Generalizable Semantic Relations for Knowledge Representation
CTF 07 ConferenceDüsseldorf, August 2007
Katrin Weller, Isabella Peters, Wolfgang G. StockInstitute for Language and Information,
Dep. of Information Science, Heinrich-Heine-University Düsseldorf
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Outline
1. Knowledge Representation in Information Science
2. Classical Relations in Knowledge Representation Methods
3. Syntagmatic Relations in Folksonomies
4. Construction of Relations in Ontologies
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Practical Background: Content Indexing and Knowledge Representation
Knowledge representation from information science‘s perspective:
• General aim: Retrieval of (relevant) information and embedding in current work flows.
• Solution: Indexing – Documents are provided with descriptive metadata.
• Methods of content indexing and information retrieval act together.
Knowledge representations are constructed for practical aims.
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Practical Background: Content Indexing and Knowledge Representation
Information retrievalBuilding knowledge representation models Search with controlled
vocabularies, relevance judgments via given abstracts or metadata.
Development of classification schemes, thesauri, rules for abstracting, etc.
Content Indexing of Documents
E.g. classification, annotation, abstracting.
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Knowledge Representation Methods in Information Science
Methods for knowledge representation
• Classifications• Thesauri• Controlled keyword indexing (Schlagwortmethode)
• Methods differ in complexity (e.g. how concepts can be interrelated).
• Some methods have been standardized, for some there are national or international norms (e.g. DIN 32705 for classification systems).
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Classification System: Example – IPC, International Patent Classification
http://www.wipo.int/classifications/fulltext/new_ipc/ipcen.html
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Retrieval Without Controlled Vocabulary
Example: Google
Examples from www.google.de and www.flickr.com
Example: Flickr
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Retrieval Without Controlled Vocabulary
Basic aim of information retrieval: „find what I mean, not what I say“.
Example I: Searching for photos of Düsseldorf, with search term „Düsseldorf“. Not retrieved are:
Examples from www.flickr.com
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Retrieval Without Controlled Vocabulary
Example II: Search for „bank“ retrieves:
German „Bank“ = bench
District Bank in London
Examples from www.flickr.com
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Practical Use of Controlled Vocabularies
• The terminology of a domain of knowledge is represented as a structured model of concepts and relations.
• Bundling of synonyms, separation and explanation of homonyms.
• Reduction of varieties. A generally valid and consistent way of indexing is enabled. Unification of users‘ and indexers‘ vocabulary.
• A search query can be refined and expanded with additional query terms (query expansion or query reformulation).
• Search results can be clustered according to contexts.
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Relations in Knowledge Representation
• Paradigmatic Relations: fixed, rigidly coupled concept relations within controlled vocabularies. Example: “vehicle”and “bicycle” as hierarchy within a classification scheme.
• Syntagmatic Relations originate merely in the actual co-occurrence of terms within a certain setting.
• Generalizable Relations are paradigmatic relations that can be meaningfully used in all (or in most of all) general or domain-specific knowledge representation and organization models.
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Research on Relations
• There are detailed and highly-professional theoretical reflections on relationships from fields such as linguistics, philosophy, information science, knowledge engineering, artificial intelligence studies and related disciplines.
• Also to be considered: properties, attributes-value pairs, slots and fillers...
New Approach:Examination of existing knowledge representation approaches.Application of detected relations to practical tasks.
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Classical Relations Used in Knowledge Representation Systems
In contrast to rich theoretical research, only a small number of paradigmatic semantic relations is currently differentiated and practically applied in classical methods of knowledge representation:
Classical Types of Relations• Relation of equivalence • Hierarchical Relations
– Hyponymy– Meronymy
• Associative relations (all other relations)
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Classical Relations Used in Knowledge Representation Systems
Relation of equivalence
• Synonymy (Relation between different names for the sameconcept)
• Quasi-Synonyms (Relation between concepts with similar meaning)
• Genidentity (Relation between concepts, whose meaning has changed slightly in the course of time (Leningrad - St. Petersburg)
• Using this relations helps to unify a controlled vocabulary and to increase recall in information retrieval.
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Classical Relations Used in Knowledge Representation Systems
Hierarchical Relations• Relations between (upper) concepts and their sub-concepts. • Basis framework for most concept models – dominate
current systems.
Hyponymy• Logical view. Specification of features; „is a kind of“, „is_a“.• kind-of-relation, taxonomic relation, taxonomy • Examples: mammal – cat; vehicle – car.
Meronymy• Objective view; „is a part of“.• Part-whole-relation, mereology, part-of relation, partonomy
(meronym – holonym).• Examples: car – motor; England – London.
