Null Queries Research Articles

Investigating queries and search failures in academic search

Academic search concerns the retrieval and profiling of information objects in the domain of academic research. In this paper we reveal important observations of academic search queries, and provide an algorithmic solution to address a type of failure during search sessions: null queries. We start by providing a general characterization of academic search queries, by analyzing a large-scale transaction log of a leading academic search engine. Unlike previous small-scale analyses of academic search queries, we find important differences with query characteristics known from web search. E.g., in academic search there is a substantially bigger proportion of entity queries, and a heavier tail in query length distribution. We then focus on search failures and, in particular, on null queries that lead to an empty search engine result page, on null sessions that contain such null queries, and on users who are prone to issue null queries. In academic search approximately 1 in 10 queries is a null query, and 25% of the sessions contain a null query. They appear in different types of search sessions, and prevent users from achieving their search goal. To address the high rate of null queries in academic search, we consider the task of providing query suggestions. Specifically we focus on a highly frequent query type: non-boolean informational queries. To this end we need to overcome query sparsity and make effective use of session information.We find that using entities helps to surface more relevant query suggestions in the face of query sparsity. We also find that query suggestions should be conditioned on the type of session in which they are offered to be more effective. After casting the session classification problem as a multi-label classification problem, we generate session-conditional query suggestions based on predicted session type. We find that this session-conditional method leads to significant improvements over a generic query suggestion method. Personalization yields very little further improvements over session-conditional query suggestions.

Approximate answers to null queries with incomplete fuzzy information in object oriented database

Lập luận tương tự la một cong cụ suy diễn quan trọng trong nghien cứu của tri tuệ nhan tạo. Truy vấn Null la những truy vấn ma no nhận một cau trả lời Null từ hệ thống cơ sở dữ liệu, thường la vi thong tin khong đầy đủ trong cơ sở dữ liệu (CSDL). Lập luận tương tự thật sự rất la hữu ich để tạo ra một cau trả lời gần đung cho truy vấn Null. Lập luận tương tự sử dụng phụ thuộc ham mờ để trả lời cac truy vấn Null tren cơ sở dữ liệu quan hệ mờ đa được nghien cứu. Trong bai bao nay, chung toi trinh bay một cach tiếp cận để co được một cau trả lời gần đung cho truy vấn Null tren mo hinh CSDL hướng đối tượng (HĐT) với thong tin mờ va khong chắc chắn dựa tren đại số gia tử (ĐSGT). Phương phap tiếp cận của chung toi la dựa tren lan cận ngữ nghĩa của ĐSGT để xay dựng cac xac định mờ thuộc tinh va xac định mờ phương thức.

On a similarity measure between LR-type fuzzy numbers and its application to database acquisition

This article presents a new similarity measure for LR-type fuzzy numbers. The proposed similarity measure is based on a defined metric between LR-type fuzzy numbers. It is known that an exponential operation is highly useful in dealing with the classical Shannon entropy and cluster analysis. We adopted, therefore, the exponential operation on this metric. Furthermore, we analyze its properties and make numerical comparisons to several similarity measures. The results show that the proposed similarity measure can overcome the drawbacks of the existing similarity measures. We then apply it to compound attributes for handling null queries to database systems. These applications can also be widely used in fuzzy queries to databases. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1001–1016, 2005.

Answering Null Queries by Analogical Reasoning on Similarity-based Fuzzy Relational Databases

We present here a method of using analogical reasoning to infer approximate answers for null queries on similarity-based fuzzy relational databases. Null queries are queries that elicit a null answer from a database. Analogical reasoning assumes that if two situations are known to be similar in some respects, it is likely that they will be similar in others. Application of analogical reasoning to infer approximate answers for null queries using fuzzy functional dependency and fuzzy equality relation on possibility-based fuzzy relational database has been studied. However, the problem of inferring approximate answers has not been fully explored on the similarity-based fuzzy relational data model. In this work, we introduce the concept of approximate dependency and define a similarity measure on the similaritybased fuzzy model, as extensions to the fuzzy functional dependency and fuzzy equality relation respectively. Under the framework of reasoning by analogy, our method provides a flexible query answering mechanism for null queries on the similarity-based fuzzy relational data model.

