書目名稱Graph-Based Representation and Reasoning影響因子(影響力)學(xué)科排名
書目名稱Graph-Based Representation and Reasoning網(wǎng)絡(luò)公開度
書目名稱Graph-Based Representation and Reasoning網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Graph-Based Representation and Reasoning被引頻次
書目名稱Graph-Based Representation and Reasoning被引頻次學(xué)科排名
書目名稱Graph-Based Representation and Reasoning年度引用
書目名稱Graph-Based Representation and Reasoning年度引用學(xué)科排名
書目名稱Graph-Based Representation and Reasoning讀者反饋
書目名稱Graph-Based Representation and Reasoning讀者反饋學(xué)科排名
作者: 期滿 時(shí)間: 2025-3-21 22:34 作者: Obloquy 時(shí)間: 2025-3-22 00:55
Graph-Based Representation and Reasoning978-3-030-23182-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 切割 時(shí)間: 2025-3-22 06:15 作者: 難理解 時(shí)間: 2025-3-22 09:39
Graph-Based Variability Modelling: Towards a Classification of Existing Formalismsed options. A customised product is then derived by combining the artefacts implementing the backbone with the ones implementing the chosen options. Variability analysis and representation is a central task of this paradigm: it consists in suitably defining and structuring the scope, the commonaliti作者: 有助于 時(shí)間: 2025-3-22 13:48 作者: 有助于 時(shí)間: 2025-3-22 18:11
Formal Context Generation Using Dirichlet Distributionsrithms. We suggest an improved way to randomly generate formal contexts based on Dirichlet distributions. For this purpose we investigate the predominant method, coin-tossing, recapitulate some of its shortcomings and examine its stochastic model. Building upon this we propose our Dirichlet model an作者: 原諒 時(shí)間: 2025-3-22 23:57 作者: 廢墟 時(shí)間: 2025-3-23 03:37
Temporal Relations Between Imprecise Time Intervals: Representation and Reasoningcise time intervals which are classical time intervals characterized by gradual beginnings and/or endings. It is mainly based on extending the Allen’s interval algebra. It is not only suitable to express precise temporal interval relations (e.g., “Before”) but also imprecise personalized ones (e.g.,作者: GENRE 時(shí)間: 2025-3-23 08:08
Relevant Attributes in Formal Contextsdeal with this, e.g., random sampling, parallelization, or attribute extraction. A so far not investigated method in the realm of formal concept analysis is attribute selection, as done in machine learning. Building up on this we introduce a method for attribute selection in formal contexts. To this作者: 逗它小傻瓜 時(shí)間: 2025-3-23 10:35 作者: indifferent 時(shí)間: 2025-3-23 17:29 作者: 粗語(yǔ) 時(shí)間: 2025-3-23 19:37
Enhancing Layered Enterprise Architecture Development Through Conceptual Structuresnces EA by using meta-models made up of layered meta-objects, interconnected by semantic relations. Organisations can use these meta-models to benefit from a novel, ontology-based, object-oriented way of EA thinking and working. Furthermore, the meta-models are directed graphs that can be read linea作者: hermitage 時(shí)間: 2025-3-23 23:22
Ontology-Informed Lattice Reduction Using the Discrimination Power Indexsis (FCA). FCA creates a lattice comprising partial order relationships between sets of object instances in a domain (extent) and their properties (intent). This is mapped onto a semantic knowledge structure comprising domain concepts with their instances and properties. However, this automatic extr作者: 草本植物 時(shí)間: 2025-3-24 03:49
Redescription Mining for Learning Definitions and Disjointness Axioms in Linked Open Dataoms between classes of individuals in the web of data. RM is aimed at mining alternate descriptions from two datasets related to the same set of individuals. We reuse this process for providing definitions in terms of necessary and sufficient conditions to categories in DBpedia. Firstly, we recall t作者: 旋轉(zhuǎn)一周 時(shí)間: 2025-3-24 10:21 作者: Fibroid 時(shí)間: 2025-3-24 13:45 作者: kindred 時(shí)間: 2025-3-24 16:38 作者: 使人煩燥 時(shí)間: 2025-3-24 22:10
