„von

concept = „Player“ | concept = „player“ gender = „gen“ | pos = „posspron“ } >Torschuss</term>

<...lang="DE" type="synonym„ pos = „V“ comp = {„SUBJ“ concept = „Player“} >abzieh</term>

<definition>URL: DFB home page/glossary</definition></lex-element>


Integrating Syntactic and Domain Knowledge

Including Syntactic Analysis for a more accurate tagging of domain specific semantic annotation


Abstraction over Syntactic Annotation

Ontology_1: NPHead:NMod: {Adj*,PP?,GenNP}Spec: {Det? PossPron?}Type: {RefNP, ProNP, DateNP,etc.}

Ontology_2: PP Head: PrepType: {LocPP,DatePP, etc.}

Comp: NP

Ontology_4:Grammatical FunctionsSubject, Object, Ind. ObjectNP Adjunct, PP Adjunct, etc..

Ontology_3: Dependencies Head Comp Mod Spec


Merging of Syntactic and Domain Knowledge

Example of a possible rule for conceptual annotation:

If (Head of Subj_NP of Verb[type=soccer::shot-on-goal] is a person) => { annotate head of NP with semantic class “soccer::player”; …}

Example of a rule for Instance Filling:

If (term annotated with concept “soccer::player”) =>{ try to find information about relations “Team”, “Age” etc.} (Template Filling in Information Extraction).


NLP-based knowledge markup


document sentence

umlsterms

xrceterms

ewnterms

semrels

gramrels

chunks

text

cui

sense

umlsterm

xrceterm

ewnterm

semrel

gramrel

chunk

token

to

id from

to

offset

from

id

code

typeterm2term1id

pref tui

code pref tui

type

id

to

id from

type

id pos

lemma

msh

cui msh

MuchMore: DTD for Annotation


Balint syndrom is a combination of symptoms including simultanagnosia, a disorder of spatial and object-based attention, disturbed spatial perception and representation, and optic ataxia resulting from bilateral parieto-occipital lesions.

<text> <token id="w1" pos="NN">Balint</token> <token id="w2" pos="NN">syndrom</token> <token id="w3" pos="VBZ" lemma="be">is</token> <token id="w4" pos="DT" lemma="a">a</token> <token id="w5" pos="NN" lemma="combination">combination</token> <token id="w6" pos="IN" lemma="of">of</token> <token id="w7" pos="NNS" lemma="symptom">symptoms</token> ... <token id="w20" pos="JJ" lemma="spatial">spatial</token> <token id="w21" pos="NN" lemma="perception">perception</token> <token id="w22" pos="CC" lemma="and">and</token> <token id="w23" pos="NN" lemma="representation">representation</token> ...</text>

<chunks><chunk id="c1" from="w1" to="w2" type="NP"/><chunk id="c7" from="w20" to="w23" type="NP"/></chunks>>

MuchMore: Linguistic Annotation(Lemmatization, POS, Basic Chunking)


Balint syndrom is a combination of symptoms including simultanagnosia, a disorder of spatial and object-based attention, disturbed spatial perception and representation, and optic ataxia resulting from bilateral parieto-occipital lesions.

<umlsterm id="t7" from="w20" to="w21"><concept id="t7.1" cui="C0037744" preferred="Space Perception" tui="T041"> <msh code="F2.463.593.778"/> <msh code="F2.463.593.932.869"/></concept>

</umlsterm>

<umlsterm id="t8" from="w26" to="w26"><concept id="t8.1" cui="C0029144" preferred="Optics" tui="T090"> <msh code="H1.671.606"/></concept>

</umlsterm>

<semrel id="r7" term1="t7.1" term2="t8.1" reltype="issue_in"/>

<ewnterm id="e2" from="w21" to="w21"><sense offset="0487490"/><sense offset="3955418"/><sense offset="4002483"/>

</ewnterm>

MuchMore: Semantic Annotation (UMLS, EuroWordNet)


MUMIS: DTD for Linguistic Annotation

Document SentenceParagraph

PP

VG

NP

NE

AP

AdvP

Subord-Clause


AP

TYPE

STRUK

AP_AGR

STRING

AP_HEADW



VG

TYPE

VG_SUBCAT_STEM

STRING

KLAMMER

VG_STRG

SENT_STRING

VG_TYPE

VG_AGR

STRUK

VG_HEAD

...

VG

W



W

INFL

STRING

CLAUSE_PRED_SUBCAT

CLAUSE_PP_LIST

...

