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Context-Aware Analytics in MOM Applications [article]

Martin Ringsquandl, Steffen Lamparter, Raffaello Lepratti
<span title="2014-12-26">2014</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Manufacturing Operations Management (MOM) systems are complex in the sense that they integrate data from heterogeneous systems inside the automation pyramid. The need for context-aware analytics arises from the dynamics of these systems that influence data generation and hamper comparability of analytics, especially predictive models (e.g. predictive maintenance), where concept drift affects application of these models in the future. Recently, an increasing amount of research has been directed
more &raquo; ... owards data integration using semantic context models. Manual construction of such context models is an elaborate and error-prone task. Therefore, we pose the challenge to apply combinations of knowledge extraction techniques in the domain of analytics in MOM, which comprises the scope of data integration within Product Life-cycle Management (PLM), Enterprise Resource Planning (ERP), and Manufacturing Execution Systems (MES). We describe motivations, technological challenges and show benefits of context-aware analytics, which leverage from and regard the interconnectedness of semantic context data. Our example scenario shows the need for distribution and effective change tracking of context information.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1412.7968v1">arXiv:1412.7968v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/evc4gfxcunhl7lbxs2oi7cdvxa">fatcat:evc4gfxcunhl7lbxs2oi7cdvxa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191015203718/https://arxiv.org/pdf/1412.7968v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/60/db/60db937707a49524d6e6633d3444aa7926759161.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1412.7968v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Combining Sub-Symbolic and Symbolic Methods for Explainability [article]

Anna Himmelhuber, Stephan Grimm, Sonja Zillner, Mitchell Joblin, Martin Ringsquandl, Thomas Runkler
<span title="2021-12-03">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Similarly to other connectionist models, Graph Neural Networks (GNNs) lack transparency in their decision-making. A number of sub-symbolic approaches have been developed to provide insights into the GNN decision making process. These are first important steps on the way to explainability, but the generated explanations are often hard to understand for users that are not AI experts. To overcome this problem, we introduce a conceptual approach combining sub-symbolic and symbolic methods for
more &raquo; ... centric explanations, that incorporate domain knowledge and causality. We furthermore introduce the notion of fidelity as a metric for evaluating how close the explanation is to the GNN's internal decision making process. The evaluation with a chemical dataset and ontology shows the explanatory value and reliability of our method.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.01844v1">arXiv:2112.01844v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tcecav3ylvbllerx3eyhrfycbm">fatcat:tcecav3ylvbllerx3eyhrfycbm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211207032542/https://arxiv.org/pdf/2112.01844v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/74/b6/74b6f988103e4ec826206195c4135aef32e361ae.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.01844v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Reasoning on Knowledge Graphs with Debate Dynamics [article]

Marcel Hildebrandt, Jorge Andres Quintero Serna, Yunpu Ma, Martin Ringsquandl, Mitchell Joblin, Volker Tresp
<span title="2020-01-02">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose a novel method for automatic reasoning on knowledge graphs based on debate dynamics. The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to promote the fact being true (thesis) or the fact being false (antithesis), respectively. Based on these arguments, a binary classifier, called the judge, decides whether the fact is true or false. The two
more &raquo; ... ts can be considered as sparse, adversarial feature generators that present interpretable evidence for either the thesis or the antithesis. In contrast to other black-box methods, the arguments allow users to get an understanding of the decision of the judge. Since the focus of this work is to create an explainable method that maintains a competitive predictive accuracy, we benchmark our method on the triple classification and link prediction task. Thereby, we find that our method outperforms several baselines on the benchmark datasets FB15k-237, WN18RR, and Hetionet. We also conduct a survey and find that the extracted arguments are informative for users.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2001.00461v1">arXiv:2001.00461v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/b3x5mla7qfdqhihytg6tau5u2u">fatcat:b3x5mla7qfdqhihytg6tau5u2u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200321003244/https://arxiv.org/pdf/2001.00461v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2001.00461v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing [article]

