Cungen Cao

Knowledge modeling and acquisition of traditional Chinese herbal drugs and formulae from text

Traditional Chinese medicine has developed over more than 4000 years. A tremendous amount of medical knowledge has been accumulated, among which herbal drugs and formulae are an important portion. This paper presents an ontology for traditional Chinese drugs and formulae, and an ontology-based system for extracting knowledge of drugs and formulae from semi-structured text. The system consists of two components: an executable knowledge extraction language (or EKEL) for specifying knowledge-extracting agents, and a support machine for executing EKEL programs. Experiments show that the system is adequate of extracting knowledge of herbal drugs and formulae from semi-structured text.

An information entropy-based approach to outlier detection in rough sets

The information entropy in information theory, developed by Shannon, gives an effective measure of uncertainty for a given system. And it also seems a competing mechanism for the measurement of uncertainty in rough sets. Many researchers have applied the information entropy to rough sets, and proposed different information entropy models in rough sets. Especially, Duntsch et al. presented a well-justified information entropy model for the measurement of uncertainty in rough sets. In this paper, we shall demonstrate the application of this model for the study of a specific data mining problem - outlier detection. By virtue of Duntschs information entropy model, we propose a novel definition of outliers -IE (information entropy)-based outliers in rough sets. An algorithm to find such outliers is also given. And the effectiveness of IE-based method for outlier detection is demonstrated on two publicly available data sets.

A rough set approach to outlier detection

“One persons noise is another persons signal” (Knorr and Ng 1998). In recent years, much attention has been given to the problem of outlier detection, whose aim is to detect outliers—objects who behave in an unexpected way or have abnormal properties. Detecting such outliers is important for many applications such as criminal activities in electronic commerce, computer intrusion attacks, terrorist threats, agricultural pest infestations. In this paper, we suggest to exploit the framework of rough sets for detecting outliers. We propose a novel definition of outliers—RMF (rough membership function)-based outliers, by virtue of the notion of rough membership function in rough set theory. An algorithm to find such outliers is also given. And the effectiveness of RMF-based method is demonstrated on two publicly available data sets.

Outlier detection using rough set theory

In this paper, we suggest to exploit the framework of rough set for detecting outliers — individuals who behave in an unexpected way or feature abnormal properties. The ability to locate outliers can help to maintain knowledge base integrity and to single out irregular individuals. First, we formally define the notions of exceptional set and minimal exceptional set. We then analyze some special cases of exceptional set and minimal exceptional set. Finally, we introduce a new definition for outliers as well as the definition of exceptional degree. Through calculating the exceptional degree for each object in minimal exceptional sets, we can find out all outliers in a given dataset.

An incremental decision tree algorithm based on rough sets and its application in intrusion detection

As we know, learning in real world is interactive, incremental and dynamical in multiple dimensions, where new data could be appeared at anytime from anywhere and of any type. Therefore, incremental learning is of more and more importance in real world data mining scenarios. Decision trees, due to their characteristics, have been widely used for incremental learning. In this paper, we propose a novel incremental decision tree algorithm based on rough set theory. To improve the computation efficiency of our algorithm, when a new instance arrives, according to the given decision tree adaptation strategies, the algorithm will only modify some existing leaf node in the currently active decision tree or add a new leaf node to the tree, which can avoid the high time complexity of the traditional incremental methods for rebuilding decision trees too many times. Moreover, the rough set based attribute reduction method is used to filter out the redundant attributes from the original set of attributes. And we adopt the two basic notions of rough sets: significance of attributes and dependency of attributes, as the heuristic information for the selection of splitting attributes. Finally, we apply the proposed algorithm to intrusion detection. The experimental results demonstrate that our algorithm can provide competitive solutions to incremental learning.

Medical Knowledge Acquisition from the Electronic Encyclopedia of China

The Encyclopedia of China contains considerably complete medical knowledge in unrestricted text. We have been developing a new method for extracting medical knowledge from the Electronic Encyclopedia of China. The method consists of two major parts: a high-level conceptual description language for use by knowledge engineers to formalize the text and a knowledge compiler for compiling the formalized text to a conceptual model.

A formal fuzzy reasoning system and reasoning mechanism based on propositional modal logic

We establish in this paper a fuzzy propositional modal logic, FPML, and the associated semantics, fuzzy Kripke semantics. We prove that FPML is sound and complete. Furthermore, we set up a formalized reasoning mechanism based on FPML.

A Survey of Commonsense Knowledge Acquisition

Collecting massive commonsense knowledge (CSK) for commonsense reasoning has been a long time standing challenge within artificial intelligence research. Numerous methods and systems for acquiring CSK have been developed to overcome the knowledge acquisition bottleneck. Although some specific commonsense reasoning tasks have been presented to allow researchers to measure and compare the performance of their CSK systems, we compare them at a higher level from the following aspects: CSK acquisition task (what CSK is acquired from where), technique used (how can CSK be acquired), and CSK evaluation methods (how to evaluate the acquired CSK). In this survey, we first present a categorization of CSK acquisition systems and the great challenges in the field. Then, we review and compare the CSK acquisition systems in detail. Finally, we conclude the current progress in this field and explore some promising future research issues.

Learning concepts from text based on the inner-constructive model

This paper presents a new model for automatic acquisition of lexical concepts from text, referred to as Concept Inner-Constructive Model (CICM). The CICM clarifies the rules when words construct concepts through four aspects including (1) parts of speech, (2) syllable, (3) senses and (4) attributes. Firstly, we extract a large number of candidate concepts using lexico-patterns and confirm a part of them to be concepts if they matched enough patterns for some times. Then we learn CICMs using the confirmed concepts automatically and distinguish more concepts with the model. Essentially, the CICM is an instances learning model but it differs from most existing models in that it takes into account a variety of linguistic features and statistical features of words as well. And for more effective analogy when learning new concepts using CICMs, we cluster similar words based on density. The effectiveness of our method has been evaluated on a 160G raw corpus and 5,344,982 concepts are extracted with a precision of 89.11% and a recall of 84.23%.

A Domain-Specific Formal Ontology for Archaeological Knowledge Sharing and Reusing

Inherent heterogeneity and distribution of knowledge strongly prevent knowledge from sharing and reusing among different agents and across different domains; formal ontologies have been viewed as a promising means to tackle this problem. In this paper, we present a domain-specific formal ontology for archaeological knowledge sharing and reusing. The ontology consists of three major parts: archaeological categories, their relationships and axioms. The ontology not only captures the semantics of archaeological knowledge, but also provides archaeology with an explicit and formal specification of a shared conceptualization, thus making archaeological knowledge shareable and reusable across humans and machines in a structured fashion. As an application of the ontology, we have developed an ontology-driven approach to knowledge acquisition from archaeological text.