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Classical Relations Used in Knowledge Representation Systems
Associative Relations
They are unspecified connections of concepts that can have any kind of relation – except synonyms and hierarchical relations.
Proposals for specification:
– contrasts (antonymy)– cause – effect (causality)– producer – product (genetic relation)– material – product – predecessor – successor (succession)– sender – recipient (transmission)– etc.
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Classical Relations Used in Knowledge Representation Systems
Controlled Keywords
ThesaurusClassification
Extend of captured knowledge domain
Complexity in structure
Hyponymy, meronymy, equivalents & associations
Hierarchy & equivalents
Equivalents & associations
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Use of Relations: Query-Expansion
Search queries can be expanded by adding concepts related to theinitial search terms to improve search results.
Example: Search for a stud farm in the German region “Rhein-Erft-Kreis”.
Rhein-Erft-Kreis
Bergheim Kerpen
Quadrat-Ichendorf
Niederaußem
region
cities
districts
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Reconsidering Classical Relations
Caution! Expansions may include only one path, if the relations are not transitive.
Transitivity– Example: Tottenham is_part_of London; London
is_part_of UK. And also: Tottenham is_part_of UK.– Classical counter-example: Nose is_part_of Professor;
Professor is_part_of University. But not: Nose is_part_ofUniversity.
Possibly solution: specification of meronymy.
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Decomposition of Classical RelationsExample: Meronymy
Meronymy(part-whole)
Geograph. subunit –geograph. unit
Element - Collection
Unit - Organization
Component-Complex
Decomposition of structures
Structure-independent composition
Piece - Whole
Phase - Activity
Segment - Event
Part - Object
Portion -Compound
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New Methods for Knowledge Representation
Current Trends
• Web 2.0 Knowledge Representation with Folksonomies.Users take over the indexing of web content.
• Semantic WebKnowledge Representation with Ontologies. Higher complexity, formal structuring.
Examples from www.flickr.com and http://protege.stanford.edu
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New Methods for Knowledge Representation
Folksonomy
Controlled Keywords
ThesaurusClassification
Ontology
Extend of capturedknowledge domain
Complexityin structure
Hyponymy, meronymy, equivalents & specified associations
Trend: enhancement
Hyponymy, meronymy, equivalents & associations
Hierarchy & equivalents
Equivalents & associations
(no paradigmaticrelations)
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http://www.flickr.com
Folksonomies
Content indexing 2.0
• Users have begun to organize web content (e.g. photos, bookmarks).
• Tags are added to documents to describe their content.
• The process is called (social) tagging and resembles uncontrolled keyword indexing.
• The whole collection of tags within one platform is called folksonomy.
• Tags can then be used for searching.
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Relations in Folksonomies?
• No controlled vocabulary, no explicit relations.
• Like in plain texts, relations in folksonomies are purely syntagmatic.
But: • Users‘ Terminology is captured and can provide insights to
their actual language use for indexing/searching.
• Relations may be „hidden“ in the combined assignment of several tags. Further research is needed.
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Example I
Photos and tags from www.flickr.com
(geographic) Hierarchy
SynonymsIs_A
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Example II
Photos and tags from www.flickr.com
Equivalent: football - soccer
Rival
Relatedcolours
Name of Stadium –changes over time
Location - (geographic) hierarchywith region
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Example III
• del.icio.us: Bookmarks can be tagged by different users: tag clouds.
Examples from:http://del.icio.us
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Evaluation of Social Tags
• Systems, where documents are tagged by several users, tend to cover synonyms and spelling variants. We can also find kinds of hierarchies.
• Often more than one tag is assigned to each document, so that interrelations may be exploited.
• Differences can be noted between tags referring to pictures and documents/bookmarks.
• Tags also include many personal associations, most of them cannot be used for our purpose (e. g. me, to do, my dog, last year, fantastic, readthis).
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Ontologies
• Ontologies as new knowledge representation systems consist of concepts, instances, relations that connect concepts ans specify properties of instances.
• Ontologies can be represented with formal ontology languages such as OWL.
• Ontologies shall enable the semantic annotation and integration of information in a Semantic Web.
• Currently there are no rules or guidelines regarding the use of relations in ontology engineering.
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Elements of OntologiesClasses: hierarchical editor, rules for class membership can be added. Instances (individuals) are assigned to classes.
Excerpt from Generations Ontology: http://www.co-ode.org/ontologies/
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Elements of Ontologies
• Additional relations can be chosen to model the domain.
• Object and datatype relations.