CLAUSE_TYPE

TC

CLAUSE_SUBJ

CLAUSE_PRED_STRG

STEM

TYPE

SENT_STRING

CLAUSE_VG_LIST

CLAUSE_PRED_AGR

CLAUSE

POS

CLAUSE_PP_ADJUNKT

CLAUSE_NP_LIST



Industrie, Handel und Dienstleistungen werden in der ersten Liste aufgeführt, wobei die in Klammern gesetzten Zahlen auf die Mutterfirmen hinweisen.

(Industry, trade and services are mentioned in the first list, in which numbers within brackets point to parent companies.) <chunks> <chunk id="c1" from="w1" to="w5" type="NP" head=”w1,w3,w5”/> <chunk id="c2" from="w6" to="w6" type="VG"/> <chunk id="c3" from="w7" to="w10" type="PP" head=”w7” complement=”w8,w9,w10”/> <chunk id="c4" from="w11" to="w1" type="VG"/> ….</chunks> <clauses> <clause id="cl1" from="c1" to="c4" pred_struct="c2 c4" GF_Subj="c1"/> <clause id="cl2" from="c6" to="c9" pred_struct="c9" GF_Subj="c6"/></clauses>

MUMIS: Linguistic Annotation(Lemmatization … Dependency Structure)


7. Ein Freistoss von Christian Ziege aus 25 Metern geht über das Tor.

<chunks> <chunk id="c1" from="w1" to="w5" type="NP" head=”w2” pp modifier=”w3 w4 w5”/> <chunk id="c2" from="w6" to="8" type="PP" head=”w6” complement=”w7 w8”/> <chunk id="c3" from="w9" to="9" type="VG"/> <chunk id="c4" from="w10" to="w12" type="PP" head=”w10” complement=”w11 w12”/></chunks>

<clauses> <clause id="cls1" from="c1" to="c4" pred_struct="c3“ GF_Subj="c1"/></clauses>

<events> <event id="e1" clause=”cls1” event-name=”free-kick”> <arguments>

<argument id="arg1" name="player” value=”w4, w5”/> <argument id="arg2" name="location” value=”25-meter”/>

<argument id="arg3" name="time” value=”07:00”/> </arguments> </event> <event id="e2" clause=”cls1” event-name=”goal-scene-fail”> <arguments>

<argument id="arg1" name="player” value=”w4, w5”/> <argument id="arg2" name="location” value=”25-meter”/> <argument id="arg3" name="time” value=”07:00”/> </arguments> </event></events>

MUMIS: Semantic Annotation (Events)


Conceptual Annotations for Multimedia Indexing and Retrieval: A multilingual cross-document and incremental IE approach (MUMIS)

Technology development to automatically index (with formal annotations) lengthy multimedia recordings (off-line process): Find and annotate relevant entities, relations and events

Technology development to exploit indexed multimedia archives (on-line process): Search for interesting scenes and play them via Internet

Test Domain: Soccer Games / UEFA Tournament 2000


Off-line TaskAutomatic Speech Recognition (Radio/TV Broadcasts)

Automatically transforms the speech signals into texts (for 3 languages — Dutch, English and German)

Natural Language Processing (Information Extraction)

Analyse all available textual documents (newspapers, speech transcripts, tickers, formal texts ...), identify and extract interesting entities, relations and events

Merging all the annotations produced so far

Create a database with formal annotations

Use video processing to adjust time marks

Indexing by...


Information Extraction

Information Extraction (IE) is the task of identifying, collecting and normalizing relevant information for a specific application or user. The relevant information is typically represented in form of predefined “templates”, which are filled by means of Natural Language (NL) analysis. IE combines pattern matching mechanisms, (shallow) NLP and domain knowledge (terminology and ontology).


Information Extraction (2)

IE is generally subdivided in following tasks:- Named Entity task (NE)

- Template Element task (TE)

- Template Relation task (TR)

- Scenario Template task (ST)

- Co-reference task (CO)


Subtask of IE

Named Entity task (NE): Mark into the text each string that represents, a person, organization, or location name, or a date or time, or a currency or percentage figure.Template Element task (TE): Extract basic information related to organization, person, and artifact entities, drawing evidence from everywhere in the text.


Subtask of IE (2)

Template Relation task (TR): Extract relational information on employee_of, manufacture_of, location_of relations etc. (TR expresses domain-independent relationships).Scenario Template task (ST): Extract pre-specified event information and relate the event information to particular organization, person, or artifact entities (ST identifies domain and task specific entities and relations).Co-reference task (CO): Capture information on co-referring expressions, i.e. all mentions of a given entity, including those marked in NE and TE.


