this paper, we address the resource discovery issue, which consists of two subtasks, namely database detection and database selection. Database detection can be performed relatively easily, either by exploiting the name conventions used in the domain name service of the internet (e.g. names of ftp servers should start with `ftp.', names of Web servers with `www.') or by establishing central registries (e.g. the directory-of-servers for WAIS systems)

In the logical approach to information retrieval (IR), retrieval is considered as uncertain inference.

The aggregated structure of documents plays a key role in full-text, multimedia, and network Information Retrieval (IR). Considering aggregation provides new querying facilities and improves retrieval effectiveness. We present a knowledge representation for IR purposes which pays special attention to this aggregated structure of objects. In addition, further features of objects can be described. Thus, the structure of full-text documents, the heterogeneity and the spatial and temporal relationships of objects typical for multimedia IR, and meta information for network IR are representable within one integrated framework. The model we propose allows for querying on the content of documents (objects) as well as on other features. The query result may contain objects having different types. Instead of retrieving only whole documents, the retrieval process determines the least aggregated entities that imply the query. 1 Motivation and Background New IR applications like full-text, multime...

In this paper, we show that there is a mismatch between information retrieval (IR) and database (DB) concepts, and we devise solutions for this problem. DB oriented approaches have to distinguish between the logical and the content structure of objects, and should also consider the layout structure. Data independence — not regarded in IR before — can be achieved by using the notion of vague predicates. Since IR is based on uncertain inference, data models with uncertainty are required for an integrated IR-DB system. For this purpose, we present a probabilistic relational algebra. As extensions, probabilistic Datalog yields a more expressive query language, whereas a probabilistic nested relational model is more appropriate for modelling document structures. 1

We describe the design and implementation of a system for logic-based multimedia retrieval. As highlevel logic for retrieval of hypermedia documents, we have developed a probabilistic object-oriented logic (POOL) which supports aggregated objects, different kinds of propositions (terms, classifications and attributes) and even rules as being contained in objects. Based on a probabilistic four-valued logic, POOL uses an implicit open world assumption, allows for closed world assumptions and is able to deal with inconsistent knowledge. POOL programs and queries are translated into probabilistic Datalog programs which can be interpreted by the HySpirit inference engine. For storing the multimedia data, we have developed a new basic IR engine which yields physical data abstraction. The overall architecture and the flexibility of each layer supports logic-based methods for multimedia information retrieval.

This paper discusses a different application: improving information retrieval through name recognition. It investigates name recognition accuracy, and the effect on retrieval performance of indexing and searching personal names differently from non-name terms in the context of ranked retrieval. The main conclusions are: that name recognition in text can be effective; that names occur frequently enough in a variety of domains, including those of legal documents and news databases, to make recognition worthwhile; and that retrieval performance can be improved using name searching.

Describing the information retrieval task as computing the probability P (d ! q) that a document d implies a query q has become a key issue of theoretical information retrieval research work. We introduce HySpirit as a flexible system for describing the retrieval process as probabilistic implication and for representing the diverse knowledge dimensions of multimedia documents. HySpirit supports the description of varying information retrieval strategies and thus allows for studying the retrieval effectiveness of the probabilistic implication practically. It is designed for coping with large data sets, which in the past often prohibited the application of powerful and expressive probabilistic reasoning for experimental information retrieval tests. We show the scope of HySpirit along with a knowledge representation suitable for complex and heterogeneous document collections and we demonstrate the description of retrieval functions for calculating the probability P (d ! q). 1 Introductio...

This thesis describes a probabilistic model for optimum information retrieval in a distributed heterogeneous environment. The model assumes the collection of documents offered by the environment to be hierarchically partitioned into subcollections. Documents as well as subcollections have to be indexed. At this, indexing methods using different indexing vocabularies can be employed. A query provided by a user is answered in terms of a ranked list of documents. The model determines a procedure for ranking the documents that stems from the Probability Ranking Principle: For each subcollection, the subcollection's elements are ranked; the resulting ranked lists are combined into a final ranked list of documents, where the ordering is determined by the documents' probabilities of being relevant with respect to the user's query. Various probabilistic ranking methods may be involved in the distributed ranking process. The underlying data volume is arbitrarily scalable. A criterion for effect...

Medoc supports searching and browsing in distributed, heterogeneous digital libraries and bibliographic databases.

In this paper a new model and architecture for information retrieval in a widely distributed heterogenous multimedia document collection is described. The model generalizes existing probabilistic models for non-distributed information retrieval. The architecture is a conceptual realization of this model. It is hierarchically built in order to provide extendability and scalability and designed to integrate existing dynamic multimedia databases. Keywords: Information Retrieval, Multimedia databases, Probabilistic Models, Distributed Systems 1 Introduction The internet provides access to a large amount of data which is growing from day to day. Most of the data are simple text documents, but the fraction of multimedia data like image, audio and video documents is increasing rapidly. Hence, for users, it is getting more and more difficult to find documents containing relevant information. This is true especially for multimedia documents, because often one cannot clearly decide whether a d...