3242205
OmniSeer: A Cognitive Framework for User Modeling, Reuse of Prior and Tacit Knowledge, and Collaborative Knowledge Services
This paper describes the current state of the OmniSeer system. OmniSeer supports intelligence analysts in the handling of massive amounts of data, the construction of scenarios, and the management of hypotheses. OmniSeer models analysts with dynamic user models that capture an analyst's context, interests, and preferences, thus enabling more efficient and effective information retrieval. OmniSeer explicitly represents the prior and tacit knowledge of analysts, thus enabling transfer and reuse of such knowledge. Both the user and cognitive models employ a Bayesian network fragment representation, which supports principled probabilistic reasoning and analysis. An independent evaluation of OmniSeer was carried out at NIST and will be used to guide further development.
14986573
Sharing ontology schema information for Web service integration
In spite of many standards efforts, Web services with similar or compatible functionalities often have heterogeneous semantics. One reason is the disparate ontologies used for service descriptions. In order to compare and compose Web services, the ability to merge different ontologies is essential. This paper describes an approach to align numerous, independently designed ontologies. Our approach is a completely automated one, without the need for prior agreement on semantics. It incorporates WordNet and heuristic reasoning and infers new knowledge by self-learning. Our system provides a solid base for the seamless integration of Web services.
12118210
Reconciling Agent Ontologies for Web Service Applications
Because there is still no agreed-upon global ontology, Web services supplied by different providers typically have individual and unique semantics, described by independently developed ontologies. The seamless connection of these distributed Web services for business-to-business applications depends heavily on reconciling the disparate semantics, possibly by integrating the ontologies. In this paper, we describe an approach to reconcile ontologies from distributed Web services. Our approach is totally automated, and features the following: i) alignment of the ontologies is performed without previous agreement on the semantics of the terminology in each ontology; ii) both linguistic and contextual features are considered; iii) the use of WordNet for linguistic analysis; iv) integration of heuristic knowledge for contextual analysis; and v) inference of new relationships by applying several rules based on domain-independent relationships and property lists. Experiments have been carried out to show the promising results of our system.
16266978
Ontology Reconciliation for Service-Oriented Computing
Service-oriented computing (SOC) is viewed as the computing paradigm of the near future, allowing for the dynamic interaction of services provided by distributed business partners. Being a declarative knowledge representation model, ontologies serve as a foundation for SOC. Due to the heterogeneous nature of independently designed ontologies, it is problematic for partners to understand the concepts adopted in ontologies from other sources. In order for partners to achieve seamless collaboration of services, they need to reconcile their ontologies with each other. During the alignment process and the following service interactions, compatibility is an important measurement that has been neglected in most research work. We extend a vector system to encode ontology compatibility. In addition, we present a new model - probabilistic center ontology - for better recording and maintenance of ontology alignment results. Our precise and efficient approach is verified by both theoretic proofs and experimental results
3032576
Generative models for name disambiguation
Name ambiguity is a special case of identity uncertainty where one person can be referenced by multiple name variations in different situations or evenshare the same name with other people. In this paper, we present an efficient framework by using two novel topic-based models, extended from Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA). Our models explicitly introduce a new variable for persons and learn the distribution of topics with regard to persons and words. Experiments indicate that our approach consistently outperforms other unsupervised methods including spectral and DBSCAN clustering. Scalability is addressed by disambiguating authors in over 750,000 papers from the entire CiteSeer dataset.
806919
Using Learning to Facilitate the Evolution of Features for Recognizing Visual Concepts
This paper describes a hybrid methodology that integrates genetic algorithms (GAs) and decision tree learning in order to evolve useful subsets of discriminatory features for recognizing complex visual concepts. A GA is used to search the space of all possible subsets of a large set of candidate discrimination features. Candidate feature subsets are evaluated by using C4.5, a decision tree learning algorithm, to produce a decision tree based on the given features using a limited amount of training data. The classification performance of the resulting decision tree on unseen testing data is used as the fitness of the underlying feature subset. Experimental results are presented to show how increasing the amount of learning significantly improves feature set evolution for difficult visual recognition problems involving satellite and facial image data. In addition, we also report on the extent to which other more subtle aspects of the Baldwin effect are exhibited by the system.
1720329
Toward Inductive Logic Programming for Collaborative Problem Solving
In this paper, we tackle learning in distributed systems and the fact that learning does not necessarily involve the participation of agents directly in the inductive process itself. Instead, many systems frequently employ multiple instances of induction separately. The paper's main contribution is a new approach that tightly integrates processes of induction between distributed agents, based on inductive logic programming techniques, for a wider class of problem solving tasks. The approach combines inverse entailment with an epistemic approach to reasoning about knowledge, facilitating a systematic approach to the sharing of knowledge and invention of predicates only when required. We illustrate the approach for learning declarative program fragments and for a well-known path planning problem and compare results empirically to (multiple instances of) single agent-based induction over varying distributions of data. Given a chosen path planning algorithm, our algorithm enables agents to combine their local knowledge in an effective way to avoid central control while significantly reducing communication costs.
10414560
Distributed Interactive Learning in Multi-Agent Systems
Both explanation-based and inductive learning techniques have proven successful in a variety of distributed domains. However, learning in multi-agent systems does not necessarily involve the participation of other agents directly in the inductive process itself. Instead, many systems frequently employ multiple instances of induction separately, or single-agent learning. In this paper we present a new framework, named the Multi-Agent Inductive Learning System (MAILS), that tightly integrates processes of induction between agents. The MAILS framework combines inverse entailment with an epistemic approach to reasoning about knowledge in a multi-agent setting, facilitating a systematic approach to the sharing of knowledge and invention of predicates when required. The benefits of the new approach are demonstrated for inducing declarative program fragments in a multi-agent distributed programming system.
669798
On the effect of group mobility to data replication in ad hoc networks
The growth in wireless communication technologies attracts a considerable amount of attention in mobile ad hoc networks. Since mobile hosts in an ad hoc network usually move freely, the topology of the network changes dynamically and disconnection occurs frequently. These characteristics make it likely for a mobile ad hoc network to be separated into several disconnected partitions, and the data accessibility is hence reduced. Several schemes are proposed to alleviate the reduction of data accessibility by replicating data items. However, little research effort was elaborated upon exploiting the group mobility where the group mobility refers to the phenomenon that several mobile nodes tend to move together. In this paper, we address the problem of replica allocation in a mobile ad hoc network by exploring group mobility. We first analyze the group mobility model and derive several theoretical results. In light of these results, we propose a replica allocation scheme to improve the data accessibility. Several experiments are conducted to evaluate the performance of the proposed scheme. The experimental results show that the proposed scheme is able to not only obtain higher data accessibility, but also produce lower network traffic than prior schemes.