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检索条件"主题词=semantic computing"

共 128 条记录，以下是61-70 订阅

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Unsupervised Singleton Expansion from Free Text 12

Unsupervised Singleton Expansion from Free Text

引用

12th IEEE International Conference on semantic computing (ICSC)

作者： Atzori, Maurizio Balloccu, Simone Bellanti, Andrea Univ Cagliari Dept Maths & Comp Sci Via Osped 72 I-09124 Cagliari Italy

ISBN: (纸本)9781538644072

Set Expansion is the problem of automatically extending a given set of seed elements with objects of the same class, for instance {red, green, white}. {red, green, white, gray, yellow,...}. In this paper we address the problem in the challenging scenario of extending singletons, that is, sets with only one seed element. Differently from existing work, we do not assume the presence of markup, such as html lists, nor whatsoever ontology, indeed relying only on free (unstructured, plain) text. Despite the challenging problem, we show that the singleton expansion can be accomplished unsupervisedly by means of nearest neighbor search (NNS) over word embeddings. We further propose an algorithm that significantly improve the performance of NNS both for small and large (long tail) expansions, while maintaining the important quality of being language independent.

关键词： Word Embeddings Set Expansion Similarity Measure semantic computing Natural Language Processing

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Feature Distance-based Framework for Classification of Low-Frequency semantic Relations

Feature Distance-based Framework for Classification of Low-F...

引用

5th Annual IEEE International Conference on semantic computing (ICSC)

作者： Horie, Andre Kenji Ishizuka, Mitsuru Univ Tokyo Sch Informat Sci & Technol Tokyo Japan

ISBN: (纸本)9780769544922

In the relation extraction of semantic relations, it is not uncommon to face settings in which the training data provides very few instances of some relation classes. This is mostly due to the high cost of producing such data and to the class imbalance problem, which may result in some classes presenting small frequencies even with a large annotated corpus. This work thus presents a semi-supervised bootstrapped method to expand this initial training dataset, using pattern matching to extract new candidate instances from the Web. The core of this process uses a multiview feature distance-based framework, which allows quantitative and qualitative analysis of intermediate steps of the process. Experimental results show that this framework provides better results in the relation classification task than the baseline, and the bootstrapped architecture improves the relation classification task as a whole for these low-frequency semantic relations settings.

关键词： semantic computing Concept Description Natural Language Text

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ESKAPE: Information Platform for Enabling semantic Data Processing 19

ESKAPE: Information Platform for Enabling Semantic Data Proc...

引用

19th International Conference on Enterprise Information Systems (ICEIS)

作者： Pomp, Andre Paulus, Alexander Jeschke, Sabina Meisen, Tobias Rhein Westfal TH Aachen Inst Informat Management Mech Engn Aachen Germany

ISBN: (纸本)9789897582486

Over the last years, many Internet of Things (IoT) platforms have been developed to manage data from public and industrial environmental settings. To handle the upcoming amounts of structured and unstructured data in those fields, a couple of these platforms use ontologies to model the data semantics. However, generating ontologies is a complex task since it requires to collect and model all semantics of the provided data. Since the (Industrial) IoT is fast and continuously evolving, a static ontology will not be able to model each requirement. To overcome this problem, we developed the platform ESKAPE, which uses semantic models in addition to data models to handle batch and streaming data on an information focused level. Our platform enables users to process, query and subscribe to heterogeneous data sources without the need to consider the data model, facilitating the creation of information products from heterogeneous data. Instead of using a pre-defined ontology, ESKAPE uses a knowledge graph which is expanded by semantic models defined by users upon their data sets. Utilizing the semantic annotations enables data source substitution and frees users from analyzing data models to understand their content. A first prototype of our platform was evaluated by a user study in form of a competitive hackathon, during which the participants developed mobile applications based on data published on the platform by local companies. The feedback given by the participants reveals the demand for platforms that are capable of handling data on a semantic level and allow users to easily request data that fits their application.

关键词： semantic computing semantic Model Knowledge Graph Internet of Things Data Processing

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Guiding Corpus-based Set Expansion by Auxiliary Sets Generation and Co-Expansion 20

Guiding Corpus-based Set Expansion by Auxiliary Sets Generat...

