scholarly journals A Multi-Agent Architecture for Distributed Domain-Specific Information Integration

2006 ◽  
pp. 129-148 ◽  
Author(s):  
Shahram Rahimi ◽  
Norm Carver ◽  
Frederick Petry
Keyword(s):  
Information Integration ◽  
Specific Information ◽  
Agent Architecture ◽  
Domain Specific ◽  
Multi Agent

Knowledge Enhanced LSTM for Coreference Resolution on Biomedical Texts

2021 ◽  
Author(s):  
Yufei Li ◽  
Xiaoyong Ma ◽  
Xiangyu Zhou ◽  
Pengzhen Cheng ◽  
Kai He ◽  
...  
Keyword(s):  
Information Integration ◽  
Short Term Memory ◽  
Superior Performance ◽  
Supplementary Information ◽  
Specific Information ◽  
Coreference Resolution ◽  
Fine Grained ◽  
Domain Specific ◽  
Memory Network ◽  
Biomedical Texts

Abstract Motivation Bio-entity Coreference Resolution focuses on identifying the coreferential links in biomedical texts, which is crucial to complete bio-events’ attributes and interconnect events into bio-networks. Previously, as one of the most powerful tools, deep neural network-based general domain systems are applied to the biomedical domain with domain-specific information integration. However, such methods may raise much noise due to its insufficiency of combining context and complex domain-specific information. Results In this paper, we explore how to leverage the external knowledge base in a fine-grained way to better resolve coreference by introducing a knowledge-enhanced Long Short Term Memory network (LSTM), which is more flexible to encode the knowledge information inside the LSTM. Moreover, we further propose a knowledge attention module to extract informative knowledge effectively based on contexts. The experimental results on the BioNLP and CRAFT datasets achieve state-of-the-art performance, with a gain of 7.5 F1 on BioNLP and 10.6 F1 on CRAFT. Additional experiments also demonstrate superior performance on the cross-sentence coreferences. Supplementary information Supplementary data are available at Bioinformatics online.

Multi-Agent Architecture for Knowledge-Driven Decision Support

2010 ◽  
pp. 28-46
Author(s):  
Rahul Singh
Keyword(s):  
Decision Support ◽  
Intelligent Agents ◽  
Agent Architecture ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Knowledge Based ◽  
Intelligent Software Agents ◽  
Intelligent Decision ◽  
Domain Specific Knowledge ◽  
Multi Agent

Organizations use knowledge-driven systems to deliver problem-specific knowledge over Internet-based distributed platforms to decision-makers. Increasingly, artificial intelligence (AI) techniques for knowledge representation are being used to deliver knowledge-driven decision support in multiple forms. In this chapter, we present an Architecture for knowledge-based decision support, delivered through a Multi-Agent Architecture. We illustrate how to represent and exchange domain-specific knowledge in XML-format through intelligent agents to create exchange and use knowledge to provide intelligent decision support. We show the integration of knowledge discovery techniques to create knowledge from organizational data; and knowledge repositories (KR) to store, manage and use data by intelligent software agents for effective knowledge-driven decision support. Implementation details of the architecture, its business implications and directions for further research are discussed.

scholarly journals A cyber-anima-based model of material conscious information network

2017 ◽  
Vol 1 (1) ◽  
pp. 9-25 ◽  
Author(s):  
Jianping Shen ◽  
Yadong Huang ◽  
Yueting Chai
Keyword(s):  
Information Integration ◽  
Collective Intelligence ◽  
Chain Structure ◽  
Information Network ◽  
Agent Architecture ◽  
Content Type ◽  
Networked System ◽  
Self Organized ◽  
The Future ◽  
Multi Agent

Purpose This paper aims to study the node modeling, multi-agent architecture and addressing method for the material conscious information network (MCIN), which is a large-scaled, open-styled, self-organized and ecological intelligent network of supply–demand relationships. Design/methodology/approach This study models the MCIN by node model definition, multi-agent architecture design and addressing method presentation. Findings The prototype of novel E-commerce platform based on the MCIN shows the effectiveness and soundness of the MCIN modeling. By comparing to current internet, the authors also find that the MCIN has the advantages of socialization, information integration, collective intelligence, traceability, high robustness, unification of producing and consuming, high scalability and decentralization. Research limitations/implications Leveraging the dimensions of structure, character, knowledge and experience, a modeling approach of the basic information can fit all kinds of the MCIN nodes. With the double chain structure for both basic and supply–demand information, the MCIN nodes can be modeled comprehensively. The anima-desire-intention-based multi-agent architecture makes the federated agents of the MCIN nodes self-organized and intelligent. The MCIN nodes can be efficiently addressed by the supply–demand-oriented method. However, the implementation of the MCIN is still in process. Practical implications This paper lays the theoretical foundation for the future networked system of supply–demand relationship and the novel E-commerce platform. Originality/value The authors believe that the MCIN, first proposed in this paper, is a transformational innovation which facilitates the infrastructure of the future networked system of supply–demand relationship.

