1. Information Networks: State of the Art
Michael R. Berthold and Tobias Kötter
The subject of the presentation is Information Networks: State of the Art

2. Information Networks: State of the Art
Outline
Information Networks
Properties of Information Networks
Information Unit properties
Relation properties
Prominent Types of Information Networks
Ontologies
Semantic Networks
Topic Maps
Bayesian Networks
Bisociative Information Networks (BisoNets)
Comparative Matrix
Let me first give you a brief overview.
I will start with a short introduction on information networks
followed by properties of information units and relations that can be used to classify information networks.
After that I will talk about prominent types of information networks such as Ontologies and the properties they support.
Finally I will conclude with a comparative matrix of the supported properties per information network.
Information Networks: State of the Art

3. Information Networks: State of the Art
Composed of:
Information Units
Physical items, concepts, ideas, …
Represented by vertices
Relations
Connections between Information Units
Usually represented by edges
Commonly used for data integration
Well defined structure allows to
discover pattern of interest
extract network summarization
visually explore underlying relations
Information networks are composed of Information units and relations.
Information units represent concepts, ideas, physical items and are represented by vertices.
Relations represent connections between Information units and are usually represented by edges
Information networks are mostly used for data integration due to their flexible structure.
This flexible but well defined structure also allows to discover pattern of interest such as cliques or hubs,
to summarize the network in order to get a better overview of the global structure
and to explore the underlying relations.
Information Networks: State of the Art

4. Properties of Information Units
Named
the name of the information unit
Attributed
E.g. link to original data or translations of the original label
Might be considered while reasoning or analyzing the network
Do not carry general semantic information
Typed
Allows to distinguish between different semantics of information units
Can additionally be organized in a hierarchy or ontology
Hierarchical
Subgraph
Represents more complex concepts
Coming next to the properties of information networks starting with the properties of information units.
The first property allows to attache attributes to information units such as links to original data. This information can be considered … but do not carry general semantic information
The second kind of property are typed information units allow to distinguish between…
Hierarchical information units are subgraphs that represent more complex concepts such as cellular processes
Information Networks: State of the Art

5. Properties of Relations
Attributed
Can be considered during the reasoning process
Do not carry a general semantic information
Typed
Distinguishes between different semantics of relations
Can be organized in a hierarchy or ontology
Weighted
Measure of reliability
Allows the integration of facts and pieces of evidence
Directed
Explicitly models relationships that are only valid in one direction
Multi relation
Multi edges supporting any number of members
The second type of network properties that describe the relations.
Attributes allow to attach additional information to a relation. They can be considered … but do not…
Typed relations can be used to distinguish … Relation types as well as information unit types can be organized in a …
A relation weight represents the reliability of a connection and allows to integrate not only facts but also pieces of evidence
Directed relations model explicit relations that are only valid in one direction such as a parent child relationship.
Multi relations support more than two members which can be necessary to describe gene expression experiments or authors of a paper
Information Networks: State of the Art

6. Properties of Ontologies
Relations
Attributed
Typed
Weighted
Directed
Multi relation
Information Units
Named
Hierarchical
On the left you can see the properties of the information units and on the top the properties of relations.
Information Networks: State of the Art

7. Information Networks: State of the Art
Ontology
Controlled vocabulary for information units and relations
Requires comprehensive domain knowledge
Mostly manual or semi-automatic created
The first type of the prominent information networks I want to talk about are ontologies.
They support typed and directed relations.
They use a controlled…
The creation of the vocabulary requires a comprehensive…
Most of the Ontologies are manual…
The example shows information networks and their possible properties
Information Networks: State of the Art

8. Properties of Semantic Networks
Relations
Attributed
Typed
Weighted
Directed
Multi relation
Information Units
Named
Hierarchical
Information Networks: State of the Art

