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DC Proposal: Ontology Learning from Noisy Linked Data
[chapter]
2011
Lecture Notes in Computer Science
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., partial or erroneous data) arises from Linked Data construction. Our doctoral researches will make theoretical and engineering contribution to ontology learning approaches for noisy Linked Data. ...
Ontology learning -loosely, the process of knowledge extraction from diverse data sources -provides (semi-) automatic support for ontology construction. ...
Additionally, we gratefully acknowledge funding from the National Science Foundation of China under grants 60873153, 60803061, and 61170165.
References ...
doi:10.1007/978-3-642-25093-4_31
fatcat:uggfwwvk75d57de4gpd2vtclly
DC Proposal: Graphical Models and Probabilistic Reasoning for Generating Linked Data from Tables
[chapter]
2011
Lecture Notes in Computer Science
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A table's meaning is thus captured by mapping columns to classes in an appropriate ontology, linking cell values to literal constants, implied measurements, or entities in the linked data cloud (existing ...
Most current approaches to generating Semantic Web representations from tables requires human input to create schemas and often results in graphs that do not follow best practices for linked data. ...
The concepts come from their knowledge base Probase created from the text on the World Wide Web which can be noisy and "semantically poor" as compared to concepts from the Linked Open Data cloud. ...
doi:10.1007/978-3-642-25093-4_24
fatcat:fvx4dr54aja3zh2ctxw3wlspdu
Linked open data to represent multilingual poetry collections. A proposal to solve interoperability issues between poetic repertoires
2016
Zenodo
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Th is paper describes the creation of a poetic ontology in order to use it as a basis to link different databases and projects w orking on metrics and poetry. ...
The conceptual semantic model, written in OWL, includes classes and metadata from standard ontological models related to humanities fields (such as CIDOC or Dublin Core), and adds specific elements and ...
From 2007 to 2011, is has been maintained and further developed at SFB 632 in the context of the project "Linguistic Data Base". ...
doi:10.5281/zenodo.2551595
fatcat:4nbzl534ebgnbort742fhfoqam
Integrating Ontologies Using Ontology Learning Approach
2013
IEICE transactions on information and systems
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In this paper, we propose a Mid-Ontology learning approach that can automatically construct a simple ontology, linking related ontology predicates (class or property) in different data sets. ...
However, manually checking each data set is time-consuming, especially when many data sets from various domains are used. ...
To adapt to the LOD data sets, we designed an automatic ontology learning approach, which can construct an integrated ontology from various linked data sets. ...
doi:10.1587/transinf.e96.d.40
fatcat:tisoofnjxzapdovqnh7655vmnu
LinkedMDR: A Collective Knowledge Representation of a Heterogeneous Document Corpus
[chapter]
2017
Lecture Notes in Computer Science
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In this paper, we propose LinkedMDR: a novel ontology for Linked Multimedia Document Representation that describes the knowledge of a heterogeneous document corpus in a semantic data network. ...
The ever increasing need for extracting knowledge from heterogeneous data has become a major concern. ...
LinkedMDR: a novel ontology for Linked Multimedia Document Representation We propose a novel ontology for Linked Multimedia Document Representation, entitled LinkedMDR 7 . ...
doi:10.1007/978-3-319-64468-4_28
fatcat:7dltrdkudncnxkfx4csgeka454
Context-Aware Personalized Activity Modeling in Concurrent Environment
2017
2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
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Activity recognition, having endemic impact on smart homes, faces one of the biggest challenges in learning a personalized activity model completely by using a generic model especially for parallel and ...
Exploit Pyramid Match Kernel approach, augmented with a WorldNet matching on hierarchical concepts, to recognize activities, using the domain ontology, from a potentially noisy sensor sequence. ...
Data driven techniques [13] based on machine learning and data mining approaches. Strength these approaches is the ability to handle the noise, uncertainty, and incomplete sensor data. ...
doi:10.1109/ithings-greencom-cpscom-smartdata.2017.150
dblp:conf/ithings/SafyanUSIA17
fatcat:zwgru2hlongbbavghcwq3abxka
Toward an ontology-based approach of context-aware
2019
International Journal of Latest Trends in Engineering and Technology
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In this paper, we propose an ontology-based approach for modeling context-aware in an adaptive mobile learning environment. ...
In this study, we propose an ontology-based approach for modeling learner contextual information in order to provide learners with appropriate content related to their context of learning. ...
We aim to propose a metadata that defines the temporal context; this metadata is based on the following attributes: Temporal Context Awareness Meta Data Meta Data Description _TimeOfDay It presents periodic ...
doi:10.21172/1.123.02
fatcat:oty44puksjbxnjkcuu2nmxhozu
Clustering with relative constraints
2011
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '11
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• Informativeness: How can one extract the most informative relative constraints from given knowledge sources? ...
