19200784
Searching Databases with Keywords
Traditionally, SQL query language is used to search the data in databases. However, it is inappropriate for end-users, since it is complex and hard to learn. It is the need of end-user, searching in databases with keywords, like in web search engines. This paper presents a survey of work on keyword search in databases. It also includes a brief introduction to the SEEKER system which has been developed.
39601037
Nchiql: a Chinese natural language query system to databases
This paper describes Nchiql, a Chinese natural language query system for databases. With a portable query interface to a Chinese database system, it resolves the problems of traditional database query systems and natural language interfaces. We discuss three subsystems in Nchiql: an intelligent user interface; a domain-knowledge extraction system; and a query statement using a natural Chinese language processing system.
6116737
Domain Knowledge Extracting in a Chinese Natural Language Interface to Databases: NChiql
This paper presents the method of domain knowledge extracting in NChiql, a Chinese natural language interface to databases. After describing the overall extracting strategy in NChiql, we mainly discuss the basic semantic information extracting method, called DSE. A semantic conceptual graph is employed to specify two types of modification and three types of verbal relationship among the entities, relationships and attributes. Compared with related works. DSE has more strongly extracting ability.
42912843
NChiql: The Chinese Natural Language Interface to Databases
Numerous natural language interface to databases (NLIDBs) developed in the mid-eighties demonstrated impressive characteristic in certain application areas, but NLIDBs did not gained the expected rapid and wide commercial acceptance. We argue that there are two good reasons explain why: limited portability and poor usability. This paper describes the design and implementation of NChiql, a Chinese natural language interface to databases. In order to bright the essence of these problems, we provide an abstract model (AM) in NChiql. We try to give a solution for these problems based on the model in our system. In this paper, we depict a novel method based on database semantics (SCM) to handle the Chinese natural language query, which greatly promotes the system's usability. The experiments show that NChiql has good usability and high correctness.
2740448
A novel conflict detection and resolution strategy based on TLRSP in replicated mobile database systems
Replication is one of the key technologies in promoting the performance of mobile database systems. In this paper, a novel mobile database replication scheme, the transaction-level result-set propagation (TLRSP) model, is put forward. A conflict detection and resolution strategy based on TLRSP is discussed in detail and its implementation algorithm is proposed. In the TLRSP model, mobile users are allowed to access local replicas of the database and to submit local transactions when the system is disconnected. The locally committed transactions are sent to a fixed database server for conflict reconciliation and result-set incorporation when the system is reconnected. The TLRSP model uses the incremental refreshing method to synchronize database replicas and to maintain the consistency of the replicated mobile database system.
1771247
Using query-specific variance estimates to combine Bayesian classifiers
Many of today's best classification results are obtained by combining the responses of a set of base classifiers to produce an answer for the query. This paper explores a novel "query specific" combination rule: After learning a set of simple belief network classifiers, we produce an answer to each query by combining their individual responses, using weights based inversely on their respective variances around their responses. These variances are based on the uncertainty of the network parameters, which in turn depend on the training datasample. In essence, this variance quantifies the base classifier's confidence of its response to this query. Our experimental results show that these "mixture-using-variance belief net classifiers" MUVS work effectively, especially when the base classifiers are learned using balanced bootstrap samples and when their results are combined using James-Stein shrinkage. We also found that our variance-based combination rule performed better than both bagging and AdaBoost, even on the set of base classifiers produced by AdaBoost itself. Finally, this framework is extremely efficient, as both the learning and the classification components require only straight-line code.
11278363
A Distributed Artificial Network Solving Complex and Multiple Causal Associations
Causal reasoning (known also as abduction) is a hard task that cognitive agents perform reliably and quickly. A particular class of causal reasoning that raises several difficulties is the cancellation class. Cancellation occurs when a set of causes (hypotheses) cancel each other's explanation with respect to a given effect (observation). For example, a cloudy sky may suggest a rainy weather; whereas a shiny sky may suggest the absence of rain. In the current paper, we extend a recent neural model to handle cancellation interactions. We conduct a sensitivity analysis of this proposal on ad hoc problems put at extreme cases. Finally, we test the model on a large database and propose objective criteria to quantitatively evaluate its performance. Simulation results are very satisfactory and should encourage research.
18505600
FCM-Based Model Selection Algorithms for Determining the Number of Clusters
Clustering is an important research topic that has practical applications in many fields. It has been demonstrated that fuzzy clustering, using algorithms such as the fuzzy C-means (FCM), has clear advantages over crisp and probabilistic clustering methods. Like most clustering algorithms, however, FCM and its derivatives need the number of clusters in the given data set as one of their initializing parameters. The main goal of this paper is to develop an effective fuzzy algorithm for automatically determining the number of clusters. After a brief review of the relevant literature, we present a new algorithm for determining the number of clusters in a given data set and a new validity index for measuring the "goodness" of clustering. Experimental results and comparisons are given to illustrate the performance of the new algorithm.
9669585
Conceptual construction on incomplete survey data
The raw survey data for data mining are often incomplete. The issues of missing data in knowledge discovery are often ignored in data mining. This article presents the conceptual foundations of data mining with incomplete survey data, and proposes query processing for knowledge discovery and a set of query functions for the conceptual construction in survey data mining. Through a case, this paper demonstrates that conceptual construction on incomplete data can be accomplished by using artificial intelligence tools such as self-organizing maps.
35913610
Knowledge Discovery Through Self-Organizing Maps: Data Visualization and Query Processing
Abstract. In data mining, the usefulness of a data pattern depends on the user of the database and does not solely depend on the statistical strength of the pattern. Based on the premise that heuristic search in combinatorial spaces built on computer and human cognitive theories is useful for effective knowledge discovery, this study investigates how the use of self-organizing maps as a tool of data visualization in data mining plays a significant role in human–computer interactive knowledge discovery. This article presents the conceptual foundations of the integration of data visualization and query processing for knowledge discovery, and proposes a set of query functions for the validation of self-organizing maps in data mining.