IE applied to soccer

Terms as descriptors for the NE task Team: Titelverteidiger Brasilien, den respektlosen Außenseiter Schottland

Player:Superstar Ronaldo, von Bewacher Calderwood noch von Abwehrchef Hendry, von Jackson als drittem Stürmer, Torschütze Cesar, von Roberto Carlos (16.),

Referee: vom spanischen Schiedsrichter Garcia Aranda

Trainer: Schottlands Trainer Brown, Kapitän Hendry seinen Keeper Leighton

Location: im Stade de France von St. Denis (more fine-grained location detection would be: Stadion: im Stade de France and City: von St. Denis )

Attendance: Vor 80000 Zuschauern


IE applied to soccer (2)Terms for NE Task

Time: in der 73. Minute, nach gerade einmal 3:50 Minuten, von Roberto Carlos (16.), nach einer knappen halben Stunde, scheiterte Rivaldo (49./52.) jeweils nur knapp, das vor der Pause Versäumte versuchten die Brasilianer nach Wiederbeginn, ...

Date: am Mittwoch, der Turnierstart (?), im WM-Eröffnungsspiel (?)

Score/Result: Brasilien besiegt Schottland 2:1, einen 2:1 (1:1)-Sieg, der zwischenzeitliche Ausgleich, in der 4. Minute in Führung gebracht, köpfte zum 1:0 ein


IE applied to soccer (3)Relations for TR Task:

Opponents: Brasilien besiegt Schottland, feierte der Top-Favorit ... einen glücklichen 2:1 (1:1)-Sieg über den respektlosen Außenseiter Schottland,

Player_of: hatte Cesar Sampaio den vierfachen Weltmeister ... in Führung gebracht, Collins gelang ... der zwischenzeitliche Ausgleich für die Schotten, der Keeper des FC Aberdeen, Brasiliens Keeper Taffarel

Trainer_of: Schottlands Trainer Brown

...


IE applied to soccer (4)Events for ST task:

Goal: in der 4. Minute in Führung gebracht, das schnellste Tor ... markiert, Cesar Sampaio köpfte zum 1:0 ein, Collins (38.) verwandelte den Strafstoß, hätte Kapitän Hendry seinen Keeper Leighton um ein Haar zum zweiten Mal bezwungen, von dem der Ball ins Tor prallte

Foul: als er den durchlaufenden Gallacher im Strafraum allzu energisch am Trikot zog

Substitution: und mußte in der 59. Minute für Crespo Platz machen...


Conceptual Annotations for Multimedia Indexing and Retrieval: MUMIS

FormalText

FormalText

FormalTextFormal

TextFormal

TextFormal

TextFormalText

FormalText

FormalTextFree

Text

FormalText

FormalText

FormalText

FormalTextFormal

TextFormal

TextFormal

TextFormal

TextFormalText

FormalText

FormalTextFormal

Text

IEMergedAnnotated formal text

Information Extraction

FormalText

FormalText

FormalText

FormalTextFormal

TextFormal

TextFormal

TextFormal

TextFormalText

FormalText

FormalTextTrans-

criptsASR

Automatic Speech RecognitionFormal

TextFormal

TextFormal

TextFormal

TextFormalText

FormalText

FormalText

FormalTextFormal

TextFormal

TextFormal

TextSpeechSignals

Merging

Annotations

FormalText

Merging

FormalTextFormal

TextAnno-tations

Domain Modeling

DM

FormalText

FormalText

FormalText

FormalTextFormal

TextFormal

TextFormal

TextFormal

TextFormalText

FormalText

FormalTextSoccer

Texts

Ontology

OntologyOntologyDomain Lexicon

User Interface

UI

OntologyOntologyQueryDEEN NL

FORMAL

Legend


The first user interface of MUMIS


Ontology-based Annotation

Annotate accurately document with concepts and terms described in various semantic resources: EuroWordNet, UMLS, Soccer ontology etc.

