Language Technology and the Semantic Web
Thierry Declerck & Paul Buitelaar
(Saarland University & DFKI GmbH)
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
MUMIS (dealing with multimedia content indexing and searching in the soccer domain, finished in December 2002) MuchMore (dealing with cross-lingual information retrieval in the medical domain, finished in Mai 2003) Esperonto (developing a Semantic Annotation Service for upgrading the actual Web to the Semantic Web, Sept. 2002 Mai 2005)
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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)
See also the Special Interest Group (SIG-5) OntoWeb-lt on Language Technology in Ontology Development and Use: http://ontoweb-lt.dfki.de
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Knowledge Markup and Knowledge Extraction
Text/Speech
Text/Speech Mining Linguistic and Semantic Annotations
Concepts, Relations, Events
Linguistic Analysis
Morpho-Syntactic Analysis and Tagging, Semantic Class Tagging, Term/NE Recognition, Grammatical Function Tagging, Dependency Structure Analysis T. Declerck, P. Buitelaar 4
Knowledge Markup and Knowledge Extraction (2)
Text/Speech/Image-Video
Text/Speech/Media Mining Linguistic, Low-level Image and Semantic Annotations
Concepts, Relations, Events
Linguistic and Media Analysis
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Integration of Language Technology and Domain Knowledge
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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.
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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]
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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-ofspeech tags, next to guessing of the correct part-of-speech tag for unknown words on the basis of context information.
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Chunking
Following 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.
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Named Entities detection
Related 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
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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 socalled grammatical functions (like subject and direct object).
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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”.
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Grammatical Functions
Determine 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!)
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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.
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Semantic Resources
Semantic 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)
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The MeSH Thesaurus
MeSH (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 ENTRY = ENTRY = ENTRY = ENTRY = etc. = Gene Library Bank, Gene Banks, Gene DNA Libraries Gene Bank
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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)
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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 0 10025462 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
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CyC: A Semantic Network
CYC 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.
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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)
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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: … …
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Instances
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: … …
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Example of RDF Schema for the Movie Ontology
Details of company that created special effects in this movie Films that deal solely with police activity
etc…
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Integration of Ontology and Semantic Lexicon
Example of Semantic Lexicon is WordNet (sometimes also referred to as a ‘Linguistic Ontology’) Ontologies are domain specific models, usually lacking linguistic information (PoS, Morphology, Syntax etc.) To be Integrated in One Resource or Kept/Accessed Separately?
Standardization
Format Web-based Standards for Lexical Semantic Representation will Increase their Uptake (Easy Plug-and-Play, Remote Access, etc.) Widely Used (Lexical) Semantic Resources will lead to (Further) Semantic Standardization
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Content
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Defining a Linguistic Ontology for the Art World (Tentative)
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Defining Art World Concepts (Classes, “Synsets”) (Tentative)
art-world.01
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Defining Properties (“Selection Restrictions”) (Tentative)
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(Semantic) Lexicons will be…
…an Important Part of the Semantic Web …Represented Using Markup Languages (RDF) …Accessible in a Remote, Distributed Fashion …Central to Further Semantic Standardization
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Multilingual terminological lexicon, attached to a domain ontology (MUMIS)
<... lang="DE" type="main">Torschuss <... lang="EN" type="main">shot on goal <... lang="NL" type="main">schot op doel ein Angriffsspieler kickt den Ball zu den gegnerischen Tor <... lang="DE" type="synonym">Distanzschuss <... lang="DE" type="synonym">Nachschuss <... lang="DE" type="synonym">Schuss <... lang="DE" type="synonym">abzieh
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Extension and Formalization of the multilingual terminological lexicon, including syncategorematic information. Supporting WSD.
<...lang = "DE" type = "main„ pos = „N“ mod = {„von concept = „Player“ | concept = „player“ gender = „gen“ | pos = „posspron“ } >Torschuss <...lang="DE" type="synonym„ pos = „V“ comp = {„SUBJ“ concept = „Player“} >abzieh URL: DFB home page/glossary
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Integrating Syntactic and Domain Knowledge
Including Syntactic Analysis for a more accurate tagging of domain specific semantic annotation
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Abstraction over Syntactic Annotation
Ontology_3: Dependencies Head Comp Mod Spec Ontology_2: PP Head: Prep Type: {LocPP,DatePP, etc.} Comp: NP
Ontology_1: NP Head:N Mod: {Adj*,PP?,GenNP} Spec: {Det? PossPron?} Type: {RefNP, ProNP, DateNP,etc.}
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Ontology_4: Grammatical Functions Subject, Object, Ind. Object NP Adjunct, PP Adjunct, etc..
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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).
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NLP-based knowledge markup
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MuchMore: DTD for Annotation
umlsterms umlsterm
id from to cui id from to cui id term1 term2 type code pref tui msh code pref tui msh
xrceterms
xrceterm
semrels document sentence ewnterms
semrel
ewnterm
sense
offset
gramrels
gramrel
id type id from to type id pos lemma
chunks
chunk
text
token
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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. id="w1" id="w2" id="w3" id="w4" id="w5" id="w6" id="w7" pos="NN">Balint pos="NN">syndrom pos="VBZ" lemma="be">is pos="DT" lemma="a">a pos="NN" lemma="combination">combination pos="IN" lemma="of">of pos="NNS" lemma="symptom">symptoms pos="JJ" pos="NN" pos="CC" pos="NN" lemma="spatial">spatial lemma="perception">perception lemma="and">and lemma="representation">representation
id="w20" id="w21" id="w22" id="w23"
>
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MuchMore: Semantic Annotation (UMLS, EuroWordNet)
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.