https://doi.org/10.1007/978-1-4615-2718-3textual two-dimensional web ontology language. Using the first dimension, we can define contexts-dependent classes, properties, and axioms and using the second dimension, we can express knowledge about contexts which we consider formal objects, as proposed by McCarthy [.]. Moreover, we describe a co作者: 監(jiān)禁 時(shí)間: 2025-3-25 00:27 作者: 1分開 時(shí)間: 2025-3-25 07:14
https://doi.org/10.1007/b118340to represent that fuzzy, vague, ambiguous and uncertain information. Current standards of the Semantic Web and Linked Data do not support such a representation in a formal way and independently of any theory. We present a new vocabulary and a framework to capture and handle uncertainty in the Semant作者: Custodian 時(shí)間: 2025-3-25 09:51 作者: 推崇 時(shí)間: 2025-3-25 14:16
https://doi.org/10.1007/978-3-030-04885-3 building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. Another type of query asks for a most probable explanation (MPE) for given events. Specifically, this paper contributes (i) LDJT. to efficiently solve the temporal MPE problem for作者: STRIA 時(shí)間: 2025-3-25 19:37 作者: 構(gòu)想 時(shí)間: 2025-3-25 22:01
https://doi.org/10.1007/978-3-642-12331-3deal with this, e.g., random sampling, parallelization, or attribute extraction. A so far not investigated method in the realm of formal concept analysis is attribute selection, as done in machine learning. Building up on this we introduce a method for attribute selection in formal contexts. To this作者: GAVEL 時(shí)間: 2025-3-26 03:30 作者: ear-canal 時(shí)間: 2025-3-26 08:05
https://doi.org/10.1007/978-3-642-31208-3upporting unambiguous communication of information about system requirements between engineers. We present a diagrammatic approach to modelling rules of trust using an extended version of concept diagrams. Within the context of our proof-of-concept Network Function Virtualisation and Attestation env作者: judicial 時(shí)間: 2025-3-26 11:48 作者: 使腐爛 時(shí)間: 2025-3-26 14:01 作者: Ophthalmologist 時(shí)間: 2025-3-26 19:26
Modeling of Dynamic Object Systemsoms between classes of individuals in the web of data. RM is aimed at mining alternate descriptions from two datasets related to the same set of individuals. We reuse this process for providing definitions in terms of necessary and sufficient conditions to categories in DBpedia. Firstly, we recall t作者: ostensible 時(shí)間: 2025-3-27 00:04 作者: progestin 時(shí)間: 2025-3-27 03:23 作者: 運(yùn)動(dòng)性 時(shí)間: 2025-3-27 07:09
Modelling of Thinking and the Mindication. In this paper, we address the exploitation of LOD by utilizing SPARQL queries in order to extract social networks of entities. This enables the application of techniques from Social Network Analysis to study social interactions among entities, providing deep insights into their latent socia作者: Living-Will 時(shí)間: 2025-3-27 09:47 作者: GLOOM 時(shí)間: 2025-3-27 13:50 作者: Vasoconstrictor 時(shí)間: 2025-3-27 20:42 作者: 瑣事 時(shí)間: 2025-3-27 22:03
Temporal Relations Between Imprecise Time Intervals: Representation and Reasoningse personalized relations are based on our extension of the Vilain and Kautz’s point algebra. We showed that, unlike most related work, our temporal interval relations preserve many of the properties of the Allen’s interval algebra. Furthermore, we show how they can be used for temporal reasoning by作者: lacrimal-gland 時(shí)間: 2025-3-28 03:35
