Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics最新文献

SciPuRe

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405978

Martin Lentschat, Patrice Buche, Juliette Dibie-Barthélemy, Mathieu Roche

{"title":"SciPuRe","authors":"Martin Lentschat, Patrice Buche, Juliette Dibie-Barthélemy, Mathieu Roche","doi":"10.1145/3405962.3405978","DOIUrl":"https://doi.org/10.1145/3405962.3405978","url":null,"abstract":"Retrieving entities associated with experimental data in the textual content of scientific documents faces numbers of challenges. One of them is the assessment of the extracted entities for further process, especially the identification of false positives. We present in this paper SciPuRe (Scientific Publication Representation): a new representation of entities. The extraction process presented in this paper is driven by an Ontological and Terminological Resource (OTR). It is applied to the extraction of entities associated with food packaging permeabilities, that can be symbolic (e.g. the Packaging \"low density polyethylene\") or quantitative (e.g. the Temperature \"25\", \"°C\" or the H20_Permeability \"4.34 * 10-3\", \"cm3 μm-2 d-1 kPa\"). A representation of each entity, composed of a set of features, is built during the extraction process. These features can be gathered in three categories: Ontological, Lexical and Structural. The features of SciPuRe are used to compute Relevance scores that consider the different information available for each entity extracted. Such Relevance scores inform the usefulness of SciPuRe and can then be used to rank the extraction results and discard false positives.","PeriodicalId":247414,"journal":{"name":"Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114980772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

A Novel Dataset for Fake Android Anti-Malware Detection 伪Android反恶意软件检测的新数据集

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405980

Saeed Seraj, Michalis Pavlidis, Nikolaos Polatidis

引用次数: 1

Splitting the Web Analytics Atom: From Page Metrics and KPIs to Sub-Page Metrics and KPIs 拆分Web分析原子:从页面指标和kpi到子页面指标和kpi

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405984

Ilan Kirsh, M. Joy

{"title":"Splitting the Web Analytics Atom: From Page Metrics and KPIs to Sub-Page Metrics and KPIs","authors":"Ilan Kirsh, M. Joy","doi":"10.1145/3405962.3405984","DOIUrl":"https://doi.org/10.1145/3405962.3405984","url":null,"abstract":"Web analytics Key Performance Indicators (KPIs) are important metrics used to evaluate websites and web pages against objectives. The power of KPIs is in their simplicity. Every web page can be assessed by numeric KPI values, which can be easily calculated, compared, and tracked over time. KPIs highlight the strengths and weaknesses of individual web pages and significantly help in maintaining, improving, and optimizing websites. Current web analytics metrics and KPIs, in academic studies as well as in commercial tools, relate to entire websites and web pages. This paper advocates extending KPIs use to sub-page elements, such as paragraphs, as an effective way to refine knowledge and leverage web analytics capabilities. We discuss the potential and challenges of sub-page web analytics and define a framework for calculating sub-page metrics from accumulated in-page user activity data, such as mouse and keyboard events. Then we propose potential KPIs that may be effective in highlighting the strengths and weaknesses of individual page parts, such as paragraphs. We use web usage data from a sample website to demonstrate these ideas. This study is the first step towards sub-page web analytics metrics and KPIs. Further work is required in order to gain more knowledge about potential KPIs that are introduced in this work, as well as to explore new methods, metrics, and KPIs.","PeriodicalId":247414,"journal":{"name":"Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics","volume":"52 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114127963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Semi-automatic extraction and validation of concepts in ontology learning from texts in Spanish 西班牙语文本本体学习中概念的半自动提取与验证

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405977

Manuela Gómez-Suta, J. Echeverry-Correa, José A. Soto Mejía

引用次数: 1

The ICARUS Ontology: A general aviation ontology developed using a multi-layer approach ICARUS本体:使用多层方法开发的通用航空本体

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405983

Dimosthenis Stefanidis, Chrysovalantis Christodoulou, M. Symeonidis, G. Pallis, M. Dikaiakos, Loukas Pouis, Kalia Orphanou, Fenareti Lampathaki, D. Alexandrou

{"title":"The ICARUS Ontology: A general aviation ontology developed using a multi-layer approach","authors":"Dimosthenis Stefanidis, Chrysovalantis Christodoulou, M. Symeonidis, G. Pallis, M. Dikaiakos, Loukas Pouis, Kalia Orphanou, Fenareti Lampathaki, D. Alexandrou","doi":"10.1145/3405962.3405983","DOIUrl":"https://doi.org/10.1145/3405962.3405983","url":null,"abstract":"The management of aviation data is a great challenge in the aviation industry, as they are complex and can be derived from heterogeneous data sources. To handle this challenge, ontologies can be applied to facilitate the modelling of the data across multiple data sources. This paper presents an aviation domain ontology, the ICARUS ontology, which aims at facilitating the semantic description and integration of information resources that represent the various assets of the ICARUS platform and their use. To present the functionality and usability of the proposed ontology, we present the results of querying the ontology using SPARQL queries through three use case scenarios. As shown from the evaluation, the ICARUS ontology enables the integration and reasoning over multiple sources of heterogeneous aviation-related data, the semantic description of metadata produced by ICARUS, and their storage in a knowledge-base which is dynamically updated and provides access to its contents via SPARQL queries.","PeriodicalId":247414,"journal":{"name":"Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132618398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Optimizing Business Process Designs with a Multiple Population Genetic Algorithm 用多种群遗传算法优化业务流程设计