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MUMIS: DTD for Linguistic Annotation
Subord-Clause
AdvP
Document
Paragraph
Sentence
AP
NE
NP
PP
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VG
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MUMIS: DTD for Linguistic Annotation
TYPE
STRUK
AP
AP_AGR
STRING
W
AP_HEAD
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MUMIS: DTD for Linguistic Annotation
TYPE
VG_TYPE VG_SUBCAT_STEM VG_AGR STRING VG SENT_STRING
KLAMMER
STRUK W VG_STRG
VG_HEAD
...
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VG
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MUMIS: DTD for Linguistic Annotation
STEM INFL POS TC TYPE STRING CLAUSE_PP_ADJUNKT CLAUSE_SUBJ SENT_STRING CLAUSE_PRED_SUBCAT CLAUSE CLAUSE_TYPE CLAUSE_VG_LIST CLAUSE_PP_LIST CLAUSE_NP_LIST CLAUSE_PRED_STRG CLAUSE_PRED_AGR ...
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W
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MUMIS: Linguistic Annotation (Lemmatization … Dependency Structure)
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.)
from="w1" to="w5" type="NP" head=”w1,w3,w5”/> from="w6" to="w6" type="VG"/> from="w7" to="w10" type="PP" head=”w7” complement=”w8,w9,w10”/> from="w11" to="w1" type="VG"/>
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MUMIS: Semantic Annotation (Events)
7. Ein Freistoss von Christian Ziege aus 25 Metern geht über das Tor. <... lang="DE" type="main">zweite Halbzeit <... lang="EN" type="main">second half <... lang=„ES" type="main">reanudacion
….
Processing with the SCHUG system: Example
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Conceptual Annotations for Multimedia Indexing and Retrieval: MUMIS
Automatic Speech Recognition
Formal Formal Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Speech Text Text Formal Formal Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text TransText Text
Domain Modeling
Formal Formal Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Soccer Text Text
Ontology
ASR
Signals
cripts
DM
Texts
Ontology Ontology Domain Lexicon
Information Extraction
Formal Formal Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Formal Text Text Formal Formal Formal Text Text Formal Text Formal Formal Text Text Formal Text Formal Formal Text Free Text Text
Merging
Formal Text Formal Text Formal AnnoText
Text
Merged Annotated formal text
IE
tations
Text
Legend
User Interface
Ontology Ontology Query UI
Merging
EN
NL
DE
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FORMAL
Annotations 56
The first user interface of MUMIS
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Esperonto:Partners
Intelligent Software Components (Coord) : Semantic Web, Annotation Services. UPM : ontology development and evaluation. University of Innsbruck : Semantic Web languages. Saarland University : multilingual Annotation services, using Information Extraction UNILIV : Semantic indexation of Semantic Web content. Routing solutions. Visualization and navigation to make content presentation user-friendlier. Residencia de Estudiantes : Content provider. Cultural tour test case. Evaluation. CIDEM : Content provider. Fund finder test case. Evaluation. BioVista : Content provider. Scientific Discovery test case.
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Aim
Application Service Provision of Semantic Annotation, Aggregation, Indexing and Routing of Textual, Multimedia, and Multilingual Web Content
The project aims at bridging the gap between the actual World Wide Web and the semantic Web by providing a service to "upgrade" existing content to semantic Web content. Ontologies play a key role in this effort, together with multilingual Natural Language Analysis of textual documents currently in the web as free or HTML encoded texts.
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Main Goals
To bridge the gap between the current web and the Semantic Web: SemASP Ontology-based annotation Sources: Static pages Pages dinamically generated from DB Textual and multimedia information Web services Added value knowledge-based services on top of the constructed semantic web Routing based on P2P communication Semantic aggregation Meaning negotiation Support Multilinguality on ontology construction, ...
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Agent
Applications
Multilingual NL Generation
Visualization Service Provider
Semantic Web
Router Portal Agent Router Router Router
Router
Semantic indices, Concept instances
Certificate Workbench Ontology Repository Service Multilinguality Reengineering Mapping Tagger/ Wrapper Tagger/ Wrapper Tagger/ Wrapper Tagger/ Wrapper
SemASP
Maintenance
Multilingual NL Understanding
XML DAML OIL RDF(S)
World Wide Web
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Web Server Provider
Dynamic Information Provider
Static 61 Multimedia Data Information Provider Provider
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
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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.
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Ontology construction from Text: A starting experiment: Medicine
Document Set: 65 sample phrases that link symptoms with Rheumatoid Arthritis (RA).
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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
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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“.
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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!
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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
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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.
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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?
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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.
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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
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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)
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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!
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Future Work
Still have to identify accurately the sub-set of linguistic tags, describing syntactic/semantic patterns that are relevant for ontology extraction (or even ontology mark-up).
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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.
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Practical Sessions (Adrian Raschip)
• Exercise 1 : Semi-Automatic Terminological extension: Romanian and other languages. On the base of the TMX encoded MUMIS multilingual terminology • Exercise 2 : (Manual) linguistic annotation of English and Romanian Text on Soccer • Exercise 3 : Define a soccer ontology in Protégé • Exercise 4: Search for possible mapping rules between
linguistic annotations and relations that might be relevant to be extracted
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