5842841
Incremental mining of information interest for personalized web scanning
Businesses and people often organize their information of interest (IOI) into a hierarchy of folders (or categories). The personalized folder hierarchy provides a natural way for each of the users to manage and utilize his/her IOI (a folder corresponds to an interest type). Since the interest is relatively long-term, continuous web scanning is essential. It should be directed by precise and comprehensible specifications of the interest. A precise specification may direct the scanner to those spaces that deserve scanning, while a specification comprehensible to the user may facilitate manual refinement, and a specification comprehensible to information providers (e.g. Internet search engines) may facilitate the identification of proper seed sites to start scanning. However, expressing such specifications is quite difficult (and even implausible) for the user, since each interest type is often implicitly and collectively defined by the content (i.e. documents) of the corresponding folder, which may even evolve over time. In this paper, we present an incremental text mining technique to efficiently identify the user's current interest by mining the user's information folders. The specification mined for each interest type specifies the context of the interest type in conjunctive normal form, which is comprehensible to general users and information providers. The specification is also shown to be more precise in directing the scanner to those sites that are more likely to provide IOI. The user may thus maintain his/her folders and then constantly get IOI, without paying much attention to the difficult tasks of interest specification and seed identification.
28084197
Dynamic category profiling for text filtering and classification
Information is often represented in text form and classified into categories. Unfortunately, automatic classifiers often conduct misclassifications. One of the reasons is that the documents for training the classifiers are mainly from the categories, leading the classifiers to derive category profiles for distinguishing each category from others, rather than measuring the extent to which a document's content overlaps that of a category. To tackle the problem, we present a technique DP4FC that selects suitable features to construct category profiles to distinguish relevant documents from irrelevant documents. More specially, DP4FC is associated with various classifiers. Upon receiving a document, it helps the classifiers to create dynamic category profiles with respect to the document, and accordingly make proper decisions in filtering and classification. Theoretical analysis and empirical results show that DP4FC may significantly promote different classifiers' performances under various environments.
9652055
Incremental context mining for adaptive document classification
Automatic document classification (DC) is essential for the management of information and knowledge. This paper explores two practical issues in DC: (1) each document has its context of discussion, and (2) both the content and vocabulary of the document database is intrinsically evolving. The issues call for adaptive document classification (ADC) that adapts a DC system to the evolving contextual requirement of each document category, so that input documents may be classified based on their contexts of discussion. We present an incremental context mining technique to tackle the challenges of ADC. Theoretical analyses and empirical results show that, given a text hierarchy, the mining technique is efficient in incrementally maintaining the evolving contextual requirement of each category. Based on the contextual requirements mined by the system, higher-precision DC may be achieved with better efficiency.
8117936
Adaptive Agents for Effective Information Monitoring
Information motoring is an essential basis for management and decision making in various domains. Once an information update is detected, suitable procedures may be triggered to identify potential problems and opportunities. In this paper, we define effective information monitoring (EIM) and explore how adaptive agents may cooperate with each other in order to achieve their collective goal of EIM. Since each information item may be updated by various entities (e.g. information servers) at any time, EIM calls for a multiagent system that may detect more information updates in a timely manner using a controlled amount of system resources (e.g. loading of related information servers and the Intranet). To achieve EIM, the agents should adapt themselves to the dynamically changing behaviors of the information items being monitored. They learn to issue requests of using system resources and concede to those agents that are more likely to detect updates at the time of negotiation. The framework is theoretically ...
44250435
An Adaptive Agent Society for Environmental Scanning through the Internet
Business managers need to promptly respond to environmental changes. Environmental scanners are thus important in discovering and monitoring the information of interest (IOI). In this paper, we explore continuous and resource-bounded environmental scanning (CRBES). The scanner continuously scans for new IOI without consuming too much resource (e.g. bandwidths of computer networks and services of information servers). In that case, new IOI may be discovered in a complete and timely manner without making the related networks and servers too exhausted to provide services. We develop a multiagent framework ACES to achieve CRBES. The agents form an adaptive society by adapting their population and specialty to information needs of individual users, resource limitation of environmental scanning, distribution of IOI in the environments, and update behaviors of the IOI. The delivery of ACES to businesses may constantly provide timelier IOI without causing serious problems to the Intranet and the Internet communities.