Yushan Liu, Marcel Hildebrandt, Mitchell Joblin, Martin Ringsquandl, Volker Tresp
<span title="2020-07-10">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The graph structure of biomedical data differs from those in typical knowledge graph benchmark tasks. A particular property of biomedical data is the presence of long-range dependencies, which can be captured by patterns described as logical rules. We propose a novel method that combines these rules with a neural multi-hop reasoning approach that uses reinforcement learning. We conduct an empirical study based on the real-world task of drug repurposing by formulating this task as a link
more &raquo; ... on problem. We apply our method to the biomedical knowledge graph Hetionet and show that our approach outperforms several baseline methods.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.05292v1">arXiv:2007.05292v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sd5ftnax7jawdll3kndhx7slxy">fatcat:sd5ftnax7jawdll3kndhx7slxy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200722173336/https://arxiv.org/pdf/2007.05292v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/58/9b/589bf79968b1a789ccf883c74bf42c92c570212e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.05292v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Debate Dynamics for Human-comprehensible Fact-checking on Knowledge Graphs [article]

Marcel Hildebrandt, Jorge Andres Quintero Serna, Yunpu Ma, Martin Ringsquandl, Mitchell Joblin, Volker Tresp
<span title="2020-01-09">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose a novel method for fact-checking on knowledge graphs based on debate dynamics. The underlying idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to justify the fact being true (thesis) or the fact being false (antithesis), respectively. Based on these arguments, a binary classifier, referred to as the judge, decides whether the fact is true or false. The
more &raquo; ... two agents can be considered as sparse feature extractors that present interpretable evidence for either the thesis or the antithesis. In contrast to black-box methods, the arguments enable the user to gain an understanding for the decision of the judge. Moreover, our method allows for interactive reasoning on knowledge graphs where the users can raise additional arguments or evaluate the debate taking common sense reasoning and external information into account. Such interactive systems can increase the acceptance of various AI applications based on knowledge graphs and can further lead to higher efficiency, robustness, and fairness.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2001.03436v1">arXiv:2001.03436v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/un54gxz5lnfp7bppyeyynaqes4">fatcat:un54gxz5lnfp7bppyeyynaqes4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200321024737/https://arxiv.org/pdf/2001.03436v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2001.03436v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Semantic-Guided Feature Selection for Industrial Automation Systems [chapter]

Martin Ringsquandl, Steffen Lamparter, Sebastian Brandt, Thomas Hubauer, Raffaello Lepratti
<span title="">2015</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Modern industrial automation systems incorporate a variety of interconnected sensors and actuators that contribute to the generation of vast amounts of data. Although valuable insights for plant operators and engineers can be gained from such data sets, they often remain undiscovered due to the problem of applying machine learning algorithms in high-dimensional feature spaces. Feature selection is concerned with obtaining subsets of the original data, e.g. by eliminating highly correlated
more &raquo; ... es, in order to speed up processing time and increase model performance with less inclination to overfitting. In terms of high-dimensional data produced by automation systems, lots of dependencies between sensor measurements are already known to domain experts. By providing access to semantic data models for industrial data acquisition systems, we enable the explicit incorporation of such domain knowledge. In contrast to conventional techniques, this semantic feature selection approach can be carried out without looking at the actual data and facilitates an intuitive understanding of the learned models. In this paper we introduce two semantic-guided feature selection approaches for different data scenarios in industrial automation systems. We evaluate both approaches in a manufacturing use case and show competitive or even superior performance compared to conventional techniques.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-25010-6_13">doi:10.1007/978-3-319-25010-6_13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hkgc27ndx5dh3fnkkcoeqtofta">fatcat:hkgc27ndx5dh3fnkkcoeqtofta</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20160430163035/http://iswc2015.semanticweb.org/sites/iswc2015.semanticweb.org/files/93670191.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2d/2a/2d2aa101115569878c5228eaee1cdab44edf5b25.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-25010-6_13"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

