Traditional Keyword Search Research Articles

AMARA: An affective agent to enhance users' enjoyment and engagement with online art collections

AbstractThis demonstration presents AMARA, a novel interactive system to help novice art enthusiasts browse online art collections. Currently being developed for the Indianapolis Museum of Art website, the system uses an embedded interactive agent who asks users a series of simple and straightforward multiple‐choice questions regarding users' current feelings, preferences, and interests in art. The answers to each question are mapped to social tags, which are then used to retrieve and display relevant artworks the user may find interesting or appealing. Using AMARA, users can easily browse or search through online art collections without having to use a traditional keyword search, which requires extensive knowledge of art terminology or advanced subject expertise. Thus, AMARA was designed to enhance users' enjoyment and engagement with online art collections and assist users in discovering their known and unknown preferences in art.

Examining Question-Answering Technology from the Task Technology Fit Perspective

The World Wide Web has become a vital supplier of information for organizations in order to carry on such tasks as business intelligence, security monitoring, and risk assessments. By utilizing the task-technology fit (TTF) theory, we investigate the issue of when open-domain question-answering (QA) technology would potentially be superior to general-purpose Web search engines. Specifically, we argue theoretically and back up our arguments with a user study that the presence of fusion (information synthesis) is crucial to warrant the use of QA. At the same time, many information seeking tasks do not require fusion and, thus, are adequately served by traditional keyword search portals (Google, MSN, Yahoo, etc.). This explains why prior attempts to demonstrate the value of QA empirically were unsuccessful. We also discuss methodological challenges to any empirical investigation of QA and present several solutions to those challenges, validated with our user study. In order to carry our study, we created a novel prototype by following the Design Science guidelines. Our prototype is the first of its kind and is capable of answering list questions, such as What companies own low orbit satellites? or In which cities have illegal methyl-methionine labs been found? This investigation is only a precursor to a full-scale empirical study, but it serves as a medium to overview the state of the art QA technologies and to introduce important theoretical and empirical concepts involved. Although we did not find empirical evidence that one technology is uniformly better than the other, we discovered that once the user accumulates experience using QA, he/she can make an intelligent decision whether to use it for a particular task, which leads to the user to be more productive on average with the same tasks compared to when there is no choice of technology.

Semantic retrieval: an approach to representing, searching and summarising text documents

Nowadays, the internet is the major source of information for millions of people. There are many search tools available on the net but finding appropriate text information is still difficult. The retrieval efficiency of the presently used systems cannot be significantly improved: ‘bag of words’ interpretation causes losing semantics of texts. We applied the functional approach to represent English text documents. It allows taking into account semantic relations between words when indexing documents and use ordinary English sentences as queries to a search engine. The proposed retrieval mechanisms return only highly relevant documents. They make it possible to generate content-aware summaries on-the-fly. The presented examples illustrate the advantage of the discussed approach compared to the traditional key word search.

VisiNav: A system for visual search and navigation on web data

Abstract Web standards such as RDF (Resource Description Framework) facilitate data integration over large number of sources. The resulting interlinked datasets describe objects, their attributes and links to other objects. Such datasets are amenable for queries beyond traditional keyword search and for visualisation beyond a simple list of links to documents. Given that data integrated from the open web exhibits enormous variety in scope and structure, the mechanisms for interacting with such data have to be generic and agnostic to the vocabularies used. Ideally, a system operating on web data is easy to use without upfront training. To this end, we present VisiNav, a system based on an interaction model designed to easily search and navigate large amounts of web data (the current system contains over 18.5m RDF triples aggregated from 70k sources). In this paper we introduce a formal query model comprising four atomic operations over object-structured datasets: keyword search, object focus, path traversal, and facet specification. From these atomic operations, users incrementally assemble complex queries that yield trees of objects as result. These results can then be either directly visualised or exported to application programs or online services for further processing. The current system provides detail, list, and table views for arbitrary types of objects; and timeline and map visualisations for temporal and spatial aspects of objects.

VisiNav: A System for Visual Search and Navigation on Web Data

Web standards such as RDF (Resource Description Framework) facilitate data integration over large number of sources. The resulting interlinked datasets describe objects, their attributes and links to other objects. Such datasets are amenable for queries beyond traditional keyword search and for visualisation beyond a simple list of links to documents. Given that data integrated from the open web exhibits enormous variety in scope and structure, the mechanisms for interacting with such data have to be generic and agnostic to the vocabularies used. Ideally, a system operating on web data is easy to use without upfront training. To this end, we present VisiNav, a system based on an interaction model designed to easily search and navigate large amounts of web data (the current system contains over 18.5m RDF triples aggregated from 70k sources). In this paper we introduce a formal query model comprising four atomic operations over object-structured datasets: keyword search, object focus, path traversal, and facet specification. From these atomic operations, users incrementally assemble complex queries that yield trees of objects as result. These results can then be either directly visualised or exported to application programs or online services for further processing. The current system provides detail, list, and table views for arbitrary types of objects; and timeline and map visualisations for temporal and spatial aspects of objects.

