Relational Models [chapter]

Volker Tresp, Maximilian Nickel
2014 Encyclopedia of Social Network Analysis and Mining  
Relational learning, statistical relational models, statistical relational learning, relational data mining Glossary Entities are (abstract) objects. An actor in a social network can be modelled as an entity. There can be multiple types of entities, entity attributes and relationships between entities. Entities, relationships and attributes are defined in the entity-relationship model, which is used in the design of a formal relational model Relation A relation or relation instance I(R) is a
more » ... ite set of tuples. A tuple is an ordered list of elements. R is the name or type of the relation. A database instance (or world) is a set of relation instances Predicate A predicate R is a mapping of tuples to true or false. R(tuple) is a ground predicate and is true when tuple ∈ R, otherwise it is false. Note that we do not distinguish between the relation name R and the predicate name R Possible worlds A (possible) world corresponds to a database instance. In a probabilistic database, a probability distribution is defined over all possible worlds under consideration. RDF The Resource Description Framework (RDF) is a data model with binary relations and is the basic data model of the Semantic Web's Linked Data. A labelled directed link between two nodes represents a binary tuple. In social network analysis, nodes would be individuals or actors and links would correspond to ties Linked Data Linked (Open) Data describes a method of publishing structured data so that it can be interlinked and can be exploited by machines. Much of Linked Data is based on the RDF data model Collective learning refers to the effect that an entity's relationships, attributes or class membership can be predicted not only from the entity's attributes but also from information distributed in the network environment of the entity Collective classification A special case of collective learning: The class membership of an entity can be predicted from the class memberships of entities in the network environment of the entity. Example: a person's wealth can be predicted from the wealth of this person's friends Relationship prediction The prediction of the existence of a relationship between entities, for example friendship between persons Entity resolution The task of predicting if two constants refer to the identical entity Homophily The tendency of a person to associate with similar other persons Graphical models A graphical description of a probabilistic domain where nodes represent random variables and edges represent direct probabilistic dependencies Latent Variables Latent variables are quantities which are not measured directly and whose states are inferred from data Definition Relational models are machine-learning models that are able to truthfully model some or all distinguishing features of a relational domain such as long-range dependencies over multiple relationships. Typical examples for relational domains include social networks and knowledge bases.
doi:10.1007/978-1-4614-6170-8_245 fatcat:f5qvlgr4bva2vfr7uyzxsvpl4y