引用

29th World Wide Web Conference (WWW)

作者： Huang, Jiaxin Xie, Yiqing Meng, Yu Shen, Jiaming Zhang, Yunyi Han, Jiawei Univ Illinois Champaign IL 61820 USA Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781450370233

Given a small set of seed entities (e.g., "USA", "Russia"), corpus-based set expansion is to induce an extensive set of entities which share the same semantic class (Country in this example) from a given corpus. Set expansion benefits a wide range of downstream applications in knowledge discovery, such as web search, taxonomy construction, and query suggestion. Existing corpus-based set expansion algorithms typically bootstrap the given seeds by incorporating lexical patterns and distributional similarity. However, due to no negative sets provided explicitly, these methods suffer from semantic drift caused by expanding the seed set freely without guidance. We propose a new framework, Set-Co Expan, that automatically generates auxiliary sets as negative sets that are closely related to the target set of user's interest, and then performs multiple sets co-expansion that extracts discriminative features by comparing target set with auxiliary sets, to form multiple cohesive sets that are distinctive from one another, thus resolving the semantic drift issue. In this paper we demonstrate that by generating auxiliary sets, we can guide the expansion process of target set to avoid touching those ambiguous areas around the border with auxiliary sets, and we show that Set-Co Expan outperforms strong baseline methods significantly.

关键词： Set Expansion Bootstrap Methods semantic computing Web Mining

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semantic Programming for Device-Edge-Cloud Continuum 31

Semantic Programming for Device-Edge-Cloud Continuum

引用

31st IEEE International Conference on Network Protocols (ICNP)

作者： Le-Tuan, Anh Bowden, David Le-Phuoc, Danh Tech Univ Berlin Berlin Germany Dell Technol Cork Ireland

ISBN: (纸本)9798350303223

This position paper presents ThothSP, a semantic Programming framework with the aim of lowering the coding effort in building smart applications on the Device-Edge -Cloud continuum by leveraging semantic knowledge. It introduces a novel neural-symbolic stream fusion mechanism, which enables the specification of data fusion pipelines via declarative rules, with degrees of learnable probabilistic weights. Moreover, it includes an adaptive federator that allows the ThothoTuntime to be distributed across multiple compute nodes in a network, and to coordinate their resources to collaboratively process tasks by delegating partial workloads to their peers. To demonstrate ThothSP's capability, we report a case study on a distributed camera network to show ThothSP's behaviour against a traditional edge-cloud setup.

关键词： edge computing autonomous system distributed AI semantic computing

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An individual-centric probabilistic extension for OWL: Modelling the Uncertainness

An individual-centric probabilistic extension for OWL: Model...

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15th Annual International Conference on Computational Science (ICCS)

作者： Pileggi, Salvatore F. INRIA Paris France UPMC LIP6 Paris France

The theoretical benefits of semantics as well as their potential impact on IT are well known concepts, extensively discussed in literature. As more and more systems are currently using or referring semantic technologies, the challenging third version of the web (semantic Web or Web 3.0) is progressively taking shape. On the other hand, apart from the relatively limited capabilities in terms of expressiveness characterizing current concrete semantic technologies, theoretical models and research prototypes are actually overlooking a significant number of practical issues including, among others, consolidated mechanisms to manage and maintain vocabularies, shared notations systems and support to high scale systems (Big Data). Focusing on the OWL model as the current reference technology to specify web semantics, in this paper we will discuss the problem of approaching the knowledge engineering exclusively according to a deterministic model and excluding a priori any kind of probabilistic semantic. Those limitations determine that most knowledge ecosystems including, at some level, probabilistic information are not well suited inside OWL environments. Therefore, despite the big potential of OWL, a consistent number of applications are still using more classic data models or unnatural hybrid environments. But OWL, even with its intrinsic limitations, reflects a model flexible enough to support extensions and integrations. In this work we propose a simple statistical extension for the model that can significantly spread the expressiveness and the purpose of OWL.

关键词： semantic computing Knowledge Modelling Ontology Engineering OWL

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A Multi-dimensional Visualization Method for Disaster Analysis on 5D World Map System

A Multi-dimensional Visualization Method for Disaster Analys...

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20th International Electronics Symposium on Knowledge Creation and Intelligent computing (IES-KCIC)

作者： Uraki, Asako Sasaki, Shiori Kiyoki, Yasushi Keio Univ Grad Sch Media & Governance Yokohama Kanagawa Japan

ISBN: (纸本)9781538680797

It is becoming essentially significant to analyze and recognize environmental situations from various viewpoints on each purpose in environmental experts in governments, international organizations, universities, and research institutes. Visualizing environments in combination with relationships, reflections, and co-occurrences through multiple kinds of data is effective to interpret actual situations to analyze those data combinatory to see causality/resultant through multiple phenomena. This paper presents "A Multidimensional Visualization Method for Disaster Analysis on 5D World Map System". The 5D World Map System allows calculating in five dimensions, semantic in one dimension (1D), geographical world map in three dimensions (3D), and time in 1 dimension (1D), we have already presented. One of the essential features of our new method is to express one semantic in one Knowledge-Layer, and the selected Knowledge-Layers can be mapped simultaneously (transparency) to integrate/reduce multiple semantics into one visual dimension on the 5D World Map. Our method makes compatibility for multiple contexts of disaster phenomena. We also show several experimental results of our method for disaster analysis correspond to SDG11 has been proposed by United Nations, to clarify the feasibility of our method, and also clarify the applicability for the area of disaster analysis.

