Organization of Information System for Semantic Search Based on Associative Vector Space

In the BlueView project, digital library services are developed and partially implemented based on the architecture of virtual document servers. Using standard tools like full-text databases or information retrieval systems, object-relational database management systems, and replication and caching services, different heterogeneous local document servers have been integrated into one virtual server. Access to heterogeneous local systems can now be performed via a single query interface, integrating features of database query systems and information retrieval or search services. BlueView distributed query processing allows for combining structured queries against meta data with content-based (text retrieval) queries against full-text collections. Legacy systems such as existing preprint servers, search engines, or meta data (catalog) systems can also be integrated. Meta data and full-text documents can be replicated within this distributed architecture. In particular, in the view of members of a certain university, metadata and documents can be replicated to a local document server and integrated under different local user views. Aspects such as terms and conditions, licenses, diverse versions of documents, different formats, and data structures are considered as well. As application scenarios, preprint servers in Mathematics and the ACM SIGMOD Anthology as local document servers have been integrated into a virtual document server.

This paper investigates the impact of three approaches to XML retrieval: using Zettair, a full-text information retrieval system; using eXist, a native XML database; and using a hybrid system that takes full article answers from Zettair and uses eXist to extract elements from those articles. For the content-only topics, we undertake a preliminary analysis of the INEX 2003 relevance assessments in order to identify the types of highly relevant document components. Further analysis identifies two complementary sub-cases of relevance assessments (General and Specific) and two categories of topics (Broad and Narrow). We develop a novel retrieval module that for a content-only topic utilises the information from the resulting answer list of a native XML database and dynamically determines the preferable units of retrieval, which we call Coherent Retrieval Elements. The results of our experiments show that -- when each of the three systems is evaluated against different retrieval scenarios (such as different cases of relevance assessments, different topic categories and different choices of evaluation metrics) -- the XML retrieval systems exhibit varying behaviour and the best performance can be reached for different values of the retrieval parameters. In the case of INEX 2003 relevance assessments for the content-only topics, our newly developed hybrid XML retrieval system is substantially more effective than either Zettair or eXist, and yields a robust and a very effective XML retrieval.

The rapid development of information technology offers people a broad information-sharing platform. We can get relevant information through information retrieval tools. In order to get higher search efficiency, the information retrieval technologies based on semantic grid are discussed. Based on analysis of the technologies of grid, semantic grid and information retrieval, the architecture of grid information retrieval system is brought out and a common model of grid information retrieval is established. The grid platform based on Globus toolkit is built which is made as the basic development platform of the system, the semantic grid information retrieval system under that platform is realized, and the designed model and algorithm are implemented and the specific experimental tests is realized. The result shows that the system has better retrieval recall factor and pertinency factor than the conventional retrieval methods, and the feasibility of system model is verified.

The electronic government is a new application field for the Semantic Web, and the ontologies play a key role in the development of the Semantic Web. Consequently, if we focus on the law and the public policy that base the governments and build a documentation ontology that encompasses the electronic governments' needs of services at the front-office and mainly at the back office, we will provide a starting point to enable a broad class of semantic electronic government application. In this chapter, the authors show the e-Government Documentation Ontology as a part of the EGO model in order to describe how to access and manage official and non-official documentation across the governments. They also show a couple of applications using the documentation ontology on a semantic information retrieval system and on a semantic peer-to-peer.

Due to the increasing number of digital document repositories there is a heavy demand for information retrieval systems and therefore, information retrieval is still appearing as an emerging area of research. The information retrieval technology these days focuses on achieving better performance under different context by extracting documents most appropriate to the user’s query. Majority of the classical keyword based retrieval techniques does not focus on semantic meanings and therefore, are found to be less effective in reconstructing the actual information conveyed in the context. Also, retrieval of the relevant documents depends on appropriate analysis of the query terms. As words are polysemic, their actual meanings are influenced by their relationships with other words and their syntactic roles in the sentence. This work presents a fuzzy-cluster based semantic information retrieval model that considers these relationships to determine the exact meaning of the user query and extracts relevant documents as per their relevance scores.

Through the research on the demand of archives utilization, it shows that the full-text retrieval strategy based on the original documents of university archives has some defects, such as bloated index library, slow retrieval speed and difficult to locate page numbers. By extracting abstract information from original documents, this paper expands the information quantity of archives structured data, and constructs a full-text retrieval system based on structured data using Lucene. Through theoretical analysis and simulation comparison, it's shown that the index library is greatly reduced, the retrieval speed is significantly improved, and there's only little change of the recall.

Difficulties with Web based full text information retrieval (IR) systems include spurious matches, manually intensive document sifting, and the absence of communication or coordination between users. To address these difficulties, we introduce the Annotate! system which enables document annotations, and captures global usage history. Annotate! provides improved data and metadata clues to guide the user in a search session. Two data sets, declared in XML, are at the core of Annotate!: discussion data, a composite of documents and user annotations and session data which captures user timings at the various interface layers. We discuss a prototype implementation, and show that the collaborative infrastructure enabled by Annotate! can be predicted to improve the diffusion of ideas in the search community.

