| Popis: |
A system for the content-based querying of large databases containing documents of different classes (texts, images, image sequences, audio signals) is introduced. Queries are formulated in natural language and are evaluated for their semantic contents. For the document evaluation, a knowledge model consisting of a set of domain specific concept interpretation methods is constructed. Thus, the semantics of both the query and the documents can be interconnected, i. e. the retrieval process searches for a match on the semantic Level (not merely on the Level of keywords or global image properties) between the query and the document. Methods from fuzzy set theory are used to find the matches. Furthermore, the retrieval methods associate information from different document classes (texts, images, ... ). To avoid the lass of information inherent to pre-indexing, documents need not be indexed; in principle, every search may be performed on the raw data under a given query. The system can therefore answer every query that can be expressed in the semantic model. To achieve the high data rates necessary for on-line analysis, dedicated VLSI search processors are being developed along with a parallel high-throughput media-server. A further speedup is achieved by a mediator module which maintains an intelligent result cache. This caching mechanism plays a similar role in HPQS as that of pre-computed indexes in more traditional retrieval systems. In this report, we outline the system architecture and detail specific aspects of the individual modules. Preliminary results, a brief overview of the current project status and a short discussion of further issues conclude the report. |
