Hiroko Kinutani

An efficiently updatable index scheme for structured documents

We propose an efficiently updatable index scheme for XML documents. This index scheme consists of four types of indices. Content index manages occurrence positions of words, element names, attribute names and attribute values. Local structure index manages logical structure of each document. Global structure index summarizes logical structure of document instances for a DTD. The last one is called structure meta index, and keeps, for each element, the number of local structure indices and global structure indices which include the element. Using our four types of indices, therefore, a wide range of queries over structured documents can be processed efficiently. We compare our indices with existing indices in terms of index size, update cost and retrieval cost.

Information Retrieval System for XML Documents

In the research field of document information retrieval, the unit of retrieval results returned by IR systems is a whole document or a document fragment, like a paragraph in passage retrieval. IR systems based on the vector space model compute feature vectors of the units and calculate the similarities between the units and the query. However, the unit of retrieval results are not suitable for document information retrieval since they are not congruent with the information which users are searching for. Therefore, the unit of retrieval results should be a portion of the XML document, such as a chapter, section, or subsection. That is, we think the most important concern of document information retrieval is to define the unit of retrieval results, that is meaningful for users. It is easy to construct the appropriate portion of XML documents as retrieval results because XML is a standard document format on the Internet and because XML documents consist of contents and document structures. In this paper, we propose an effective IR system for XML documents that automatically defines an appropriate unit of retrieval results by analyzing the XML document structure. We performed experimental evaluations and verified the effectiveness of our XML IR system. In addition, we also defined new recall and precision measures for XML information retrieval in order to evaluate our XML IR system.

Implementation of a high-speed and high-precision XML information retrieval system on relational databases

This paper describes an XML information retrieval system that we have developed. It is based on a vector space model, and implemented on top of XRel, a relational XML database system that has been developed in our research group. When a query is processed, a large number of fragments are retrieved, because a single XML document usually contains many XML fragments. Keeping all XML fragments degrades retrieval precision and increases query processing time, because some XML fragments are not appropriate as a query target. In existing methods, retrieval targets are manually selected by human experts when an XML collection is stored in the system. Such manual selection is not feasible when many kinds of XML documents are stored in the system. To cope with the problem we propose a method for automatically selecting document-centric fragments by introducing three measurements, namely, period ratio, number of different words, and empirical rules. By deleting inappropriate data-centric fragments from results of keyword query, we can improve the accuracy and performance of our system. Through performance evaluations, we confirmed the improvement of retrieval precision and query processing speed.

Identifying result subdocuments of XML search conditions

XML is becoming widely used as a standard data format on the Web. Unlike SGML, XML documents do not require having their schemas. Since design of schemas of XML documents is not an easy task, a significant number of XML documents will be simply well-formed. We focus on miscellaneous well-formed XML documents with no common schemas. We articulate issues related to query processing of those well-formed XML documents using standard data formats or vocabularies as namespaces. We believe that an end-users typical queries against XML databases will be very terse as found in current HTML searching engines. However, unlike HTML search engines, XML database systems should return appropriate XML subdocuments as a granule of query results. The authors formulate a class of queries, which is a counterpart of a simple class of queries in current HTML search engines. Then, we define a new function which serves as a basis for identifying appropriate XML subdocuments as results of such queries and we introduce indices in order to process such queries efficiently.

Extraction of Partial XML Documents Using IR-Based Structure and Contents Analysis

As Internet technologies develop, XML is becoming widely used as a standard data/document format. Although the use of XML documents has attracted public attention, the application of IR technologies in XML document retrieval is still in its premature stage. We foresee that typical XML queries for end-users will be very terse, like those used with current Web search engines. Therefore, an XML search engine should be able to search appropriate retrieval results using only a few keywords. In this paper, we introduce a notion of context nodes. Context nodes are used to automatically extract coherent partial documents without the knowledge of XML document structures. This method is useful because it does not require domain analysts to analyze DTDs and specify candidate partial documents beforehand. We use the term “context search” to represent search methods which employ the notion of context node. As an instantiation of context search methods, we have developed algorithms to identify result partial documents in the vector space model. We made a performance evaluation to verify the effectiveness of our method.

Metadata management for integration and analysis of earth observation data

Earth observation technologies have developed rapidly during the last decades. Substantial amounts of earth observation data have been acquired and stored among the literature and databases of various research fields such as climatology, oceanography, agriculture, and ecology. Analysis and integration of such data might produce valuable data products to promote understanding of the global environment, and to solve global environmental issues. However, most institutions store and manage their Earth observation data in their own manner, with little metadata. Scientists have to struggle hard to search for valuable data from data out of their research domain and seek their usage. In this paper we introduce a conceptual model for earth observation data. Utilizing a model to express earth observation item associated with ontologies, The model is an simple quintuple with information extracted from conventional data models, and is used to uniquely determine portions of earth observation data, which enables flexible annotation of earth observation data. We also introduce our systems for metadata management and user interfaces for encouraging user annotations of earth observation data that can help scientists discover and understand useful data that can support their research.