Annotate documents with relations defined in the ontology


Ontology construction from Text

There are various methodologies under investigation for extracting/learning knowledge from text, and to encode it in an ontology (see Ontology Learning Overview - OntoWeb D1.5 http://www.ontoweb.org). Many are based on Machine Learning techniques

We discuss here the possibility of a rule-based approach for partial and shallow ontology construction from text, based on various levels of syntactic patterns annotated in the documents.

http://www.ontoweb.org/


Ontology construction from Text: Apposition and Paranthesis (1)

“The effects of rheumatoid arthritis on bone include structural joint damage (erosions) and osteoporosis “

Linguistic Structure:

[[The effects of rheumatoid arthritis] [on bone]] [include] [[structural joint damage ( erosions )] [ and] [osteoporosis]]

=> The Apposition (2 syntactic heads “joint” and “erosions” in one NP) including a parenthesis construction suggests a synonymy relation or a definition. Heuristic: Establishing Semantic Relations on the top of linguistic “head-modifiers” constructions


Ontology construction from Text: Apposition with Paranthesis (2)

“For symptoms of rheumatoid arthritis (pain, joint stiffness), the reference treatment is a nonsteroidal antiinflammatory drug (NSAID) such as diclofenac or ibuprofen.”

Linguistic Structure

[For symptoms of rheumatoid arthritis ( pain , joint stiffness )] , [the reference treatment] [is] [a nonsteroidal antiinflammatory drug ( NSAID)]

Suggesting a semantic relation between („pain“ and „joint stiffness“)

Classify „pain“ and „joint stiffness“ as symptom of RA. The word „symptom“ is linguistically annotated as the head of the Compl-NP of the PP starting with „For“.


Ontology construction from Text: Apposition with Paranthesis (3)

But there is a need for constraining the hypothesis:

“In patients with rheumatoid arthritis (RA)” => RA is abbreviation of rheumatoid arthritis

And in the sentence:

“Fourteen consecutive elbows have been treated for rheumatoid arthritis (9 elbows) and for post-traumatic osteoarthrosis (5 elbows) by total elbow replacement with the GSB III implant. “,

the parenthesis (9 elbows) and (5 elbows) have no semantic relations to the preceding head nouns!


Ontology construction from Text: Apposition with commas

“Etoricoxib, a selective COX2 inhibitor, has been shown to be as effective as non-selective non-steroidal anti-inflammatory drugs in the management of chronic pain in rheumatoid arthritis and osteoarthritis, …”

Linguistic Structure:

[Etoricoxib, a selective COX2 inhibitor,] [has been shown]…

The same hypothesis as in the former examples: a semantic relation between “Etoricoxib” and “selective COX2 inhibitor”. Probably a “isa” relation


Ontology construction from Text: Compound Analysis

„Joints destructions, „joint damage“, „joint disease“, „joint stiffness“ but „joint cartilage“.

„Knee joints“ vs. „tender joints”

What can happen to joins, where are joints located?. Use of synsets to detect relations? „Joint cartilage“ is not a disease.


Ontology construction from Text: PP post-modification

„inflammation of joints, synovial lining of joints”

Here: use of synsets for grouping that what can happen to joints?


Ontology construction from Text: Phrase Internal Coordination

“The effects of rheumatoid arthritis on bone include structural joint damage (erosions) and structural joint damage “Linguistic Structure:[[The effects of rheumatoid arthritis] [on bone]] [include] [[structural joint damage ( erosions )] [ and] [osteoporosis]]

RA causes structural joint damage AND structural joint damage (interpreting the head noun “effects” as a causation).Hypothesis: The two heads of an NP coordination are somehow related.


Ontology construction from Text: Phrase Internal Coordination (2)

“A study was conducted to determine the incidence of ulnar and peripheral neuropathy “

Linguistic Structure:… [The incidence of [[ulnar and peripheral] neuropathy]]

The AP “ulnar and peripheral” AP modifies the head noun “neuropathy”. The AP is a coordinated one, having two Adjectival heads. Hypothesis: They correspond to two types of neuropathy


Ontology construction from Text: Subject Verb Objetcs (Ind. Obj. etc.)

[Rheumatoid arthritis is an immunologically mediated inflammation of joints of unknown aetiology] and [often leads to disability]

=> RA leads to Disability (effect of ellipsis resolution: RA detected as the subject of the verb „leads“, even if not realised in text. Reference resolution very important for knowledge extraction)

=> Lexical semantic info: collects all objects of RA leads to …

=>Suggest Causality (verb lead + to)


Ontology construction from Text: Subject Verb Objects (Ind. Obj etc.)

“These changes constitute hallmarks of synovial cell activation and contribute to both chronic inflammation and hyperplasia”

On line exercise!


First Conclusions

Construction of partial and shallow ontologies from (complex) syntactic patterns seems feasible. It might seem “expensive” in the sense that documents first should be (automatically) linguistically annotated.

But Machine Learning methods also needs a lot of semi-automatically annotated data for training.

A need to conduct a comparative evaluation taking into account as many parameters as possible.

Information Extraction, Language Technology and the Semantic Web

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