Simple Keyword Queries Research Articles

Mapping research fields using co-nomination: the case of hyper-authorship heavy flavour physics

This paper introduces the use of co-nomination as a method to map research fields by directly accessing their knowledge networks organised around exchange relationships of intellectual influence. Co-nomination is a reputation-based approach combining snowball sampling and social network analysis. It compliments established bibliometric mapping methods by addressing some of their typical shortcomings in specific instances. Here we test co-nomination by mapping one such instance: the idiosyncratic field of CERN-based heavy flavour physics (HFP). HFP is a ‘hyper-authorship’ field where papers conventionally list thousands of authors alphabetically, masking individual intellectual contributions. We also undertook an illustrative author co-citation analysis (ACA) mapping of 2310 HFP articles published 2013–18 and identified using a simple keyword query. Both maps were presented to two HFP scientists for commentary upon structure and validity. Our results suggest co-nomination allows us to access individual-level intellectual influence and discern the experimental and theoretical HFP branches. Co-nomination is powerful in uncovering current and emerging research specialisms in HFP that might remain opaque to other methods. ACA, however, better captures HFP’s historical and intellectual foundations. We conclude by discussing possible future uses of co-nomination in science policy and research evaluation arrangements.

Implementation of the Intelligent Question Answering System Based on Remote Service Framework

Remote answering system of modern distance education system is an indispensable component. However, the current system is based on answering user input on the exam questions simple keyword matching, accuracy and query the user interface cannot meet the needs of users. We have designed a semantic network theory to build up intelligent answering system. We analyzed the necessity of establishing intelligent answering system, and presents a model of intelligent question answering system based on its technology defined areas, and two university computer courses as a source of knowledge to achieve the function of the system. The results show that the proposed method can improve the accuracy of the query, user-friendly and convenient. System Based on J2EE three-tier system, in the middle layer of the advanced MVC design pattern thought to achieve page display and business logic separation, improve system maintainability.

Exploiting semantics for searching agricultural bibliographic data

Filtering and search mechanisms which permit to identify key bibliographic references are fundamental for researchers. In this paper we propose a fully automatic and semantic method for filtering/searching bibliographic data, which allows users to look for information by specifying simple keyword queries or document queries, i.e. by simply submitting existing documents to the system. The limitations of standard techniques, based on either syntactical text search and on manually assigned descriptors, are overcome by considering the semantics intrinsically associated to the document/query terms; to this aim, we exploit different kinds of external knowledge sources (both general and specific domain dictionaries or thesauri). The proposed techniques have been developed and successfully tested for agricultural bibliographic data, which play a central role to enable researchers and policy makers to retrieve related agricultural and scientific information by using the AGROVOC thesaurus.

Efficient keyword search in fuzzy XML

Evaluation of keyword queries over XML documents is one of the most fundamental tasks for XML data management. Previous methods have focused on the processing of deterministic XML data. However, uncertain data are inherent in practical applications, and how to support efficient keyword search over fuzzy XML data remains at large an open problem. In this paper, we tackle the problem of efficiently producing SLCA (smallest lowest common ancestor) results for keyword queries in fuzzy XML documents. We propose an efficient approach that can find all SLCA results for a given keyword query over fuzzy XML data. In particular, we introduce an effective method to transform a simple keyword query into a segmented keyword query that captures the original query requirements and conforms to the underlying fuzzy XML data. The proposed approach could help us eliminate irrelevant SLCA results and speed up the query processing. The final experiments show the effectiveness and efficiency of our proposed approach in generating SLCA results.

Google vs. the Library (Part II): Student Search Patterns and Behaviors when Using Google and a Federated Search Tool

This study examines the information-seeking behavior of undergraduate students within a research context. Student searches were recorded while the participants used Google and a library (federated) search tool to find sources (one book, two articles, and one other source of their choosing) for a selected topic. The undergraduates in this study believed themselves to be skilled researchers, but their search queries and behaviors did not support this belief. Students did not examine their topics to identify keywords and related terms. They relied heavily on the language presented to them via the list of research topics and performed natural language or simple keyword or phrase queries. They did not reformulate or refine their research questions or search queries, did not move beyond the first page of results, and did not examine metadata to improve searches. When using Google, students frequently visited commercial sites such as Amazon; content farms such as About.com ; and subscription databases such as JSTOR. This study concludes by offering suggestions for search interface improvement and pedagogical opportunities on which librarians may wish to focus or refocus. This article is the second in a series that examines student use of Google and a library (federated) search tool.