On event-driven knowledge graph completion in digital factories

Martin Ringsquandl, Evgeny Kharlamov, Daria Stepanova, Steffen Lamparter, Raffaello Lepratti, Ian Horrocks, Peer Kroger
<span title="">2017</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/faqqmambavbalpofpx3p6nntua" style="color: black;">2017 IEEE International Conference on Big Data (Big Data)</a> </i> &nbsp;
Smart factories are equipped with machines that can sense their manufacturing environments, interact with each other, and control production processes. Smooth operation of such factories requires that the machines and engineering personnel that conduct their monitoring and diagnostics share a detailed common industrial knowledge about the factory, e.g., in the form of knowledge graphs. Creation and maintenance of such knowledge is expensive and requires automation. In this work we show how
more &raquo; ... ne learning that is specifically tailored towards industrial applications can help in knowledge graph completion. In particular, we show how knowledge completion can benefit from event logs that are common in smart factories. We evaluate this on the knowledge graph from a real world-inspired smart factory with encouraging results.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/bigdata.2017.8258105">doi:10.1109/bigdata.2017.8258105</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/bigdataconf/RingsquandlKSLL17.html">dblp:conf/bigdataconf/RingsquandlKSLL17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zsujquk2gve7fc4ut3463kqdbu">fatcat:zsujquk2gve7fc4ut3463kqdbu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210915002137/https://arxiv.org/pdf/2109.03655v1.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2a/19/2a1918d21147d062ff9fa6291c4c5a8bf586466d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/bigdata.2017.8258105"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Reasoning on Knowledge Graphs with Debate Dynamics

Marcel Hildebrandt, Jorge Andres Quintero Serna, Yunpu Ma, Martin Ringsquandl, Mitchell Joblin, Volker Tresp
<span title="2020-04-03">2020</span> <i title="Association for the Advancement of Artificial Intelligence (AAAI)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wtjcymhabjantmdtuptkk62mlq" style="color: black;">PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE</a> </i> &nbsp;
We propose a novel method for automatic reasoning on knowledge graphs based on debate dynamics. The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments – paths in the knowledge graph – with the goal to promote the fact being true (thesis) or the fact being false (antithesis), respectively. Based on these arguments, a binary classifier, called the judge, decides whether the fact is true or false. The two
more &raquo; ... can be considered as sparse, adversarial feature generators that present interpretable evidence for either the thesis or the antithesis. In contrast to other black-box methods, the arguments allow users to get an understanding of the decision of the judge. Since the focus of this work is to create an explainable method that maintains a competitive predictive accuracy, we benchmark our method on the triple classification and link prediction task. Thereby, we find that our method outperforms several baselines on the benchmark datasets FB15k-237, WN18RR, and Hetionet. We also conduct a survey and find that the extracted arguments are informative for users.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1609/aaai.v34i04.6600">doi:10.1609/aaai.v34i04.6600</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jqiazvhvuzccdf74vmjao5dkbi">fatcat:jqiazvhvuzccdf74vmjao5dkbi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201106070559/https://www.aaai.org/ojs/index.php/AAAI/article/download/6600/6454" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fa/bf/fabfcc4b1f92f9f6165fcb9c403b49dfecd6c5bd.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1609/aaai.v34i04.6600"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Task-driven knowledge graph filtering improves prioritizing drugs for repurposing

Florin Ratajczak, Mitchell Joblin, Martin Ringsquandl, Marcel Hildebrandt
<span title="2022-03-04">2022</span>
Drug repurposing aims at finding new targets for already developed drugs. It becomes more relevant as the cost of discovering new drugs steadily increases. To find new potential targets for a drug, an abundance of methods and existing biomedical knowledge from different domains can be leveraged. Recently, knowledge graphs have emerged in the biomedical domain that integrate information about genes, drugs, diseases and other biological domains. Knowledge graphs can be used to predict new
more &raquo; ... ons between compounds and diseases, leveraging the interconnected biomedical data around them. While real world use cases such as drug repurposing are only interested in one specific relation type, widely used knowledge graph embedding models simultaneously optimize over all relation types in the graph. This can lead the models to underfit the data that is most relevant for the desired relation type. For example, if we want to learn embeddings to predict links between compounds and diseases but almost the entirety of relations in the graph is incident to other pairs of entity types, then the resulting embeddings are likely not optimised to predict links between compounds and diseases. We propose a method that leverages domain knowledge in the form of metapaths and use them to filter two biomedical knowledge graphs (Hetionet and DRKG) for the purpose of improving performance on the prediction task of drug repurposing while simultaneously increasing computational efficiency. We find that our method reduces the number of entities by 60% on Hetionet and 26% on DRKG, while leading to an improvement in prediction performance of up to 40.8% on Hetionet and 14.2% on DRKG, with an average improvement of 20.6% on Hetionet and 8.9% on DRKG. Additionally, prioritization of antiviral compounds for SARS CoV-2 improves after task-driven filtering is applied. Knowledge graphs contain facts that are counter productive for specific tasks, in our case drug repurposing. We also demonstrate that these facts can be removed, resulting in an improved performance in that task and a more efficient learning process.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-022-04608-y">doi:10.1186/s12859-022-04608-y</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35246025">pmid:35246025</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8894843/">pmcid:PMC8894843</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zcukyhfvlzhn5g4ejj5rhqb5ga">fatcat:zcukyhfvlzhn5g4ejj5rhqb5ga</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220421041148/https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-022-04608-y.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/16/56/1656bc2f34a9ed12ba755b3ef0b3828524d39726.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-022-04608-y"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8894843" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Power to the Relational Inductive Bias: Graph Neural Networks in Electrical Power Grids [article]

