A relevance model for a data warehouse contextualized with documents

Abstract

This paper presents a relevance model to rank the facts of a data warehouse that are described in a set of documents retrieved with an information retrieval (IR) query. The model is based in language modeling and relevance modeling techniques. We estimate the relevance of the facts by the probability of finding their dimensions values and the query keywords in the documents that are relevant to the query. The model is the core of the so-called contextualized warehouse, which is a new kind of decision support system that combines structured data sources and document collections. The paper evaluates the relevance model with the Wall Street Journal (WSJ) TREC test subcollection and a self-constructed fact database.

Introduction

During decades the information retrieval (IR) area has provided users with methods and tools for searching interesting pieces of text among huge document collections. However, until very recently these techniques have been implemented apart from databases due to the very different nature of the objects they manage: whereas data is well-structured with well-defined semantics, texts are unstructured and require approximate query processing (Baeza-Yates & Ribeiro-Neto, 1999).

Nowadays, corporate information systems need to include internal and external text-based sources (e.g., web documents) into the information processes defined within the organization. For example, decision support systems would greatly benefit from text-rich sources (e.g., financial news and market research reports) as they can help analysts to understand the historical trends recorded in corporate data warehouses. Opinion forums and blogs are also valuable text-sources that can be of great interest for enhancing the decision making processes. Unfortunately, there are scarce works in the literature concerned with a true integration of data and document retrieval techniques.

Recent proposals in the field of IR include language modeling (Ponte & Croft, 1998) and relevance modeling (Lavrenko & Croft, 2001). Language modeling represents each document as a language model. Thus, documents are ranked according to the probability of emitting the query keywords from the corresponding language model. Relevance modeling estimates the joint probability of the query’s keywords and the document words over the set of documents deemed relevant for that query. In this paper, we apply the language modeling and relevance modeling approaches to develop a new model that estimates the relevance of the facts stored into a data warehouse with respect to an IR query. These facts are well-structured data tuples, whose meaning is described by a set of documents retrieved with the same IR query from a separate text repository.

The proposed relevance model is the core of the contextualized warehouse described in Pérez, Berlanga, Aramburu, and Pedersen (2008). However, the topic of Pérez et al. (2008) was the multidimensional model of the contextualized warehouse, rather than the relevance model. In the current paper, we describe the relevance model in detail, and we compare it with the relevance-based language model techniques that support it. The paper provides a series of experiments over a well-known IR collection in order to demonstrate that the ranking of facts provided by the model is good enough for helping analysts in their tasks. This evaluation is completely new and has not been previously published anywhere. The review of the language modeling and relevance modeling approaches included in this paper is also an original contribution.

The rest of the paper is organized as follows: Section 2 overviews the contextualized warehouse. Section 3 reviews the language modeling and the relevance modeling IR approaches. Section 4 presents the contextualized warehouse relevance model and Section 5 evaluates it. Finally, Section 6 discusses some conclusions and future lines of work.