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.
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.
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.
10317661
Nonrigid Shape Correspondence Using Landmark Sliding, Insertion and Deletion
The growing usage of statistical shape analysis in medical imaging calls for effective methods for highly accurate shape correspondence. This paper presents a novel landmark-based method to correspond a set of 2D shape instances in a nonrigid fashion. Different from prior methods, the proposed method combines three important factors in measuring the shape-correspondence error: landmark-correspondence error, shape-representation error, and shape-representation compactness. In this method, these three important factors are explicitly handled by the landmark sliding, insertion, and deletion operations, respectively. The proposed method is tested on several sets of structural shape instances extracted from medical images. We also conduct an empirical study to compare the developed method to the popular Minimum Description Length method.
10710388
CAPTCHA Challenge Tradeoffs: Familiarity of Strings versus Degradation of Images
It is a well documented fact that, for human readers, familiar text is more legible than unfamiliar text. Current-generation computer vision systems also are able to exploit some kinds of prior knowledge of linguistic context: for example, many OCR systems can use known lexica (word-lists, such as of commonly occurring English words) to disambiguate interpretations. It is interesting that human readers can exploit various degrees of familiarity; for example, strings of characters which, while not found in dictionaries, are similar to spelled words: e.g. "pronounceable" strings, or strings made up of frequently occurring character n-grams. In contrast to this, computer vision technologies for exploiting such poorly characterized constraints (absent an explicit, complete lexicon) are not yet well developed. This gap in ability may allow us to design stronger CAPTCHAs. We measure the familiarity of challenge strings generated by four methods (described by Bentley and Mallows) and we use the ScatterType CAPTCHA to degrade challenge images. We report the results of a human legibility trial which supports the hypothesis that more familiar strings are indeed more legible in CAPTCHAs. Our measurements may enable engineering CAPTCHAs with a more uniform distribution of difficulty by balancing image degradations against familiarity
17967492
Network Game and Boosting
We propose an ensemble learning method called Network Boosting which combines weak learners together based on a random graph (network). A theoretic analysis based on the game theory shows that the algorithm can learn the target hypothesis asymptotically. The comparison results using several datasets of the UCI machine learning repository and synthetic data are promising and show that Network Boosting has much resistance to the noisy data than AdaBoost through the cooperation of classifiers in the classifier network.
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.
22971764
Integrating Semantic Web Services for Declarative Accesses in Natural Language
Although extensive research on Web services has been reported in the last decade, problems still remain due to the lack of natural language query support, the lack of automatic service discovery and composition, and the existence of a large number of existing web sites that are query driven. In this paper we introduce a framework that integrates some novel solutions to the above problems. Specifically we differentiate query-based services (QBS) from the traditional function-based services (FBS) and address the following issues: natural language user interface, automatic service discovery and composition, and semantic UDDI