Ontology-guided knowledge retrieval in an automobile assembly environment

In the body shop of an automobile assembly plant, having access to correct and timely information is very important in solving problems encountered in the assembly process. The Variation Reduction Adviser (VRA) system used within General Motors (GM) is a database containing problems encountered in this process and their possible solutions. The VRA acts as an electronic logbook that shares information across shifts within a plant as well as across multiple plants. The VRA also serves as a problem-solving tool by which solutions to problems encountered may be retrieved and reused. To function effectively as a problem-solving tool, it is important that relevant information is quickly retrieved from the VRA database. Traditionally, keyword-based retrieval strategies have been used. In these approaches, the user types in a list of words or phrases and those records in the database that contain those words or phrases exactly as typed are retrieved. The problem with this approach is that records containing words or phrases that are semantically related to the ones typed in but not exactly the same are not retrieved. For instance, if the user types “left-hand side,” the traditional keyword search will not find records that contain the abbreviation “LHS.” This paper describes a search mechanism based on a thesaurus (a simple version of an ontology) that overcomes this problem. It describes the standard criteria to measure the effectiveness of a search, defines a new criterion, and shows that in terms of these criteria, the ontology-guided approach gives better search results than the exact match mechanism. The results are shown in the context of real searches during the use of the VRA in a GM assembly plant.

Co-occurrence and ranking of entities based on semantic annotation

This paper presents an extension of the KIM semantic annotation and search platform called CORE: Co-Occurrence and Ranking of Entities. It enables popularity timeline analysis and a novel faceted search interface. The idea and its advantages to the traditional keyword search are explained along with the environment, technical description and use case scenarios. The essence of the approach is a specific indexing, performed on the basis of semantic annotation of text with respect to named entities and key-phrases forming a reduced-dimension feature space. This allows for a sort of semantic search through adaptation of standard probabilistic IR models where documents are also considered contexts.

A UDDI Search Engine for SVG Federated Medical Imaging Web Services

With more and more medical web services appearing on the web, web service's discovery mechanism becomes essential. UDDI is an online registry standard to facilitate the discovery of business partners and services. However, most medical imaging applications exist within their own protected domain and were never designed to participate and operate with other applications across the web. However, private UDDI registries in federated organizations should be able to share the service descriptions as well as to access them if they are authorized. The new initiatives on Federated Web Services Identity Management can resolve a range of both technical and political barriers to enable wide-scale participation and interoperation of separate domains into a singular, robust user experience. However, there is no widely acceptable standard for federated web services and most of the available venders frameworks concentrate only on the security issue of the federation leaving the issue of searching and discovering web services largely primitive. Federated web services security and web services searching are uniquely intertwined, mutually reliant on each other and are poised to finally solve a long-running problem in both IT and systems security. Traditional keyword search is insufficient for web services search as the very small text fragments in web services are unsuitable for keyword search and the underlying structure and semantics of the web service are not exploited. Engineering solutions that address the security and accessibility concerns of web services, however, is a challenging task. This article introduces an extension to the traditional UDDI that enables sophisticated types of searching based on a lightweight web services federated security infrastructure.

Information retrieval and knowledge discovery utilising a biomedical Semantic Web

Although various ontologies and knowledge sources have been developed in recent years to facilitate biomedical research, it is difficult to assimilate information from multiple knowledge sources. To enable researchers to easily gain understanding of a biomedical concept, a biomedical Semantic Web that seamlessly integrates knowledge from biomedical ontologies, publications and patents would be very helpful. In this paper, current research efforts in representing biomedical knowledge in Semantic Web languages are surveyed. Techniques are presented for information retrieval and knowledge discovery from the Semantic Web that extend traditional keyword search and database querying techniques. Finally, some of the challenges that have to be addressed to make the vision of a biomedical Semantic Web a reality are discussed.

User Models and Filtering Agents for Improved Internet Information Retrieval

Over the past few years, the amount of electronic information available through the Internet has increased dramatically. Unfortunately, the search tools currently available for retrieving and filtering information in this space are not effective in balancing relevance and comprehensiveness. This paper analyzes the results of experiments in which HTML documents are searched with user models and software agents used as intermediaries to the search. Simple user models are first combined with search specifications (or ’User Needs‘), to define an Enhanced User Need. Then Uniform Resource Agents are constructed to filter information based on the EUN parameters. The results of searches using different agents are then compared to those obtained through a comparable simple keyword search, and it is shown that a user searching a pool of existing agents can obtain better search results than by conducting a traditional keyword search. This work thus demonstrates that the use of user models and information filtering agents do improve search results and may be used to improve Internet information retrieval.