Probabilistic Models in Information Retrieval

Probabilistic Models in Information Retrieval

In this paper, an introduction and survey over probabilistic information retrieval (IR) is given. First, the basic concepts of this approach are described: the probability ranking principle shows that optimum retrieval quality can be achieved under certain assumptions; a conceptual model for IR along with the corresponding event space clarify the interpretation of the probabilistic parameters i...

Representations, Models and Abstractions in Probabilistic Information Retrieval

We show that most approaches in probabilistic information retrieval can be regarded as a combination of the three concepts representation, model and abstraction. First, documents and queries have to be represented in a certain form, e.g. as a sets of terms. Probabilistic models use certain assumptions about the distribution of the elements of the representation in relevant and nonrelevant docum...

A survey of probabilistic models in Information Retrieval

The paper provides an introduction to and survey of probabilistic approaches to modelling Information Retrieval. The basic concepts of probabilistic approaches to Information Retrieval are outlined, and the principles and assumptions upon which the approaches are based are presented. The various models that have been proposed in the development of IR are described, classi ed, and compared. The ...

Lavrenko Introduction to Probabilistic Models for Information Retrieval

Information Retrieval with Probabilistic Datalog

The probabilistic logical approach in Information Retrieval (IR) aims at describing the retrieval process as the computation of the probability P (d! q) that a document d implies a query q. Probabilistic Datalog (DatalogP ) is a logic that enables uncertain inference. We use DatalogP as a platform for investigating the probabilistic logical approach in IR. The expressiveness of DatalogP allows ...