Semistructured Data Search

Abstract

Abstract. This paper presents a selection of methods for searching in heterogeneous data collections where some amount of structure is available. We start with a general retrieval framework, based on generative probabilistic modeling, for ranking unstructured document representations. Then, we consider structure at two different levels: documents and queries. For documents, the internal structure is captured through the use of multiple document fields, and various approaches to setting field weights are discussed. For queries, the focus is on effectively utilizing additional input data that the user might provide along with the keyword query, such as target categories or example documents. We place a particular emphasis on methods that are robust with respect to the availability of structured data and are able to deal with inconsistent or incomplete information.

Introduction

“…These queries are very precise. However, formulating them requires a "knowledge of the underlying schema as well as that of the query language" [3]. Structured queries are primarily intended for expert users and well-defined, precise information needs.…”

“…We use the term keyword++ query (coined in [3]) to refer to keyword queries that are complemented with additional structural elements. For example, when users supply target categories or various filters via faceted search interfaces, those extra pieces of input constitute the ++ part.…”

“…Typically, it contains tags or other types of markup to separate textual content from semantic elements. Semi-structured data is "self-describing," i.e., "the schema is contained within the data and is evolving together with the content" [3]. Structured data adheres to a predefined (fixed) schema and is typically organized in a tabular format-think of relational databases.…”