43626831
Parsing-Driven Generalization for Natural Language Acquisition
Parsing is an important step in natural language processing. It involves tasks of searching for applicable grammatical rules which can transform natural language sentences into their corresponding parse trees. Therefore parsing can be viewed as problem solving, and language acquisition can be achieved by generalizing problem solving heuristics. In this paper we investigate how machine learning methodologies can be integrated with a Wait-And-See Parser (the problem solver) to acquire parsing-related knowledge that is needed for the parser. We call this approach parsing-driven generalization since learning (acquisition of parsing rules and classification of lexicons) is basically derived from the parsing process. Three types of generalization are reported in this paper: simple generalization, generalization by asking questions, and generalization back-propagation. Simple generalization generalizes any two parsing rules whose action parts (right-hand sides) are the same but whose condition parts (left-hand sides) have a single difference. Generalization by asking questions is triggered when a “climbing-up” move on a concept hierarchy is attempted. It is necessary for avoiding over-generalization. Generalization back-propagation propagates a confirmed generalization of some later parsing rule back to its precedent rules in a parsing sequence and thus causes them to be generalized as well. It can reduce the number of questions asked by the system. With these three types of generalization and a mechanism for maintaining lexicon classification (the domain concept hierarchy), parsing and learning can interact to utilize and acquire parsing-related knowledge. To promote the practical performance of parsing after learning, a relaxation parsing mechanism is also designed to process unseen sentences.
23067524
A new method for proving chromatic uniqueness of graphs
Abstract We give a brief survey on a new method for proving the chromatic uniqueness of graphs by their adjoint polynomials. We obtain some simpler proofs of relevant theorems and a new family of chromatically unique graphs.
7031488
Algorithmic aspects of topology control problems for ad hoc networks
Topology control problems are concerned with the assignment of power values to the nodes of an ad hoc network so that the power assignment leads to a graph topology satisfying some specified properties. This paper considers such problems under several optimization objectives, including minimizing the maximum power and minimizing the total power. A general approach leading to a polynomial algorithm is presented for minimizing maximum power for a class of graph properties called textbf monotone properties. The difficulty of generalizing the approach to properties that are not monotone is discussed. Problems involving the minimization of total power are known to be bf NP -complete even for simple graph properties. A general approach that leads to an approximation algorithm for minimizing the total power for some monotone properties is presented. Using this approach, a new approximation algorithm for the problem of minimizing the total power for obtaining a 2-node-connected graph is obtained. It is shown that this algorithm provides a constant performance guarantee. Experimental results from an implementation of the approximation algorithm are also presented.
10425168
Adjoint polynomials and chromatically unique graphs
Abstract The aim of this expository paper is to give a survey of the results on chromatically unique graphs obtained by studying their adjoint polynomials.
884902
CLTC: a cluster-based topology control for ad hoc networks
The topology of an ad hoc network has a significant impact on its performance in that a dense topology may induce high interference and low capacity, while a sparse topology is vulnerable to link failure and network partitioning. Topology control aims to maintain a topology that optimizes network performance while minimizing energy consumption. Existing topology control algorithms utilize either a purely centralized or a purely distributed approach. A centralized approach, although able to achieve strong connectivity (k-connectivity for k /spl ges/ 2), suffers from scalability problems. In contrast, a distributed approach, although scalable, lacks strong connectivity guarantees. We propose a hybrid topology control framework, cluster-based topology control (CLTC) that achieves both scalability and strong connectivity. By varying the algorithms utilized in each of the three phases of the framework, a variety of optimization objectives and topological properties can be achieved. In this paper, we present the CLTC framework; describe topology control algorithms based on CLTC and prove that k-connectivity is achieved using those algorithms; analyze the message complexity of an implementation of CLTC, namely, CLTC-A, and present simulation studies that evaluate the effectiveness of CLTC-A for a range of networks.
40972997
Algorithmic Aspects of Topology Control Problems for Ad Hoc Networks
Topology control problems are concerned with the assignment of power values to the nodes of an ad hoc network so that the power assignment leads to a graph topology satisfying some specified properties. This paper considers such problems under several optimization objectives, including minimizing the maximum power and minimizing the total power. A general approach leading to a polynomial algorithm is presented for minimizing maximum power for a class of graph properties called monotone properties. The difficulty of generalizing the approach to properties that are not monotone is discussed. Problems involving the minimization of total power are known to be NP-complete even for simple graph properties. A general approach that leads to an approximation algorithm for minimizing the total power for some monotone properties is presented. Using this approach, a new approximation algorithm for the problem of minimizing the total power for obtaining a 2-node-connected graph is developed. It is shown that this algorithm provides a constant performance guarantee. Experimental results from an implementation of the approximation algorithm are also presented.