Traditionally, popular synonym acquisition methods are based on the distributional hypothesis, and a metric such as Jaccard coefficients is used to evaluate the similarity between the contexts of words to obtain synonyms for a query. On the other hand, when one tries to compile and clean a thesaurus, one often already has a modest number of synonym relations at hand. Could something be done with a half-built thesaurus alone? We propose the use of spectral methods and discuss their relation to other network-based algorithms in natural language processing (NLP), such as PageRank and Bootstrapping. Since compiling a thesaurus is very laborious, we believe that adding the proposed method to the toolkit of thesaurus constructors would significantly ease the pain in accomplishing this task.

This paper describes the architecture to implement an application in network environments, which adapts to unexpected change in the development phase. In this architecture, an application is expressed as an agent which consists of two layers: base level and meta level. The base level program is an application program and the meta level program is the program that controls the execution of the base level and changes the base level program. Virtual places are also provided in the network. They are used for the release of programs and information which agents retrieve to change their own base level program. An application (or an agent), when a change is required, moves from places to places for the retrieval of programs to adapt to the change. A program search strategy is introduced to adapt to changes by using distributed thesauri of released programs, which realizes an agent's program retrieval method in network environments.

In this paper, we will discuss a map synthesis system which handles static information (geographic objects) as well as dynamic information (traffic conditions, weather, etc. ). In addition to geographic thesauruses used in the previous systems, we will use co-existence relationships to improve the quality of maps generation. The system is considered to be general purpose (not restricted to car navigation nor travel maps) and can generate arbitrary maps according to the user's specification. It is very difficult for a user to specify a query which corresponds to the required map, because map description is not easy. The system should automatically generate missing information or find errors in the user specification. For the purpose we use geographic domain thesauruses which contain aggregation and other geographic relationships as well as conventional thesaurus hierarchy. In this paper, we will discuss to use co-existence relationships to enhance ability to select geographic objects automatically. Co-existence specifies relationships among geographic objects which should appear in a map together although they may not have geographic relationship by thesauruses. By utilizing co-existence relationships, a user can acquire much more understandable maps.

Queries to generate a map from geographic detabases are too large and complex for users to specify all components in it. Thematic parts of the map should be described by users as ad hoc queries. However, background parts of it should be inferred from users' queries corresponding to the thematic parts. Furthermore, it is important for the map systems to lead users' constructing their ad hoc queries and to infer visualization methods applied to the data retrieved by the ad hoc queries. This paper discusses a framework to infer supplemental queries and visualization methods in order to make the retrieval results into a feasible map using geographic domain hierarchical levels, geographic domain thesauruses and existing example queries. The framework allows users to know mismatches of components in queries, inappropriate queries for maps, and deriving candidates for additional components in queries.

Recently, a flexible image database retrieval system where image keywords can be captured automatically is strongly required, in order to manage a practical number of image data successfully. However, image recognition/understanding technology level is not generally sufficient enough to achieve this requirement. In order to overcome this problem, a new type of image database framework is proposed in this paper. In the proposed system, image keywords are extracted in fully-automated fashion by the flexible and generalized image recognition system. Image keywords employed in this system are a collection of recognized objects in the image, where achieved recognition levels are allowed to be intermediate or imperfect. The concept of recognition thesaurus" has been introduced to manage these various abstraction level of kerwords successfully. As an embodiment of this concept, an experimental image database with various types of sports scenes has been implemented and various retrieval evaluations have been performed. Experimental results reveal the effectiveness of the proposed method.

An information retrieval system based on a dynamic thesaurus was developed utilizing the connectionist approach. The dynamic thesaurus consists of nodes, which represent each term of a thesaurus, and links, which represent the connections between nodes. Term information that is automatically extracted from user's relevant documents is used to change node weights and generate links. Thus, node weights and links reflect a user's interest. A document retrieval experiment using the dynamic thesaurus was conducted in which both a high recall rate and a high precision rate were achieved.