Martin Ringsquandl, Houssem Sellami, Marcel Hildebrandt, Dagmar Beyer, Sylwia Henselmeyer, Sebastian Weber, Mitchell Joblin
<span title="2021-09-08">2021</span> <span class="release-stage" >pre-print</span>
The application of graph neural networks (GNNs) to the domain of electrical power grids has high potential impact on smart grid monitoring. Even though there is a natural correspondence of power flow to message-passing in GNNs, their performance on power grids is not well-understood. We argue that there is a gap between GNN research driven by benchmarks which contain graphs that differ from power grids in several important aspects. Additionally, inductive learning of GNNs across multiple power
more &raquo; ... rid topologies has not been explored with real-world data. We address this gap by means of (i) defining power grid graph datasets in inductive settings, (ii) an exploratory analysis of graph properties, and (iii) an empirical study of the concrete learning task of state estimation on real-world power grids. Our results show that GNNs are more robust to noise with up to 400% lower error compared to baselines. Furthermore, due to the unique properties of electrical grids, we do not observe the well known over-smoothing phenomenon of GNNs and find the best performing models to be exceptionally deep with up to 13 layers. This is in stark contrast to existing benchmark datasets where the consensus is that 2 to 3 layer GNNs perform best. Our results demonstrate that a key challenge in this domain is to effectively handle long-range dependence.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3459637.3482464">doi:10.1145/3459637.3482464</a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.03604v1">arXiv:2109.03604v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/owwrx5vhercwpizpno5fu6xc6i">fatcat:owwrx5vhercwpizpno5fu6xc6i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210914162722/https://arxiv.org/pdf/2109.03604v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/70/51/7051877b41956fc4bc68e75a6b24914be9fcf768.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3459637.3482464"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> acm.org </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.03604v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Assessing IFC classes with means of geometric deep learning on different graph encodings

Fiona C. Collins, Alexander Braun, Martin Ringsquandl, Daniel M. Hall, André Borrmann
<span title="2021-07-26">2021</span> <i title="University College Dublin"> Proceedings of the 2021 European Conference on Computing in Construction </i> &nbsp; <span class="release-stage">unpublished</span>
Machine-readable Building Information Models (BIM) are of great benefit for the building operation phase. Losses through data exchange or issues in software interoperability can significantly impede their availability. Incorrect and imprecise semantics in the exchange format IFC are frequent and complicate knowledge extraction. To support an automated IFC object correction, we use a Geometric Deep Learning (GDL) approach to perform classification based solely on the 3D shape. A Graph
more &raquo; ... al Network (GCN) uses the native triangle-mesh and automatically creates meaningful local features for subsequent classification. The method reaches an accuracy of up to 85% on our self-assembled, partially industry dataset.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.35490/ec3.2021.168">doi:10.35490/ec3.2021.168</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xczvgevgu5habd2racnho75ijy">fatcat:xczvgevgu5habd2racnho75ijy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210918145415/https://ec-3.org/conference2021/wp-content/uploads/sites/4/2021/07/Contribution_168_final.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/44/34/44342ff176615e575b37f802edaaa1818ab9d9f3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.35490/ec3.2021.168"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