A Keyword Query Method Based on Digital Type Properties

Now, the way to access the database in most still uses a simple keyword query method, it is generally based on semantic matching, rather than based on semantic matching. So it can't make full use of the semantic relationship between the data, and unable to obtain accurate results. This paper proposes a keyword query method based on digital type, it can largely improve the efficiency of user queries. We propose two query algorithm namely multi-table query and single table query in the database query time of uncertainty. Through experimental analysis, we can see that our algorithm is more effective than simple type keyword queries.

Sequoia—An Approach to Declarative Information Retrieval

In this work, we propose an approach that allows to query heterogeneous data sources on the Web in a declarative fashion. Such an approach gives means for a generic way to formulate various information needs, much more powerful than simple keyword queries. Particularly appealing is the ability to combine (join) information from different sources and the ability to compute simple statistics that can be used to select promising information pieces. What might sound like a hopeless effort due to the inherent complexity expressible by SQL-style queries is at second glance not complicated to understand and to use. Already very simple combinations (i.e., joins) of different data sources (i.e., tables) offer a surprisingly large set of interesting use cases. In particular, using sliding window joins that limit the scope of interest to recent information, obtained, for instance, from the live stream of Twitter Tweets. This goes far beyond keyword queries enriched with operators like allintext: or allintitle: or site:, as can be used, for instance, in the Google search engine.

Historical Information Retrieval Model based on Period Query Using Time-Variant Extraction of Historical Object

In providing historical information, current searching service requires users to do repetitive information retrieval and filtering process by supplying search results based on simple keyword query that does not reflect the characteristic of historical information. If the information retrieval service reflecting the feature of time-variant is provided, users would be able to save time and reduce unnecessary effort that is repetitively consumed to acquire desired historical information. Moreover, users can get the systemic retrieving result of historical information reflecting the phrase of the times. Hereupon, in this paper, we suggest theoretical Basis for historical information retrieval and provision by analyzing common character of historical information and defining historical object. Furthermore, we suggest a historical information retrieval model consist of three-classes that are 'Collecting Historical Information, Extracting Time-Variant and structuralization of Historical Information, The Information-Providing Space' to provide users with historical information based on particular period considering one of the common character of historical information. Suggested model, through Crawler and Wrapper, automatically collects historical information from Web and restructure collected historical information to time-series considering the aspects of 'period' and 'relation' that the historical information has. Finally, it carries out structuralization based on correlation of historical objects. Also, by retrieving historical information in particular period using 'Period Query', which can search information correspondent to time-variant, one of the features of historical information, users can obtain search results that dynamically change according to the chosen particular period, although they searched the same historical keyword. Therefore, users can effectively retrieve information by reducing complicated retrieve process when they look up historical information which has the feature of time-variant. In addition, by going through the structuring process, we provide not only required historical information but also additional information such as correlation of historical objects by information graphics that are intuitive user interface. Thus, users could effectively search and acquire historical information.

Federated Access to Heterogeneous Information Resources in the Neuroscience Information Framework (NIF)

The overarching goal of the NIF (Neuroscience Information Framework) project is to be a one-stop-shop for Neuroscience. This paper provides a technical overview of how the system is designed. The technical goal of the first version of the NIF system was to develop an information system that a neuroscientist can use to locate relevant information from a wide variety of information sources by simple keyword queries. Although the user would provide only keywords to retrieve information, the NIF system is designed to treat them as concepts whose meanings are interpreted by the system. Thus, a search for term should find a record containing synonyms of the term. The system is targeted to find information from web pages, publications, databases, web sites built upon databases, XML documents and any other modality in which such information may be published. We have designed a system to achieve this functionality. A central element in the system is an ontology called NIFSTD (for NIF Standard) constructed by amalgamating a number of known and newly developed ontologies. NIFSTD is used by our ontology management module, called OntoQuest to perform ontology-based search over data sources. The NIF architecture currently provides three different mechanisms for searching heterogeneous data sources including relational databases, web sites, XML documents and full text of publications. Version 1.0 of the NIF system is currently in beta test and may be accessed through http://nif.nih.gov.

