Search (8 results, page 1 of 1)

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Abstract

This paper addresses the problem of information discovery in large collections of text. For users, one of the key problems in working with such collections is determining where to focus their attention. In selecting documents for examination, users must be able to formulate reasonably precise queries. Queries that are too broad will greatly reduce the efficiency of information discovery efforts by overwhelming the users with peripheral information. In order to formulate efficient queries, a mechanism is needed to automatically alert users regarding potentially interesting information contained within the collection. This paper presents the results of an experiment designed to test one approach to generation of such alerts. The technique of latent semantic indexing (LSI) is used to identify relationships among entities of interest. Entity extraction software is used to pre-process the text of the collection so that the LSI space contains representation vectors for named entities in addition to those for individual terms. In the LSI space, the cosine of the angle between the representation vectors for two entities captures important information regarding the degree of association of those two entities. For appropriate choices of entities, determining the entity pairs with the highest mutual cosine values yields valuable information regarding the contents of the text collection. The test database used for the experiment consists of 150,000 news articles. The proposed approach for alert generation is tested using a counterterrorism analysis example. The approach is shown to have significant potential for aiding users in rapidly focusing on information of potential importance in large text collections. The approach also has value in identifying possible use of aliases.

Source

Proceedings of the Fourth Workshop on Link Analysis, Counterterrorism, and Security, SIAM Data Mining Conference, Bethesda, MD, 20-22 April, 2006. [http://www.siam.org/meetings/sdm06/workproceed/Link%20Analysis/15.pdf]

Type

a

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Date

11. 2.2011 18:22:25

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Abstract

An ontology is "an explicit conceptualization of a domain of discourse, and thus provides a shared and common understanding of the domain." We have been producing ontologies for millennia to understand and explain our rationale and environment. From Plato's philosophical framework to modern day classification systems, ontologies are, in most cases, the product of extensive analysis and categorization. Only recently has the process of building ontologies become a research topic of interest. Today, ontologies are built very much ad-hoc. A terminology is first developed providing a controlled vocabulary for the subject area or domain of interest, then it is organized into a taxonomy where key concepts are identified, and finally these concepts are defined and related to create an ontology. The intent of this paper is to show that domain analysis methods can be used for building ontologies. Domain analysis aims at generic models that represent groups of similar systems within an application domain. In this sense, it deals with categorization of common objects and operations, with clear, unambiguous definitions of them and with defining their relationships.

Type

a

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Abstract

Semantic Search has become an active research of Semantic Web in recent years. The classification methodology plays a pretty critical role in the beginning of search process to disambiguate irrelevant information. However, the applications related to Folksonomy suffer from many obstacles. This study attempts to eliminate the problems resulted from Folksonomy using existing semantic technology. We also focus on how to effectively integrate heterogeneous ontologies over the Internet to acquire the integrity of domain knowledge. A faceted logic layer is abstracted in order to strengthen category framework and organize existing available ontologies according to a series of steps based on the methodology of faceted classification and ontology construction. The result showed that our approach can facilitate the integration of inconsistent or even heterogeneous ontologies. This paper also generalizes the principles of picking appropriate facets with which our facet browser completely complies so that better semantic search result can be obtained.