Adaptive Collaborative Filtering for Recommender Systemn construct a scalable model with small complexity, named Adaptive Collaborative Filtering. Experiments are conducted on Movielens, a public dataset, and FPT PLAY, a dataset of our media service. We have an increase of . on precision and get close to the best of previous methods on diversity, covera作者: 泛濫 時(shí)間: 2025-3-28 08:51
Enhancing Layered Enterprise Architecture Development Through Conceptual Structuresthe many pathways by which the meta-models can be traversed and understood in a Formal Concept Lattice. Through the LEAD meta-model exemplar, the wider appeal of . and directed graphs are also identified.作者: 疏遠(yuǎn)天際 時(shí)間: 2025-3-28 10:35 作者: Hot-Flash 時(shí)間: 2025-3-28 16:54
https://doi.org/10.1007/b118340l and its operational definitions support querying a data source containing different levels of uncertainty metadata. Finally, we discuss the perspectives with a view on supporting reasoning over uncertain linked data.作者: 紅潤(rùn) 時(shí)間: 2025-3-28 19:42 作者: Liability 時(shí)間: 2025-3-29 02:23 作者: 燦爛 時(shí)間: 2025-3-29 03:14 作者: Anal-Canal 時(shí)間: 2025-3-29 09:19
Ontology-Informed Lattice Reduction Using the Discrimination Power Indexs existing domain knowledge encoded in a semantic ontology and a novel relevance index to inform the reduction process. We demonstrate the utility of the proposed approach, achieving a significant reduction of lattice nodes, even when the ontology only provides partial coverage of the domain of interest.作者: 舊式步槍 時(shí)間: 2025-3-29 14:48 作者: Crepitus 時(shí)間: 2025-3-29 17:05
https://doi.org/10.1007/978-3-030-04885-3r a most probable explanation (MPE) for given events. Specifically, this paper contributes (i) LDJT. to efficiently solve the temporal MPE problem for temporal probabilistic relational models and (ii) a combination of LDJT and LDJT. to efficiently answer assignment queries for a given number of time steps.作者: 賠償 時(shí)間: 2025-3-29 20:05
https://doi.org/10.1007/978-3-319-15382-7implication. In most cases, combinations of t-norms and implications do not fit human intuitions. Based on these methods, we suggest the use of the product t-norm in the compositional rule of inference. We combine this t-norm with different known implications. We then study these combinations and check if they give reasonable consequences.作者: 撕裂皮肉 時(shí)間: 2025-3-30 00:04
https://doi.org/10.1007/978-1-4615-2718-3reasoning capabilities of . with a practical scenario from the digital humanity domain. We chose the FDS project in virtue of its inherent contextual nature, as well as its notable complexity which allow us to highlight many issues connected with contextual knowledge representation and reasoning.作者: 厭惡 時(shí)間: 2025-3-30 07:30
https://doi.org/10.1007/978-3-642-12331-3e concept lattice as well as distribution of objects on it. Finally, we overcome computational challenges for computing the relative relevance through an approximation approach based on information entropy.作者: 箴言 時(shí)間: 2025-3-30 09:05
https://doi.org/10.1007/978-3-642-31208-3d. To ensure that the modelling approach can be applied to general systems, we include generic patterns for extending our domain model and rules of trust. Consequently, through the use of a formal, yet accessible, diagrammatic notation, domain experts can define rules of trust for their systems.作者: 愉快嗎 時(shí)間: 2025-3-30 13:48 作者: Pelvic-Floor 時(shí)間: 2025-3-30 17:39 作者: placebo 時(shí)間: 2025-3-30 22:55 作者: 一加就噴出 時(shí)間: 2025-3-31 01:57
Formal Context Generation Using Dirichlet Distributionsant method, coin-tossing, recapitulate some of its shortcomings and examine its stochastic model. Building upon this we propose our Dirichlet model and develop an algorithm employing this idea. Through an experimental evaluation we show that our approach is a significant improvement with respect to the variety of contexts generated.