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405971

Nadir Mahammed, S. Bennabi, Mahmoud Fahsi

引用次数: 0

Topic Modeling of Short Texts Using Anchor Words 基于锚定词的短文本主题建模

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405968

Florian Steuber, Mirco Schönfeld, G. Rodosek

引用次数: 7

Adaptive Error Prediction for Production Lines with Unknown Dependencies 未知依赖关系生产线的自适应误差预测

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405994

S. Soller, M. Kranz, Gerold Hölzl

{"title":"Adaptive Error Prediction for Production Lines with Unknown Dependencies","authors":"S. Soller, M. Kranz, Gerold Hölzl","doi":"10.1145/3405962.3405994","DOIUrl":"https://doi.org/10.1145/3405962.3405994","url":null,"abstract":"Forecasting or predicting errors can dramatically reduce the downtime of machines in industrial settings and even allow to take counteractions long before the error affects the production system. A forecast system to predict upcoming critical values for identical production lines under different environmental circumstances is proposed. We focus on errors that result in multiple erroneous work pieces. These error patterns need manual corrections by a machine controller. An analysis of the system observed gathered the information about the types of errors that are observable. 30% of errors are measurement errors or single faulty work-pieces which are not influenced by previous work-pieces and do not show any indication to preceding work-pieces. These errors do not need any type of action by the machine controller. 70% of the observed errors are continuous system deviations which lead to multiple erroneous work-pieces in order or a high percentage of erroneous work-pieces in an observed time frame. We observe multiple production lines which consist of identical machines and produce the same product type. For the forecast of errors, we use the ARIMA, Holt and Holt-Winter method. Each production line and product type combination showed different results for the different forecast methods. We implemented a dynamic system that automatically detects the seasonality and trend of the specific combination to assign a correct forecast method and model. For 40 combinations of production line and product type the holt-winter algorithm performed best for 14, the holt-winter without seasonal or trend component performed best for 13 combinations and the holt-winter with only a trend component performed best for 10 setups. 3 combinations did not have a distinct best method for all observed results. By selecting the correct forecast methods, we were able to boost the forecast accuracy for the overall system over each single forecast method.","PeriodicalId":247414,"journal":{"name":"Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116736624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Supporting Music Pattern Retrieval and Analysis: An Ontology-Based Approach 支持音乐模式检索和分析:一种基于本体的方法

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405973

C. Achkar, T. Atéchian

引用次数: 1

Using schema.org Annotations for Training and Maintaining Product Matchers 使用schema.org注释培训和维护产品匹配器

Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2020-06-30 DOI: 10.1145/3405962.3405964

R. Peeters, Anna Primpeli, Benedikt Wichtlhuber, Christian Bizer

{"title":"Using schema.org Annotations for Training and Maintaining Product Matchers","authors":"R. Peeters, Anna Primpeli, Benedikt Wichtlhuber, Christian Bizer","doi":"10.1145/3405962.3405964","DOIUrl":"https://doi.org/10.1145/3405962.3405964","url":null,"abstract":"Product matching is a central task within e-commerce applications such as price comparison portals and online market places. State-of-the-art product matching methods achieve F1 scores above 0.90 using deep learning techniques combined with huge amounts of training data (e.g > 100K pairs of offers). Gathering and maintaining such large training corpora is costly, as it implies labeling pairs of offers as matches or non-matches. Acquiring the ability to be good at product matching thus means a major investment for an e-commerce company. This paper shows that the manual labeling of training data for product matching can be replaced by relying exclusively on schema.org annotations gathered from the public Web. We show that using only schema.org data for training, we are able to achieve F1 scores between 0.92 and 0.95 depending on the product category. As new products appear everyday, it is important that matching models can be maintained with justifiable effort. In order to give practical advice on how to maintain matching models, we compare the performance of deep learning and traditional matching models on unseen products and experiment with different fine-tuning and re-training strategies for model maintenance, again using only schema.org annotations as training data. Finally, as using the public Web as distant supervision carries inherent noise, we evaluate deep learning and traditional matching models with regards to their label-noise resistance and show that deep learning is able to deal with the amounts of identifier-noise found in schema.org annotations.","PeriodicalId":247414,"journal":{"name":"Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133759918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10