• Relations may be specified:– Domain and range– Functional?– Symmetric?– Transitive?– Inverse counterpart– Cardinalities
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Example
Generations Ontology taken from: http://www.co-ode.org/ontologies/, displayed with Protege OntoViz Tab.
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Relations in Ontologies
• Most dominant: hierarchical is_a, instance_of; also part_of.
• But: Self-defined relations should be used to capture the domain appropriately.
• Many relations are domain-specific.
• Question: Are there new relations that apply to most or many domains of interest (and are these used in current ontologies)?
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Example: Domain Specific Relations
DOLCE : a DescriptiveOntology for Linguisticand CognitiveEngineering.
Section Social Units.
Example: DOLCE (Section Social Units, http://www.loa-cnr.it/DOLCE.html)
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Example II
UMLS – Unified Medical Language System
Set of non-hierarchical relationships, grouped into five major categories: – physically related to– spatially related to– temporally related to– functionally related to– conceptually related to
See: http://www.nlm.nih.gov/pubs/factsheets/umlssemn.html and http://www.nlm.nih.gov/research/umls/meta2.html#s2_2
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Suggestions for Generalizable Relations
Properties for General Use?
• Location: geographical information (including hierarchies), locations in comparison to other objects (inclusion, adjacency)…
• Perception: color, shape, taste, sound, haptics, surface, texture, smell…
• Measures: Length, weight, height, density, temperature, time (duration), material? ...
• Functions, actions, task, behavior, processes, events, motions: e.g. develops_from, used_for, used_by, produced_by, transformations, motions…
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Conclusions
• Current methods of knowledge representation for information organization and retrieval use only limited types of relations.
• Differentiated relations that can be explicitly formalized in ontologies or may be inherently hidden in folksonomies.
• More relations will be collected, clustered and characterized.
• Newly identified general-use relations will than have to be evaluated for their use in practical applications.
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References I
• Bean C.A., Green R. (ed.): Relationships in the Organization of Knowledge. Dordrecht: Kluver, 2001.
• Bertram, J.: Einführung in die inhaltliche Erschließung. Grundlagen, Methoden, Instrumente. Würzburg: Ergon, 2005.
• Cruse, D.A.: Hyponymy and its varieties. In: Green, R., Bean, C.A., & Myaeng, S.H., ed.: The Semantics of Relationships. Dordrecht: Kluwer, 3-21, 2002.
• Gerstl, P., Pribbenow, S.: A conceptual theory of part-whole relations and its applications. In: Data & Knowledge Engineering, 20: 305-322, 1996.
• Green, R., Bean, C.A., & Myaeng, S.H., ed.: The Semantics of Relationships. Dordrecht: Kluwer, 2002.
• Gordon-Murnane, L.: Social bookmarking, folksonomies, and Web 2.0 tools. Searcher - The Magazine for Database Professionals, 14(6), 2006, 26-38.
• Guy, M., Tonkin, E.: Folksonomies: Tidying up tags? D-Lib Magazine, 12(1), 2006.
• Hovy, E.: Comparing Sets of Semantic Relations in Ontologies. In: Green, R., Bean, C.A., & Myaeng, S.H., ed.: The Semantics of Relationships. Dordrecht: Kluwer, 91-110, 2002.
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References II
• Lancaster, F.: Indexing and Abstracting in Theory and Practice. 2nd ed, London: Library Association Publishing, 1998.
• Löbner, S.: Understanding Semantics. London: Edward Arnold Publishers, 2002.
• Khoo, C.S.G. & Na, J.C.: Semantic relations in information science. In: Annual Review of Information Science and Technology, 40, 2006, 157-228.
• Mathes, A. (2004). Folksonomies – Cooperative Classification and Communication Through Shared Metadata. Retrieved August 15, 2006from http://adammathes.com/academic/computer-mediated-communication/folksonomies.html.
• Pribbenow, S.: From classical mereology to complex part-whole-relations. In: Green, R., Bean, C.A., & Myaeng, S.H., ed.: The Semantics of Relationships. Dordrecht: Kluwer, 35-50, 2002.
• Stock W.G.: Information Retrieval. Suchen und Finden von Informationen. München; Wien: Oldenbourg Wissenschaftsverlag, 2007.
• W3C Recommendation: OWL Web Ontology Language Overview, 2004, http://www.w3.org/2004/OWL/ (retrieved March 30, 2007).
• Winston, M.E., Chaffin, R., & Herrmann, D.: A taxonomy of part-whole-relations. In: Cognitive Science, 11: 417-444, 1987.