An energy-based model for neuro-symbolic reasoning on knowledge graphs [article]

Dominik Dold, Josep Soler Garrido
<span title="2021-10-04">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We thank Marcel Hildebrandt, Serghei Mogoreanu and Martin Ringsquandl for helpful discussions, Johannes Frank for setting up the demonstrator and our colleagues at Siemens SMR and the AI Lab for their  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.01639v1">arXiv:2110.01639v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sh7s2uiqvngz5l5niry7cutavi">fatcat:sh7s2uiqvngz5l5niry7cutavi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211007030633/https://arxiv.org/pdf/2110.01639v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/be/4c/be4c236a3779fe07ae8961f5bf4a1b641340bf99.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.01639v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Análise de Sentimentos: Da Psicométrica à Psicopolítica

Felipe Melhado, Jean-Martin Rabot
<span title="">2021</span> <i title="Centro de Estudos de Comunicação e Sociedade (CECS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ppf25vy6fvba5okemzccwrp5ju" style="color: black;">Revista Comunicação e Sociedade</a> </i> &nbsp;
Rabot and co-supervision by Moisés de Lemos Martins and Norval Baitello.  ...  Attesting the dissemination of sentiment analysis for electoral purposes, some authors go so far as to affirm that "it can be expected that sentiment analysis will be part of every campaign in the future" (Ringsquandl  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17231/comsoc.39(2021).2797">doi:10.17231/comsoc.39(2021).2797</a> <a target="_blank" rel="external noopener" href="https://doaj.org/article/668a1772258446e6b522d1c393e52364">doaj:668a1772258446e6b522d1c393e52364</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/btdbmwxybvgnjhnh6goypumuw4">fatcat:btdbmwxybvgnjhnh6goypumuw4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210716155343/https://revistacomsoc.pt/index.php/revistacomsoc/article/download/2797/3534" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/57/4d/574de85ce051f4f8f61ed7ea9f59b4e4e29335c6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17231/comsoc.39(2021).2797"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

The State-of-the-Art in Twitter Sentiment Analysis

David Zimbra, Ahmed Abbasi, Daniel Zeng, Hsinchun Chen
<span title="2018-08-24">2018</span> <i title="Association for Computing Machinery (ACM)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/246ryqhr5jgqlbefuy7mh23yny" style="color: black;">ACM Transactions on Management Information Systems</a> </i> &nbsp;
As part of their evaluation process, the evaluators began by using Appraisal Theory (Scherer 1999; Martin and White 2005) , and its related literature from the natural language processing community, to  ...  (Rui et al. 2013; Verma et al. 2015) , and the outcomes of political elections (Tumasjan et al. 2010; O'Connor et al. 2010; Bermingham and Smealton 2010; Chung and Mustafaraj 2011; Gayo-Avello 2013; Ringsquandl  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3185045">doi:10.1145/3185045</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fzpm7xhkyvd2newi2yp3gze7gm">fatcat:fzpm7xhkyvd2newi2yp3gze7gm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190205011006/http://www.business.uwm.edu/gdrive/Zhao_H/BUS-ADM-991/Papers/ZIM18.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b5/10/b510de361f440c2b3234077d7ad78deb4fefa27a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3185045"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Explainable methods for knowledge graph refinement and exploration via symbolic reasoning [article]

Mohamed Hassan Mohamed Gad-Elrab, Universität Des Saarlandes
<span title="2021-08-19">2021</span>
I would like to thank Martin Theobald ad Simon Razniewsk for accepting to be part of the thesis committee. Also, for the earlier fruitful discussions with them.  ...  Intuitively, these approaches turn a KG, possibly augmented with external sources such as text [Xiao et al., 2017] or log files [Ringsquandl et al., 2018] , into a probabilistic representation of its  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.22028/d291-34423">doi:10.22028/d291-34423</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hvpoxkfc5zgmbce32pfbcvmejy">fatcat:hvpoxkfc5zgmbce32pfbcvmejy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210824195238/https://publikationen.sulb.uni-saarland.de/bitstream/20.500.11880/31629/1/Doctoral_Thesis.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/27/e1/27e1015eab86c49c615c6462db46763b644ba442.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.22028/d291-34423"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>