With rapid development of Internet technology gradually penetrate to all areas; the number of electronic documents is largely increasing, resulting in contradictions between information overload and people’s desire for knowledge. How to help users take full advantage of the existing full-text retrieval system to get required documents quickly and efficiently from the mass electronic literature has become the objective to the service providers of current digital academic literature. This paper, based on the analysis of relevant users’ interest in behavior modeling technology and main recommended algorithm both domestic and abroad; the current status of full-text retrieval system and characteristics of users, raises a users’ behavior-based personalized full-text retrieval system recommended algorithm. Its main idea is, under the environment of full-text search system, to collect and use users’ implicit behavior data to build their interest model and conduct overall design on the prototype of personalized literature recommendation system supported by the theoretical model.

Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms.

Uyghur is a low resourced language, but Uyghur Information Retrieval (IR) is getting more and more important recently. Although there are related research results and stem-based Uyghur IR systems, it is always difficult to obtain high-performance retrieval results due to the limitations of the existing stemming method. In this paper, we propose a character-based N-gram model and the corresponding smoothing algorithm for Uyghur IR. A full-text IR system based on character N-gram model is developed using the open-source tool Lucene. A series of experiments and comparative analysis are conducted. Experimental results show that our proposed method has the better performance compared with conventional Uyghur IR systems.

The exploding number of computational models produced by Systems Biologists over the last years is an invitation to structure and exploit this new wealth of information. Researchers would like to trace models relevant to specific scientific questions, to explore their biological content, to align and combine them, and to match them with experimental data. To automate these processes, it is essential to consider semantic annotations, which describe their biological meaning. As a prerequisite for a wide range of computational methods, we propose general and flexible similarity measures for Systems Biology models computed from semantic annotations. By using these measures and a large extensible ontology, we implement a platform that can retrieve, cluster, and align Systems Biology models and experimental data sets. At present, its major application is the search for relevant models in the BioModels Database, starting from initial models, data sets, or lists of biological concepts. Beyond similarity searches, the representation of models by semantic feature vectors may pave the way for visualisation, exploration, and statistical analysis of large collections of models and corresponding data.

Information retrieval (IR) systems aim to select relevant material from large collections of information in response to user queries. The approaches used to accomplish this have been the focus of much research and development over the past 50 years, and have led to the algorithms underlying many commercial and Web-based search engines today. This entry describes the common components that go into the design of IR systems (from text processing to inverted file indexes). The major classes (or models) of retrieval algorithms (Boolean, vector, and probabilistic) are described along with formal definitions of the basic form of these algorithms and some of the variations in common use in IR research. In addition, the entry examines query expansion techniques, and in particular relevance feedback, and how they are used in IR systems

Have the recently introduced outside database, full text retrieval systems helped alleviate editorial page writers' time pressure problems? This study surveyed database retrieval use by 40 writers identified as employees of newspapers that had systems. Results showed 73% of the writers agreed or strongly agreed that lack of time to do adequate research was their biggest problem, but 68% said they seldom or never used the systems. Reasons given for non-use included institutional and personal constraints, and inadequate training.

Non-Learning Semantic Analysis for Context Discovery and Sentiment Estimation: Transportation Application by Himanshu Verma 〈Dr. Pushkin Kachroo〉, Examination Committee Chair Professor of Electrical and Computer Engineering University of Nevada, Las Vegas With enormous amount of linguistic data present on web, text analysis has become one of the major fields of interest today. This field includes sentiment analysis, information retrieval, text document classification, knowledge based modeling, content similarity measure, data clustering, words prediction/correction, decision making etc. Managing and processing such data has vital importance. The field being quite broad, our focus is mainly on transportation related social media(Twitter) data extraction, text categorization/classification which can be further sub-divided into concept discovery, word sense disambiguation and sentiment analysis to analyze performance of existing transportation system worldwide. Concept discovery is the method of extracting the actual concept/context in which the text is about. This also allows us to filter irrelevant data. Word sense disambiguation is to find the correct sense in which a word is being used in a sentence. It is the basic necessity for concept discovery. iii A lot of research has been done in this field with major improvements. However, when it comes to short texts, the field still seems in nascent stage. Moreover, most of the methods today require huge amount training corpus(database). Arranging such corpus is a cumbersome task and requires a lot of human effort. The other problem with the existing methods are that they require a set of defined concepts from which a concept is chosen and labeled to text. We will consider the case of finding a general context. In this work a novel approach has been proposed for word sense disambiguation which in turn allows us to find general context. For this purpose, I have used the existing knowledge based semantic dictionary called WordNet. This methodology helps in avoiding the use of huge corpus and works for general context recognition. Our focus is on short-texts(Tweets) but the concept is easily applicable to text documents as well. Sentiment measuring technique was applied on extracted data and the scores were mapped to google maps based on the location information present in the tweets. This clearly points out the locations where people are more frustrated with the existing transportation system and need immediate attention for improvements.

MEDIS, an online full-text and bibliographic medical retrieval system, was introduced by Mead Data Central to the health community in May 1985. A general description of the service is offered covering the database content, search commands and strategies, equipment, cost and support services. Strengths and weaknesses of this new end user system are reviewed from a user's perspective. Comparisons among MEDIS, BRS/Saunders Colleague, and PaperChase are drawn.