Conceptual views for entity-centric search: turning data into meaningful concepts

Abstract

The storage, management, and retrieval of entity data has always been among the core applications of database systems. However, since nowadays many people access entity collections over the Web (e.g., when searching for products, people, or events), there is a growing need for integrating unconventional types of data into these systems, most notably entity descriptions in unstructured textual form. Prime examples are product reviews, user ratings, tags, and images. While the storage of this data is well-supported by modern database technology, the means for querying it in semantically meaningful ways remain very limited. Consequently, in entity-centric search suffers from a growing semantic gap between the users' intended queries and the database's schema. In this paper, we introduce the notion of conceptual views, an innovative extension of traditional database views, which aim to uncover those query-relevant concepts that are primarily reflected by unstructured data. We focus on concepts that are vague in nature and cannot be easily extracted by existing technology (e.g., business phone and romantic movie). After discussing different types of concepts and conceptual queries, we present two case studies, which illustrate how meaningful conceptual information can automatically be extracted from existing data, thus enabling the effective handling of vague real-world query concepts.

Querying concepts in product data by means of query expansion

“…As we have shown in [13] the main differentiating factors are the clarity of the query term and the level of user consensus over the expected result. These factors span a design space (presented in Fig.…”