Type

a

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Abstract

In this article we present a method for retrieving documents from a digital library through a visual interface based on automatically generated concepts. We used a vocabulary generation algorithm to generate a set of concepts for the digital library and a technique called the max-min distance technique to cluster them. Additionally, the concepts were visualized in a spring embedding graph layout to depict the semantic relationship among them. The resulting graph layout serves as an aid to users for retrieving documents. An online archive containing the contents of D-Lib Magazine from July 1995 to May 2002 was used to test the utility of an implemented retrieval and visualization system. We believe that the method developed and tested can be applied to many different domains to help users get a better understanding of online document collections and to minimize users' cognitive load during execution of search tasks. Over the past few years, the volume of information available through the World Wide Web has been expanding exponentially. Never has so much information been so readily available and shared among so many people. Unfortunately, the unstructured nature and huge volume of information accessible over networks have made it hard for users to sift through and find relevant information. To deal with this problem, information retrieval (IR) techniques have gained more intensive attention from both industrial and academic researchers. Numerous IR techniques have been developed to help deal with the information overload problem. These techniques concentrate on mathematical models and algorithms for retrieval. Popular IR models such as the Boolean model, the vector-space model, the probabilistic model and their variants are well established.
From the user's perspective, however, it is still difficult to use current information retrieval systems. Users frequently have problems expressing their information needs and translating those needs into queries. This is partly due to the fact that information needs cannot be expressed appropriately in systems terms. It is not unusual for users to input search terms that are different from the index terms information systems use. Various methods have been proposed to help users choose search terms and articulate queries. One widely used approach is to incorporate into the information system a thesaurus-like component that represents both the important concepts in a particular subject area and the semantic relationships among those concepts. Unfortunately, the development and use of thesauri is not without its own problems. The thesaurus employed in a specific information system has often been developed for a general subject area and needs significant enhancement to be tailored to the information system where it is to be used. This thesaurus development process, if done manually, is both time consuming and labor intensive. Usage of a thesaurus in searching is complex and may raise barriers for the user. For illustration purposes, let us consider two scenarios of thesaurus usage. In the first scenario the user inputs a search term and the thesaurus then displays a matching set of related terms. Without an overview of the thesaurus - and without the ability to see the matching terms in the context of other terms - it may be difficult to assess the quality of the related terms in order to select the correct term. In the second scenario the user browses the whole thesaurus, which is organized as in an alphabetically ordered list. The problem with this approach is that the list may be long, and neither does it show users the global semantic relationship among all the listed terms.
Nevertheless, because thesaurus use has shown to improve retrieval, for our method we integrate functions in the search interface that permit users to explore built-in search vocabularies to improve retrieval from digital libraries. Our method automatically generates the terms and their semantic relationships representing relevant topics covered in a digital library. We call these generated terms the "concepts", and the generated terms and their semantic relationships we call the "concept space". Additionally, we used a visualization technique to display the concept space and allow users to interact with this space. The automatically generated term set is considered to be more representative of subject area in a corpus than an "externally" imposed thesaurus, and our method has the potential of saving a significant amount of time and labor for those who have been manually creating thesauri as well. Information visualization is an emerging discipline and developed very quickly in the last decade. With growing volumes of documents and associated complexities, information visualization has become increasingly important. Researchers have found information visualization to be an effective way to use and understand information while minimizing a user's cognitive load. Our work was based on an algorithmic approach of concept discovery and association. Concepts are discovered using an algorithm based on an automated thesaurus generation procedure. Subsequently, similarities among terms are computed using the cosine measure, and the associations among terms are established using a method known as max-min distance clustering. The concept space is then visualized in a spring embedding graph, which roughly shows the semantic relationships among concepts in a 2-D visual representation. The semantic space of the visualization is used as a medium for users to retrieve the desired documents. In the remainder of this article, we present our algorithmic approach of concept generation and clustering, followed by description of the visualization technique and interactive interface. The paper ends with key conclusions and discussions on future work.

Content

The JAVA applet is available at <http://ella.slis.indiana.edu/~junzhang/dlib/IV.html>. A prototype of this interface has been developed and is available at <http://ella.slis.indiana.edu/~junzhang/dlib/IV.html>. The D-Lib search interface is available at <http://www.dlib.org/Architext/AT-dlib2query.html>.

Type

a

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Abstract

This paper discusses issues concerning the augmentation of thesaurus relationships, in light of new application possibilities for retrieval. We first discuss a case study that explored the retrieval potential of an augmented set of thesaurus relationships by specialising standard relationships into richer subtypes, in particular hierarchical geographical containment and the associative relationship. We then locate this work in a broader context by reviewing various attempts to build taxonomies of thesaurus relationships, and conclude by discussing the feasibility of hierarchically augmenting the core set of thesaurus relationships, particularly the associative relationship. We discuss the possibility of enriching the specification and semantics of Related Term (RT relationships), while maintaining compatibility with traditional thesauri via a limited hierarchical extension of the associative (and hierarchical) relationships. This would be facilitated by distinguishing the type of term from the (sub)type of relationship and explicitly specifying semantic categories for terms following a faceted approach. We first illustrate how hierarchical spatial relationships can be used to provide more flexible retrieval for queries incorporating place names in applications employing online gazetteers and geographical thesauri. We then employ a set of experimental scenarios to investigate key issues affecting use of the associative (RT) thesaurus relationships in semantic distance measures. Previous work has noted the potential of RTs in thesaurus search aids but also the problem of uncontrolled expansion of query term sets. Results presented in this paper suggest the potential for taking account of the hierarchical context of an RT link and specialisations of the RT relationship

Type

a

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Type

a

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Abstract

Daffodil is a front-end to federated, heterogeneous digital libraries targeting at strategic support of users during the information seeking process. This is done by offering a variety of functions for searching, exploring and managing digital library objects. However, the distributed search increases response time and the conceptual model of the underlying search processes is inherently weaker. This makes query formulation harder and the resulting waiting times can be frustrating. In this paper, we investigate the concept of proactive support during the user's query formulation. For improving user efficiency and satisfaction, we implemented annotations, proactive support and error markers on the query form itself. These functions decrease the probability for syntactical or semantical errors in queries. Furthermore, the user is able to make better tactical decisions and feels more confident that the system handles the query properly. Evaluations with 30 subjects showed that user satisfaction is improved, whereas no conclusive results were received for efficiency.

Type

a