关键词： Visualization Global Environmental Analysis semantic computing Multimedia Data Mining Analytical visualization

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Improving Student Surveys with Natural Language Processing 2

Improving Student Surveys with Natural Language Processing

引用

2nd IEEE International Conference on Robotic computing (IRC)

作者： Hood, Karoline M. Kuiper, Patrick K. US Mil Acad West Point NY 10997 USA

ISBN: (纸本)9781538646519

Stakeholders from academic institutions across the world employ surveys to assess the quality of their work. With surveys these stakeholders attempt to obtain quantified, structured, and directed data in order to make decisions. Often these stakeholders employ long, directed Likert scaled surveys to gain this information. We propose an alternate construction for academic surveys, where stakeholders provide 1-3 open ended "free text" questions, allowing students to lead the discussion. We call this survey methodology "Student Directed Discussion Surveys" (SDDS). SDDS retain the ability to provide quantified, structured, and directed results by employing Natural Language Processing (NLP). We confirm the accuracy of SDDS in relation to traditional Likert scaled surveys with a permutation test, assessing a negligible statistical difference between SDDS and Likert surveys using real data. We then show the utility of SDDS by employing word frequency and sentiment analysis, providing important unbiased decision making information, which is limited when traditional Likert scaled surveys are administered.

关键词： natural language processing semantic computing surveys

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Towards Combining semantic Concepts From Multiple Knowledge Bases 12

Towards Combining Semantic Concepts From Multiple Knowledge ...

引用

12th IEEE International Conference on semantic computing (ICSC)

作者： Paulus, Alexander Pomp, Andre Poth, Lucian Lipp, Johannes Meisen, Tobias Rhein Westfal TH Aachen Inst Informat Management Mech Engn Aachen Germany

ISBN: (纸本)9781538644072

In today's connected industrial environments, semantic models allow machines to process data based on the semantic meaning instead of raw values. In that context, finding meaningful semantic concepts for data attributes currently cannot be fully automated. In this paper, we propose our concept for a framework to partially automate the annotation. Using adapters for different data sources, possible semantic concepts are acquired, compared and rated to select the most fitting ones which are then recommended for the annotation.

关键词： annotation recommendation engine semantic computing semantic networks

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5D World Map System for Disaster-Resilience Monitoring from Global to Local: Environmental AI System for Leading SDG 9 and 11 29

5D World Map System for Disaster-Resilience Monitoring from ...

引用

29th International Conference on Information Modeling and Knowledge Bases (EJC)

作者： Sasaki, Shiori Kiyoki, Yasushi Sarkar-Swaisgood, Madhurima Wijitdechakul, Jinmika Rachmawan, Irene Erlyn Wina Srivastava, Sanjay Shaw, Rajib Veesommai, Chalisa Keio Univ Grad Sch Media & Governance 5322 Endo Fujisawa Kanagawa Japan United Nations Econ & Social Commiss Asia & Pacif ICT & Disaster Risk Reduct Div IDD Bangkok Thailand Kasetsart Univ Fac Sci Bangkok Thailand

ISBN: (纸本)9781643680453;9781643680446

This paper presents a 5D World Map System's application for disaster-resilience monitoring as "Environmental AI System" of each player's implementation of United Nation's SDG 9 and DGS 11 from global-level to regional-level, country-level, sub-regional-level and city-level. In Asia-Pacific, disaster risk is outpacing disaster resilience. The gap between risk and resilience-building is growing in those countries with the least capacity to prepare for and respond to disasters. Using the Sensing-Processing-Actuation (SPA) functions of 5D World Map System, a disaster risk analysis can be conducted in multiple contexts, including regional, national, and sub-national. At the regional, national and subnational levels, the analysis will focus on identifying disaster risk hotspots through incorporating existing multi-hazard disaster risk and socio-economic risk information. The system will further be used to assess future risks through integration of global climate scenarios downscaled to the region as well as countries. This paper presents the design of two new actuation functions of 5D World Map System: (1) Short-term warning with prediction and push alert and (2) Long-term warning with context-dependent multidimensional visualization, and examines the applicability of these functions by indicating that (1) will support both resident and those who are working at the operational level by being customized to disaster risk analysis for each target region/country/area, and (2) assist both policy-makers and sectoral ministries in target countries to use the analysis for evidence-based policy formulation, planning and investment towards building disaster-resilient society.

关键词： semantic Search semantic computing Big Data IoT AI Cyber-Physical System Spatiotemporal Visualization Data Mining Warning SPA Sensing Processing Actuation Environmental Research SDGs UN United Nation Climate Change Global Warming Sustainable Development

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