標(biāo)題: Titlebook: ; [打印本頁(yè)] 作者: 萬(wàn)能 時(shí)間: 2025-3-21 19:26
書目名稱Graph Structures for Knowledge Representation and Reasoning影響因子(影響力)
書目名稱Graph Structures for Knowledge Representation and Reasoning影響因子(影響力)學(xué)科排名
書目名稱Graph Structures for Knowledge Representation and Reasoning網(wǎng)絡(luò)公開(kāi)度
書目名稱Graph Structures for Knowledge Representation and Reasoning網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Graph Structures for Knowledge Representation and Reasoning被引頻次
書目名稱Graph Structures for Knowledge Representation and Reasoning被引頻次學(xué)科排名
書目名稱Graph Structures for Knowledge Representation and Reasoning年度引用
書目名稱Graph Structures for Knowledge Representation and Reasoning年度引用學(xué)科排名
書目名稱Graph Structures for Knowledge Representation and Reasoning讀者反饋
書目名稱Graph Structures for Knowledge Representation and Reasoning讀者反饋學(xué)科排名
作者: irreducible 時(shí)間: 2025-3-21 22:59
Madalina Croitoru,Sebastian Rudolph,Christophe Gon作者: headway 時(shí)間: 2025-3-22 00:32
https://doi.org/10.1007/978-3-662-08763-3ts. So, several representations by graphs have been proposed for the study of constraint hypergraphs to extend the known results to the binary case. Another approach, finer, is interested in the study of the microstructure of CSP, which is defined by graphs. This helped, offering a new theoretical f作者: 意見(jiàn)一致 時(shí)間: 2025-3-22 07:30 作者: Spinal-Fusion 時(shí)間: 2025-3-22 09:41
Different Classes of Graphs to Represent Microstructures for CSPs,ts. So, several representations by graphs have been proposed for the study of constraint hypergraphs to extend the known results to the binary case. Another approach, finer, is interested in the study of the microstructure of CSP, which is defined by graphs. This helped, offering a new theoretical f作者: malign 時(shí)間: 2025-3-22 14:24
Automatic Strengthening of Graph-Structured Knowledge Bases, some subclass of .. In both cases, . is said to refer to ...This paper contains three contributions. First, we describe and formalize the problem from a logical point of view and give a first-order semantics for CGraphs. We show that the identification of inherited content in CGraphs depends on som作者: malign 時(shí)間: 2025-3-22 19:11 作者: lavish 時(shí)間: 2025-3-22 23:11 作者: anaphylaxis 時(shí)間: 2025-3-23 02:01
Different Classes of Graphs to Represent Microstructures for CSPs,ution methods in practice. This formalism has also provided a useful framework for the knowledge representation as well as to implement efficient methods for reasoning about knowledge. The data of a CSP are usually expressed in terms of a constraint network. This network is a (constraints) graph whe作者: 移動(dòng) 時(shí)間: 2025-3-23 07:38 作者: pancreas 時(shí)間: 2025-3-23 10:05 作者: 違反 時(shí)間: 2025-3-23 13:54
Structural Consistency: A New Filtering Approach for Constraint Networks,ncy is based on a new approach. While conventional consistencies generally rely on local properties extended to the entire network, this new partial consistency considers global consistency on subproblems. These subproblems are defined by partial constraint graphs whose tree-width is bounded by a co作者: Default 時(shí)間: 2025-3-23 20:05 作者: 癡呆 時(shí)間: 2025-3-23 23:13 作者: 似少年 時(shí)間: 2025-3-24 02:45
Learning Bayes Nets for Relational Data with Link Uncertainty,rrelations are represented in a Bayes net structure. This provides a succinct graphical way to display relational statistical patterns and support powerful probabilistic inferences. The current state of the art algorithm for learning relational Bayes nets captures only correlations among entity attr作者: META 時(shí)間: 2025-3-24 10:22
Concurrent Reasoning with Inference Graphs,lable. Knowledge representation systems using logical inference have been slow to embrace this new technology. We present the concept of inference graphs, a natural deduction inference system which scales well on multi-core and multi-processor machines. Inference graphs enhance propositional graphs 作者: Kernel 時(shí)間: 2025-3-24 10:53
Formal Concept Analysis over Graphs and Hypergraphs,d attributes (or properties) under consideration. In this paper I propose a generalization of formal concept analysis based on binary relations between hypergraphs, and more generally between pre-orders. A binary relation between any two sets already provides a bipartite graph, and this is a well-kn作者: 引起 時(shí)間: 2025-3-24 18:27 作者: 原來(lái) 時(shí)間: 2025-3-24 22:50
https://doi.org/10.1007/978-3-642-72392-6dge for cheese making. In this extension, we propose to use default rules to represent specific pieces of knowledge. We use the CoGui software to manage conceptual graphs in the application from the CTFC data expressed in Freeplan. A specific end user interface has been designed on top of CoGui to e作者: Champion 時(shí)間: 2025-3-25 02:36
Milestones in Analog and Digital Computingertain way. In such a situation, the persuading argument can be seen to have a positive utility. However, arguments can also have a negative utility — uttering the argument could reveal sensitive information, or prevent the information from being used as a bargaining chip in the future. Previous wor作者: Occupation 時(shí)間: 2025-3-25 06:00
