Jiangbo Dang

Concurrent Multiple-Issue Negotiation for Internet-Based Services

Negotiation is a technique for reaching a mutually beneficial agreement among autonomous entities. In an Internet-based services context, multiple entities are negotiating simultaneously. The concurrent negotiation protocol extends existing negotiation protocols, letting both service requestors and service providers manage several negotiation processes in parallel. Colored Petri nets, which have greater expressive power than finite state machines and offer support for concurrency, represent the negotiation protocol and facilitate the analysis of desirable properties

Ontology-based knowledge discovery and sharing in bioinformatics and medical informatics: A brief survey

Worldwide health scientists are producing, accessing, analyzing, integrating, and storing massive amounts of digital medical data daily, through observation, experimentation, and simulation. If we were able to effectively transfer and integrate data from all possible resources, then a deeper understanding of all these data sets and better exposed knowledge, along with appropriate insights and actions, would be granted. Unfortunately, in many cases, the data users are not the data producers, and they thus face challenges in harnessing data in unforeseen and unplanned ways. In order to obtain the ability to integrate heterogeneous data, and thereby efficiently revolutionize the traditional medical and biological research, new methodologies built upon the increasingly pervasive cyberinfrastructure are required to conceptualize traditional medical and biological data, and acquire the “deep” knowledge out of original data thereafter. As formal knowledge representation models, ontologies can render invaluable help in this regard. In this paper, we summarize the state-of-the-art research in ontological techniques and their innovative application in medical and biological areas.

Use artificial neural network to align biological ontologies

BackgroundBeing formal, declarative knowledge representation models, ontologies help to address the problem of imprecise terminologies in biological and biomedical research. However, ontologies constructed under the auspices of the Open Biomedical Ontologies (OBO) group have exhibited a great deal of variety, because different parties can design ontologies according to their own conceptual views of the world. It is therefore becoming critical to align ontologies from different parties. During automated/semi-automated alignment across biological ontologies, different semantic aspects, i.e., concept name, concept properties, and concept relationships, contribute in different degrees to alignment results. Therefore, a vector of weights must be assigned to these semantic aspects. It is not trivial to determine what those weights should be, and current methodologies depend a lot on human heuristics.ResultsIn this paper, we take an artificial neural network approach to learn and adjust these weights, and thereby support a new ontology alignment algorithm, customized for biological ontologies, with the purpose of avoiding some disadvantages in both rule-based and learning-based aligning algorithms. This approach has been evaluated by aligning two real-world biological ontologies, whose features include huge file size, very few instances, concept names in numerical strings, and others.ConclusionThe promising experiment results verify our proposed hypothesis, i.e., three weights for semantic aspects learned from a subset of concepts are representative of all concepts in the same ontology. Therefore, our method represents a large leap forward towards automating biological ontology alignment.

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 analysts 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.

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

Semantics-driven frequent data pattern mining on electronic health records for effective adverse drug event monitoring

Continued surveillance of post-marketing Adverse Drug Events (ADEs) is considered essential for patient safety, and Electronic Health Records (EHRs) serve as a critical source for identifying relevant information. But effective EHR knowledge discovery and data mining is not trivial because involved data usually have significantly different semantics among each other. Semantic technologies are believed to greatly assist in this regard; unfortunately, semantic technologies and conventional data mining remain largely separate disciplines, and the fusion of these two disciplines is still in its infancy. This position paper explores two semantics-driven frequent data pattern mining algorithms for EHR knowledge discovery, aiming at more effective ADE monitoring in a population. By effectively utilizing human knowledge formally encoded in EHR domain ontologies, our proposed algorithms will enhance the identification of the drug ADE causality out of large amounts of heterogeneous data sets. Through mining a large corpus of representative EHRs at semantic level, we will be able to compile a comprehensive list of ADE endpoints by obtaining critical, but originally hidden and implicit, frequent data patterns. Ultimately, our software to be developed will significantly facilitate effective ADE monitoring and prediction. Moreover, our research is expected to produce broader impacts on the pharmaceutical industry by reducing the R & D cost for new drug discovery and on transforming current pharmacovigilance methods to reduce adverse events and hence improve human health.

Workflow coordination for service-oriented multiagent systems

From a multiagent viewpoint, a workflow is a dynamic set of tasks performed by a set of agents to reach a shared goal. We show herein that commitments among agents can be used to model a workflow and coordinate their execution of it. From a service-oriented computing viewpoint, a workflow can be represented as a set of services and a specification for the control and data flows among these services to address some business needs. As a formal declarative knowledge representation model, ontology is used as a basis for agent-based workflow execution and coordination. This paper presents methodologies to map an Ontology Web Language for Services (OWL-S) representation for a workflow to a CPN graph, a graphical and mathematical modeling tool for describing and analyzing information processing systems, and then infer commitments and causal relationships from the CPN graph. We provide an example scenario to describe our algorithms.

Ontology alignment as a basis for mobile service integration and invocation

Purpose – The purpose of this paper is to present ontology alignment as a basis for mobile service integration and invocation.Design/methodology/approach – This paper presents an automated schema‐based approach to align the ontologies from interacting devices as a basis for mobile service invocation. When the ontologies are ambiguous about the services provided, compatibility vectors are introduced as a means of maintaining ontology quality and deciding which service to choose to reduce the ambiguity.Findings – Both precision and recall measurements are applied in the evaluation of the alignment approach, with promising results. In addition, for the compatibility vector system, it is not only proved theoretically that the approach is both precise and efficient, but it also shows promising results experimentally.Originality/value – In cases where sufficient resources are not available and only a certain number of mobile devices can be chosen for interaction, this approach increases the efficiency by choosi...

Knowledge acquisition, semantic text mining, and security risks in health and biomedical informatics

Computational techniques have been adopted in medical and biological systems for a long time. There is no doubt that the development and application of computational methods will render great help in better understanding biomedical and biological functions. Large amounts of datasets have been produced by biomedical and biological experiments and simulations. In order for researchers to gain knowledge from original data, nontrivial transformation is necessary, which is regarded as a critical link in the chain of knowledge acquisition, sharing, and reuse. Challenges that have been encountered include: how to efficiently and effectively represent human knowledge in formal computing models, how to take advantage of semantic text mining techniques rather than traditional syntactic text mining, and how to handle security issues during the knowledge sharing and reuse. This paper summarizes the state-of-the-art in these research directions. We aim to provide readers with an introduction of major computing themes to be applied to the medical and biological research.

Distributed Coordination of an Agent Society Based on Obligations and Commitments to Negotiated Agreements

This chapter discusses coordination from a commitment basis. Typically, commitments are established via a process of negotiation between the parties—the debtor and creditor—involved in the commitment. We define obligations to be those commitments, sometimes termed norms or social commitments, without a clearly identifiable creditor. The establishment of a commitment occurs in response to the adoption of a goal or the acceptance and performance of a task. Using a service-oriented computing (SOC) context, we describe an efficient negotiation process for establishing commitments. We then show how commitments and obligations can be used to monitor and control the aggregate behavior of a group of agents to yield coordinated progress towards the agents’ overall objective.