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Statistical relational learning (SRL) is a subdiscipline of artificial intelligence and machine learning that is concerned with domain models that exhibit both uncertainty (which can be dealt with using statistical methods) and complex, relational structure. Typically, the knowledge representation formalisms developed in SRL use (a subset of) first-order logic to describe relational properties of a domain in a general manner (universal quantification) and draw upon probabilistic graphical models (such as Bayesian networks or Markov networks) to model the uncertainty; some also build upon the methods of inductive logic programming. Significant contributions to the field have been made since the late 1990s. As is evident from the characterization above, the field is not strictly limited to learning aspects; it is equally concerned with reasoning (specifically probabilistic inference) and knowledge representation. Therefore, alternative terms that reflect the main foci of the field include ''statistical relational learning and reasoning'' (emphasizing the importance of reasoning) and ''first-order probabilistic languages'' (emphasizing the key properties of the languages with which models are represented). == Canonical tasks == A number of canonical tasks are associated with statistical relational learning, the most common ones being〔Matthew Richardson and Pedro Domingos, ( "Markov Logic Networks. )" ''Machine Learning'', 62 (2006), pp. 107–136.〕 * collective classification, i.e. the (simultaneous) prediction of the class of several objects given objects' attributes and their relations * link prediction, i.e. predicting whether or not two or more objects are related * link-based clustering, i.e. the grouping of similar objects, where similarity is determined according to the links of an object, and the related task of collaborative filtering, i.e. the filtering for information that is relevant to an entity (where a piece of information is considered relevant to an entity if it is known to be relevant to a similar entity). * social network modelling * object identification/entity resolution/record linkage, i.e. the identification of equivalent entries in two or more separate databases/datasets 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Statistical relational learning」の詳細全文を読む スポンサード リンク
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