scholarly journals Editorial – Special Issue on Collaboration Support Systems (CSS)

2010 ◽  
Vol 5 (1) ◽  
pp. 6 ◽  
Author(s):  
H.S. Ko ◽  
S. Y. Nof
Keyword(s):  
Information Integration ◽  
Domain Knowledge ◽  
Information Science ◽  
Multi Agent Systems ◽  
Solution Quality ◽  
Systems Of Systems ◽  
Effective Interactions ◽  
Domain Specific ◽  
Editorial Special Issue ◽  
Multi Agent

<p>Recent and emerging advances in computer and information science and technology have realized a powerful computing and communication environment. It enables effective interactions and collaboration among groups of people and systems (and systems-of-systems) beyond traditional restrictions of time and space. The evolution in hardware (e.g., pervasive computing devices, wireless sensor networks, nano-electronics) and software (e.g., multi-agent systems, workflow and information integration, interaction models and protocols) technology, and their flexible teaming have further enabled diverse forms of collaboration approaches. It has been observed during the last few decades that numerous collaboration methodologies, tools and applications in various domains have emerged to provide better quality services, helping to solve domain-specific, highly complex problems. The development of collaboration tools and methodologies has increased the domain knowledge that can be discovered and shared by individuals, and the level and intensity of interactions and collaboration that can dramatically decrease problem complexity and increase solution quality. At the same time, inefficient interactions, task and information overloads, and ineffective collaboration are prevalent.</p>

scholarly journals Context-Aware Feature Attention Model for Coreference Resolution on Biomedical Texts

2021 ◽  
Author(s):  
Yufei Li ◽  
Xiangyu Zhou ◽  
Jie Ma ◽  
Xiaoyong Ma ◽  
Chen Li
Keyword(s):  
Information Integration ◽  
Superior Performance ◽  
Specific Information ◽  
Coreference Resolution ◽  
Context Aware ◽  
Attention Model ◽  
Fine Grained ◽  
Domain Specific ◽  
Feature Attention ◽  
Biomedical Texts

Abstract Background: Bio-entity Coreference resolution is an important task to gain a complete understanding of biomedical texts automatically. Previous neural network-based studies on this topic are domain system based methods which rely on some domain-specific information integration. However, for the identical mentions, this may lead to misleading information, as the model tends to get similar or even identical representations, which further leads to wrongful predictions. Results: we propose a new context-aware Feature Attention model to distinguish identical mentions effectively to better resolve coreference. The new model can represent identical mentions based on different contexts by adaptively exploiting features effectively. The proposed model substantially outperforms the state-of-the-art baselines on the BioNLP dataset with a 64.0% F1 score and further demonstrates superior performance on the differential representation and coreferential link of identical mentions. Conclusion: The context-aware Feature Attention model adaptively exploit features and represent identical mentions according to different contexts, which significantly makes the system obtain semantic information effectively and make more accurate predictions. Considering that this approach is still limited when context information is insufficient, we expect to utilize such information more fine-grained with the help of the external knowledge base in coreference resolution.

Multi-Agent Architecture for Knowledge-Driven Decision Support

2010 ◽  
pp. 433-451
Author(s):  
Rahul Singh
Keyword(s):  
Decision Support ◽  
Intelligent Agents ◽  
Agent Architecture ◽  
Specific Knowledge ◽  
Domain Specific ◽  
Knowledge Based ◽  
Intelligent Software Agents ◽  
Intelligent Decision ◽  
Domain Specific Knowledge ◽  
Multi Agent

Organizations use knowledge-driven systems to deliver problem-specific knowledge over Internet-based distributed platforms to decision-makers. Increasingly, artificial intelligence (AI) techniques for knowledge representation are being used to deliver knowledge-driven decision support in multiple forms. In this chapter, we present an Architecture for knowledge-based decision support, delivered through a Multi-Agent Architecture. We illustrate how to represent and exchange domain-specific knowledge in XML-format through intelligent agents to create exchange and use knowledge to provide intelligent decision support. We show the integration of knowledge discovery techniques to create knowledge from organizational data; and knowledge repositories (KR) to store, manage and use data by intelligent software agents for effective knowledge-driven decision support. Implementation details of the architecture, its business implications and directions for further research are discussed.

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