The CIKM 2005 workshop on information retrieval in peer-to-peer networks

Peer-to-peer (P2P) networks have emerged as a popular way to build large scale information systems by using the principle of resource sharing. The P2P paradigm holds many promises, e.g. scalability, failure resilience and increased autonomy of nodes. For these reasons P2P seems also to be an interesting architectural paradigm for realizing large scale information retrieval systems. However, search methods in P2P networks are still mostly limited to simple keyword queries and the use of advanced retrieval models is in its infancy.

Manufacturing Organizational Memory: Logged Conversation Thread Analysis

Introduction Logged conversation must be conceived of as an OM (Organizational Memory) manufacturing process acquired during active discussions among conversation participants and logged into a hierarchy of text-format data. A mailing list provides a collection of informative correspondences, and its use as a source of organizational knowledge in business is widespread and obvious. To make this use easier, however, good search tools are required. The plethora of current and widespread search technologies can be classified into four categories: (1) Context-extended search using a thesaurus. In this approach, the query term is expanded in context utilizing words/clauses/phrases from a thesaurus to broaden the catchment space. (2) Query-by-example. Here, the user selects relevant document snippets, which are then used for the query base. (3) Keyword Search on full/partial text contents. 4) List Scan, by which the user sorts by date, author, and subject by listing within the structured list (See Figure 1). [FIGURE 1 OMITTED] Experimental Setting and Results Corpus Selection From the collection of 20 mailing lists gathered for this study, we found on analysis that they consist of three primary types: 1. In strongly-typed mailing lists, each reply or thread-initiating message can only start after the completion of a input. A typical example of this of mailing list is Organizational Memory Information Systems (OMIS), which force users to input a into the system, often presenting an unnecessary barrier. Such category type information can significantly enhance later OMIS search usability and effectiveness, but our observation has been that the threads do not extend to more than 10 messages. Here, we suspect the negative effect of the entry barrier outweighs usability, and we cannot argue that this of mailing list is good for our classification (Mark & Bordetsky, 1998). 2. A typical locating system allows each member to register and modify the index list pertaining to his or her specialized knowledge or experiences so that later users can locate subject matter experts by a simple keyword query. Each consultant will be registered, and the collected profiles form a thread in the mailing list. Users can add comments and feedback, rating the services received. Such registration data also constitutes a mailing list. It is rare here, as in (1), to see sufficiently long threads. Another drawback is that OM manufacturing does not happen in the locating system per se. Usually, the OM is created while messages are exchanged in a weakly typed message swap (Fisher, 2002). For these reasons, it is inadvisable to choose the locating system for analysis and classification. 3. The plain-vanilla free format or weakly-type-forced mailing lists is often used for hobby and special interest groups. In enterprise, employees form intra-net mailing list to assist OM creation and utilization (Hood, 2003). The mailing list we selected for analysis was borrowed from the amateur radio kit building community (Elecraft, 2003), where OM processes are actively performed and databases frequently accessed. Our list displays distinctive aspects arising from the nature of this community: 1. The list has been used since 1998 and the total number of messages per month increased constantly from the start. 2. The January 2003 list consists of 1406 messages by 311 members. As shown in Figure 2, 156 authors (50%) account for 86% of the messages, forming the list's active core. Fortyseven authors (15%) account for 54% of the messages. 3. Visitors and infrequent guests welcomed by expert advisers constitute 63% of the posters, posting only once or twice in the course of a typical month. Because of these well-behaved community aspects, OM created in the above list qualified as a relevant analysis corpus for our classification. …