Experiments showed that our algorithm achieves significantly higher accuracy than the existing metric learning approach based on relative comparisons. ...
In [26] , an SVM-like approach is proposed to learn a weighted distance function from relative comparisons. Kumar et al. [22] proposed to learn an SVaD measure from relative comparisons. ...
doi:10.1145/2020408.2020564
dblp:conf/kdd/LiuZW11
fatcat:da2pt4mfvbdnlel7u3cmaimf2i
An unsupervised data-driven method to discover equivalent relations in large Linked Datasets
2016
Semantic Web Journal
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the assumption of the 'well-formedness' of ontologies which is unnecessarily true in the domain of Linked Open Data; 3) few have looked at schema heterogeneity from a single source, which is also a common ...
issue particularly in very large Linked Dataset created automatically from heterogeneous resources, or integrated from multiple datasets. ...
Acknowledgements Part of this research has been sponsored by the EP-SRC funded project LODIE: Linked Open Data for Information Extraction, EP/J019488/1. ...
doi:10.3233/sw-150193
fatcat:odzg7ctdg5avxbjwzsmmyubkai
Machine Learning Methods for Forest Image Analysis and Classification: A Survey of the State of the Art
2022
IEEE Access
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There is a mismatch between what domain experts expect from remote sensing data and what remote sensing science produces. ...
Knowledge-driven approaches have received much attention from the remote sensing fraternity. ...
It was first proposed by [54] . From the original data input, the PCA method tries combinations of input features in order to determine the best features that summarise the original data. ...
doi:10.1109/access.2022.3170049
fatcat:k4igwev3bbfyjgcfbxfksynmem
Constructing Differentiated Educational Materials Using Semantic Annotation for Sustainable Education in IoT Environments
2018
Sustainability
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Such interaction data can be collected through the physical devices to define personal data. ...
In order to solve this problem, in this paper, we propose a semantic annotation scheme for sustainable computing in the IoT environment. ...
Jihoon Moon collected experimental data, processed the data and visualized the experimental results. Eenjun Hwang conceived and supervised the work. ...
doi:10.3390/su10041296
fatcat:qzunrir4mfe7hlssjcwm66xgka
Knowledge-Driven Activity Recognition and Segmentation Using Context Connections
[chapter]
2014
Lecture Notes in Computer Science
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To this end, we use the Situation concept of the DOLCE+DnS Ultralite (DUL) ontology to formally capture the context of high-level activities. ...
We propose a knowledge-driven activity recognition and segmentation framework introducing the notion of context connections. ...
Creating local contexts Data: Observations: O = {o1, o2, ..., oi}, Domain context descriptors: D = {dC 1 , dC 2 , ..., dC k }, Nearest observations threshold: r. ...
doi:10.1007/978-3-319-11915-1_17
fatcat:suke2z6mbfehrhcch3qvtryzme
An ontology-based measure to compute semantic similarity in biomedicine
2011
Journal of Biomedical Informatics
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Some of these measures have been adapted to the biomedical field by incorporating domain information extracted from clinical data or from medical ontologies (such as MeSH or SNOMED CT). ...
After that, a new measure based on the exploitation of the taxonomical structure of a biomedical ontology is proposed. ...
Susanne Greenwood from Oxford Brookes University and Joseph Soniran. ...
doi:10.1016/j.jbi.2010.09.002
pmid:20837160
fatcat:fgkojwtk45fd7eaflkgbwtazem
EduBD: A Machine Understandable Approach to Integrate Information of Educational Institutions of Bangladesh
2016
International journal of Web & Semantic Technology
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of each institution and therefore achieved the semantic interoperability of our Linked Open Data (LOD) application by eliminating natural language polisemy problems. ...
Web contents related to educational institutions as well as their geographic data of a country is an emerging field of data sharing and consolidating with suitable data repositories to extract useful information ...
In our application we use the structure of ontology and ontology knowledge-base to express our domain data as Linked Data and distribute these knowledge pieces as Linked Open Data (LOD) to establish a ...
doi:10.5121/ijwest.2016.7101
fatcat:i645gx7jvrdedophmpj3qchoq4
Global Machine Learning for Spatial Ontology Population
2015
Social Science Research Network
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The machine learning framework is evaluated with SemEval-2012 and SemEval-2013 data from the spatial role labeling task. ...
To make a mapping between natural language and the spatial ontology, we propose a novel global machine learning framework for ontology population. ...
learning from the noisy role labels in the presence of a small dataset is more tricky for recognizing the spatial qualitative labels. ...
doi:10.2139/ssrn.3199172
fatcat:odzou2qnwjdotlpgsn4krw2pbm
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