9. Information Networks: State of the Art
Semantic Networks
Types might be organized in an ontology
URI used to identify information units and relations
Usually based on Semantic Web technologies
Resource Description Framework (RDF)
Knowledge representation and storage framework
Triples consists of subject, predicate and object
RDF Vocabulary Description Language (RDF Schema)
Defines a vocabulary to describe properties and classes
Used to describe the members of a triple
Web Ontology Language (OWL)
Extends RDF Schema
The second type of information network are the semantic networks.
They support attributed information units as well as typed and directed relations
The relation types are mostly organized in an ontology to be more expressive
URIs are used to identify…
Most of the semantic web systems base on semantic web technologies such as
RDF for data storage
RDF Schema or OWL to describe the used attributes
Information Networks: State of the Art

10. Semantic Network: Example
The example shows a parent child relationship between one parent with the name Parent an its two children child1 child2.
The typical rdf triple can be seen with subject, predicate and object.
Information Networks: State of the Art

11. Properties of Topic Maps
Relations
Attributed
Typed
Weighted
Directed
Multi relation
Information Units
Named
Hierarchical
Information Networks: State of the Art

12. Information Networks: State of the Art
Topic Map
Topic represents generally everything, a concept, an idea, …
Topics have zero or more types assigned represented by topics
Associations model relations between any number of topics
Association have a type assigned represented by topics
Association members play a certain role represented as topic
Occurrences link topics with resources they stem from
Occurrences have any number of types represented by topics
Virtually everything in topic maps is a topic
Information Networks: State of the Art

13. Information Networks: State of the Art
Topic Map: Example
In this example all topics are depicted as ellipses and as you can see basically everything is a topic. Topic, relation and occurrence types are depicted as dashed lines whereas the occurrence itself is depicted as dotted line.
The example shows three genes that were overexpressed in the same experiment with name exp_1.
Information Networks: State of the Art

14. Properties of Bayesian Networks
Relations
Attributed
Typed
Weighted
Directed
Multi relation
Information Units
Named
Hierarchical
Information Networks: State of the Art

15. Information Networks: State of the Art
Bayesian Networks
Vertices represents variables
Relations and their direction model dependencies
Relation weights represent probabilities
Remarks:
Bayesian Networks do not have weighted edges. The probabilities are attached to the nodes!!!
The example depicts a typical Bayesian network with the probabilities of smoking, bronchitis, lung cancer and dyspnea.
Information Networks: State of the Art

16. Properties of BisoNets
Relations
Attributed
Typed
Weighted
Directed
Multi relation
Information Units
Named
Hierarchical
The last network type I just want to mention briefly are BisoNets that have been developed by the Bison EU consortium.
The main goal of these networks is to support the integration not only of facts but also pieces of evidence from heterogeneous data sources in order to find connections across diverse domains so called bisociations.
Information Networks: State of the Art

17. BisoNets: Bisociative Information Networks
k-partite graph
Partitions represent types e.g. gene, document, …
Nodes represent concepts, relations or BisoNets
Edge weight represents the certainty of a connection
Nodes might carry any number of attributes
Remark: What is the difference between bisonets and relational data model?
Is it necessary that partitions are aligned next to each other?
Nodes represent concepts such in the previously mentioned network types
But represent also relations which allowing the modeling of multi relations
And can also represent BisoNets themselfe in order to represent a hierarchical structure.
Information Networks: State of the Art

18. Information Networks: State of the Art
Comparative Matrix
Information Units
Relations
Attributed
Typed
Hierarchical
Weighted
Directed
Multi relation
Ontology
Semantic Networks
Topic Map
Bayesian Networks
BisoNets
I want to conclude my talk with the comparative matrix of all presented network types.
As you can see by the supported propeties ontologies, semantic web and topic maps were created to support the integration of semantic meaningful data sources such as facts.
And Bayesian networks on the otherhand support the integration of probabilisitic data representing pieces of evidence.
BisoNets combine all these properties in order to integration various kinds of data in order to find domain crossing connections.
Thank you for your attention. Any questions?
Information Networks: State of the Art

19. Thank you for your attention! Any questions?
Information Networks: State of the Art