Selected Papers from IFSA'99

The past few years we have witnessed a crystallization of soft computing as a means towards the conception and design of intelligent systems. Soft Computing is a synergetic integration of neural networks, fuzzy logic and evolutionary computation including genetic algorithms, chaotic systems, and belief networks. In this volume, we are featuting seven papers devoted to soft computing as a special issue. These papers are selected from papers submitted to the "The eighth International Fuzzy Systems Association World Congress (IFSA'99)", held in Taipei, Taiwan, in August 1999. Each paper received outstanding recommendations from its reviewers. G-H Tzeng et al. integrate fuzzy numbers, fuzzy regression, and a fuzzy DEA approach as a performance evaluation model for forecasting the productive efficiency of a set of production units when some data are fuzzy numbers. A case of Taipei City Bus Company is adopted for illustration. Y. Shi et al. adopts a fuzzy programming approach to solve a MCMDM (multiple criteria and multiple decision makers) capital budget problem. A solution procedure is proposed to systematically identify a fuzzy optimal selection of possible projects. N. Nguyen et al. propose a new formalism (Chu spaces) to describe parallelism and information flow. Chu spaces provide uniform explanations for different choices of fuzzy methodology, such as choices of fuzzy logical operations of membership functions or defuzzifications. M-C Su et al. propose a technique based on the SOM-based fuzzy systems for voltage security margin estimation. This technique was tested on 1604 simulated data randomly generated from operating conditions on the IEEE 30-bus system to indicate its high efficiency. By defining the concept of approximate dependency and a similarity measure, S-L Wang et al. present a method of using analogical reasoning to infer approximate answers for null queries on similarity-based fuzzy relational databases. K.Yeh et al. use adaptive fuzzy sliding mode control for the structural control of bridges. Combing fuzzy control and sliding mode control can reduce the complexity of fuzzy rule bases and ensure the stability and robustness. This model is demonstrated by three types of bridges, with LRB, sliding isolators and no isolation device. Based on a novel fuzzy clustering algorithm, Y-H Kuo et al. propose an adaptive traffic prediction approach to generalize and unveil the hidden structure of traffic patterns with features of robustness, high accuracy and high adaptability. The periodical, Poisson and real video traffic patterns have been used to verify their approach and investigate its properties. We would like to express our sincere gratitude to everyone who has contributed to this special issue including the authors, the co-reviewers, the JACI Editors-in-Chief Toshio Fukuda and Kaoru Hirota.

Generating Compound Attributes from Fuzzy Data for Null Queries

Abstract We present here an approach for handling null queries that contain compound attributes derived from fuzzy data. Null queries are queries that elicit a null answer from the database. Compound attributes are ambiguous attributes that are not defined in the original database schema but can be derived from multiple rigid attributes in schema. Compound attributes derived from simple numbers were studied by Nomura etc[8]. In [9,10], we extended the compound attributes to be derived from numbers, interval values, and scalars under a unified approach. In this work, we further extend compound attributes so that they can be derived from fuzzy sets of numbers, interval values, and scalars. Database management systems that can handle this type of ambiguous attributes in null queries not only can reduce the occurrences of null answers but also provide an improved user-friendly query environment.

Null Queries with Compound Fuzzy Attributes

We present a generalized approach for handling null queries that contain compound fuzzy attributes. Null queries elicit a null answer from the database. Compound fuzzy attributes are ambiguous attributes not defined in the original database schema but derived from multiple rigid attributes in a schema. Compound fuzzy attributes derived from simple numbers were studied by Nomura11). We extend compound fuzzy attributes so they can be derived from numbers, interval values, scalars, and sets of all these data types. Database management systems that handle this type of ambiguous attributes in null queries both reduce occurrences of null answers and provide an improved user-friendly query environment.

Approximate reasoning by analogy to answer null queries

Reasoning by analogy refers to recognizing certain similarities between a source situation/object and a target situation/object and deriving some properties of the target on the basis of observed similarities with the source. Analogy is an important inference tool in human cognition and is a powerful computational tool for general inference. Null queries are queries that elicit a null answer from the database system, often because of the incompleteness of information in the database, for example, the absence of certain attribute values for some objects. Analogy is useful for obtaining an approximate answer to a null query. In this paper, we develop the theoretical basis for the application of analogical reasoning to obtain approximate answers for null queries in the context of a fuzzy relational data model. The incorporation of analogical reasoning in data models enhances their user-friendliness. Our proposed model of analogy incorporates fuzzy logic and is a natural generalization of models of analogy researched in the domain of artificial intelligence.