https://doi.org/10.1007/978-3-662-08763-3ution methods in practice. This formalism has also provided a useful framework for the knowledge representation as well as to implement efficient methods for reasoning about knowledge. The data of a CSP are usually expressed in terms of a constraint network. This network is a (constraints) graph whe作者: Morsel 時(shí)間: 2025-3-25 09:23 作者: 話 時(shí)間: 2025-3-25 12:59
https://doi.org/10.1007/978-3-322-80629-1 can be meant as any “improper” use of a system, an attempt to damage parts of it, to gather protected information, to follow “paths” that do not comply with security rules, etc. In this paper we propose an hypergraph-based attack model for intrusion detection. The model allows the specification of 作者: jagged 時(shí)間: 2025-3-25 17:44 作者: 得意人 時(shí)間: 2025-3-25 21:34
https://doi.org/10.1057/9781137282156functional programming community, inductive graphs have been proposed as a purely functional representation of graphs, which makes reasoning and concurrent programming simpler. In this paper, we propose a simplified representation of inductive graphs, called Inductive Triple Graphs, which can be use作者: conscience 時(shí)間: 2025-3-26 04:12
https://doi.org/10.1057/9780230370630 hill climbing approaches. These methods are anytime algorithms as they can be stopped anytime to produce the best solution so far. However, they cannot guarantee the quality of their solution, not even mentioning optimality. In recent years, several exact algorithms have been developed for learning作者: FORGO 時(shí)間: 2025-3-26 05:40
https://doi.org/10.1007/978-3-476-04179-1rrelations are represented in a Bayes net structure. This provides a succinct graphical way to display relational statistical patterns and support powerful probabilistic inferences. The current state of the art algorithm for learning relational Bayes nets captures only correlations among entity attr作者: CAGE 時(shí)間: 2025-3-26 11:32
https://doi.org/10.1007/978-3-319-57565-0lable. Knowledge representation systems using logical inference have been slow to embrace this new technology. We present the concept of inference graphs, a natural deduction inference system which scales well on multi-core and multi-processor machines. Inference graphs enhance propositional graphs 作者: 失眠癥 時(shí)間: 2025-3-26 16:05
https://doi.org/10.1057/9781137274137d attributes (or properties) under consideration. In this paper I propose a generalization of formal concept analysis based on binary relations between hypergraphs, and more generally between pre-orders. A binary relation between any two sets already provides a bipartite graph, and this is a well-kn作者: 安撫 時(shí)間: 2025-3-26 19:30 作者: 隱藏 時(shí)間: 2025-3-27 00:08 作者: forecast 時(shí)間: 2025-3-27 01:14 作者: FEIGN 時(shí)間: 2025-3-27 07:52
Intrusion Detection with Hypergraph-Based Attack Models,various kinds of constraints on possible attacks and provides a high degree of flexibility in representing many different security scenarios. Besides discussing the main features of the model, we study the problems of checking the consistency of attack models and detecting attack instances in sequences of logged activities.作者: EWE 時(shí)間: 2025-3-27 09:51 作者: 休戰(zhàn) 時(shí)間: 2025-3-27 14:28 作者: growth-factor 時(shí)間: 2025-3-27 20:33
https://doi.org/10.1057/9780230370630ons and eventually converges to an optimal Bayesian network upon completion. The algorithm is shown to not only improve the runtime to . optimal network structures up to 100 times compared to some existing methods, but also prove the optimality of these solutions about 10 times faster in some cases.作者: vasospasm 時(shí)間: 2025-3-27 22:55 作者: angina-pectoris 時(shí)間: 2025-3-28 05:02 作者: misshapen 時(shí)間: 2025-3-28 09:59 作者: Orthodontics 時(shí)間: 2025-3-28 13:34 作者: 打包 時(shí)間: 2025-3-28 17:00
Concurrent Reasoning with Inference Graphs, perform very well in forward, backward, bi-directional, and focused reasoning. Tests demonstrate the usefulness of our scheduling heuristics, and show significant speedup in both best case and worst case inference scenarios as the number of processors increases.作者: 錯(cuò)事 時(shí)間: 2025-3-28 22:43 作者: 孤僻 時(shí)間: 2025-3-28 23:48 作者: PET-scan 時(shí)間: 2025-3-29 05:04
https://doi.org/10.1007/978-3-658-18992-1ommendation domain, where search queries include multiple sentences. We draw the comparison for search relevance improvement by pair-wise sentence generalization, phrase-level generalization, and generalizations of PTs as graphs.作者: Bone-Scan 時(shí)間: 2025-3-29 07:26 作者: 土產(chǎn) 時(shí)間: 2025-3-29 14:10 作者: strdulate 時(shí)間: 2025-3-29 17:13
Finding Maximal Common Sub-parse Thickets for Multi-sentence Search,ommendation domain, where search queries include multiple sentences. We draw the comparison for search relevance improvement by pair-wise sentence generalization, phrase-level generalization, and generalizations of PTs as graphs.作者: Orchiectomy 時(shí)間: 2025-3-29 21:41
Learning Bayes Nets for Relational Data with Link Uncertainty,t the link structure. Our base line method learns a Bayes net from join tables directly. This is a statistically powerful procedure that finds many correlations, but does not scale well to larger datasets. We compare join table search with a hierarchical search strategy.
