Semantic Computing is being used in a wide variety of domains to develop intelligent applications. With the increasing availability of different kinds of data on the web, providing better emergency management in case of natural disasters and humanitarian crises is much needed. This paper presents the use of semantic technologies to build a web application called Safety Check. The aim of this work is to implement a knowledge intensive application that identifies those people that may have been affected due to natural disasters or man-made disasters at any geographical location and notify them with safety instructions. This involves extraction of data from various sources for emergency alerts, weather alerts, and contacts data. The extracted data is integrated using a semantic data model and transformed into semantic data. Semantic reasoning was done through rules and queries.
{"title":"Safety check: a semantic web application for emergency management","authors":"Yogesh Pandey, S. Bansal","doi":"10.1145/3066911.3066917","DOIUrl":"https://doi.org/10.1145/3066911.3066917","url":null,"abstract":"Semantic Computing is being used in a wide variety of domains to develop intelligent applications. With the increasing availability of different kinds of data on the web, providing better emergency management in case of natural disasters and humanitarian crises is much needed. This paper presents the use of semantic technologies to build a web application called Safety Check. The aim of this work is to implement a knowledge intensive application that identifies those people that may have been affected due to natural disasters or man-made disasters at any geographical location and notify them with safety instructions. This involves extraction of data from various sources for emergency alerts, weather alerts, and contacts data. The extracted data is integrated using a semantic data model and transformed into semantic data. Semantic reasoning was done through rules and queries.","PeriodicalId":210506,"journal":{"name":"Proceedings of the International Workshop on Semantic Big Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133225250","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}
Information from the web is a key resource exploited in the domain of competitive intelligence. These sources represent important volumes of information to process everyday. As the amount of information available grows rapidly, this process becomes overwhelming for experts. To leverage this challenge, this paper presents a novel approach to process such sources and extract only the most valuable pieces of information. The approach is based on an unsupervised and adaptive ontology-learning process. The resulting ontology is used to enhance the performance of a focused crawler. The combination of Big Data and Semantic Web technologies allows to classify information precisely according to domain knowledge, while maintaining optimal performances. The approach and its implementation are described, and an presents the feasibility and performance of the approach.
{"title":"Ontology-based approach for unsupervised and adaptive focused crawling","authors":"Thomas Hassan, C. Cruz, Aurélie Bertaux","doi":"10.1145/3066911.3066912","DOIUrl":"https://doi.org/10.1145/3066911.3066912","url":null,"abstract":"Information from the web is a key resource exploited in the domain of competitive intelligence. These sources represent important volumes of information to process everyday. As the amount of information available grows rapidly, this process becomes overwhelming for experts. To leverage this challenge, this paper presents a novel approach to process such sources and extract only the most valuable pieces of information. The approach is based on an unsupervised and adaptive ontology-learning process. The resulting ontology is used to enhance the performance of a focused crawler. The combination of Big Data and Semantic Web technologies allows to classify information precisely according to domain knowledge, while maintaining optimal performances. The approach and its implementation are described, and an presents the feasibility and performance of the approach.","PeriodicalId":210506,"journal":{"name":"Proceedings of the International Workshop on Semantic Big Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123489206","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}
We propose a supervised algorithm for generating type embeddings in the same semantic vector space as a given set of entity embeddings. The algorithm is agnostic to the derivation of the underlying entity embeddings. It does not require any manual feature engineering, generalizes well to hundreds of types and achieves near-linear scaling on Big Graphs containing many millions of triples and instances by virtue of an incremental execution. We demonstrate the utility of the embeddings on a type recommendation task, outperforming a non-parametric feature-agnostic baseline while achieving 15× speedup and near-constant memory usage on a full partition of DBpedia. Using state-of-the-art visualization, we illustrate the agreement of our extensionally derived DBpedia type embeddings with the manually curated domain ontology. Finally, we use the embeddings to probabilistically cluster about 4 million DBpedia instances into 415 types in the DBpedia ontology.
{"title":"Supervised typing of big graphs using semantic embeddings","authors":"M. Kejriwal, Pedro A. Szekely","doi":"10.1145/3066911.3066918","DOIUrl":"https://doi.org/10.1145/3066911.3066918","url":null,"abstract":"We propose a supervised algorithm for generating type embeddings in the same semantic vector space as a given set of entity embeddings. The algorithm is agnostic to the derivation of the underlying entity embeddings. It does not require any manual feature engineering, generalizes well to hundreds of types and achieves near-linear scaling on Big Graphs containing many millions of triples and instances by virtue of an incremental execution. We demonstrate the utility of the embeddings on a type recommendation task, outperforming a non-parametric feature-agnostic baseline while achieving 15× speedup and near-constant memory usage on a full partition of DBpedia. Using state-of-the-art visualization, we illustrate the agreement of our extensionally derived DBpedia type embeddings with the manually curated domain ontology. Finally, we use the embeddings to probabilistically cluster about 4 million DBpedia instances into 415 types in the DBpedia ontology.","PeriodicalId":210506,"journal":{"name":"Proceedings of the International Workshop on Semantic Big Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129392171","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}
The current World-Wide Web enables an easy, instant access to a vast amount of online information. However, the content in the Web is typically for human consumption, and is not tailored for machine processing. The Semantic Web is hence intended to establish a machine-understandable Web, and is currently also used in many other domains and not only in the Web. The World Wide Web Consortium (W3C) has developed a number of standards around this vision. Among them is the Resource Description Framework (RDF), which is used as the data model of the Semantic Web. The W3C has also defined SPARQL as the RDF query language, RIF as the rule language, and the ontology languages RDFS and OWL to describe schemas of RDF. The usage of common ontologies increases interoperability between heterogeneous data sets, and the proprietary ontologies with the additional abstraction layer facilitate the integration of these data sets. Therefore, we can argue that the Semantic Web is ideally designed to work in heterogeneous Big Data environments. We define Semantic Big Data as the intersection of Semantic Web data and Big Data. There are masses of Semantic Web data freely available to the public - thanks to the efforts of the linked data initiative. According to http://stats.lod2.eu/ the current freely available Semantic Web data is approximately 90 billion triples in over 3,300 datasets, many of which are accessible via SPARQL query servers called SPARQL endpoints. Everyone can submit SPARQL queries to SPARQL endpoints via a standardized protocol, where the queries are processed on the datasets of the SPARQL endpoints and the query results are sent back in a standardized format. Hence, not only Semantic Big Data is freely available, but also distributed execution environments for Semantic Big Data are freely accessible. This makes the Semantic Web an ideal playground for Big Data research. The goal of this workshop is to bring together academic researchers and industry practitioners to address the challenges and report and exchange the research findings in Semantic Big Data, including new approaches, techniques and applications, make substantial theoretical and empirical contributions to, and significantly advance the state of the art of Semantic Big Data.
{"title":"Proceedings of The International Workshop on Semantic Big Data","authors":"Sven Groppe, L. Gruenwald","doi":"10.1145/3391274","DOIUrl":"https://doi.org/10.1145/3391274","url":null,"abstract":"The current World-Wide Web enables an easy, instant access to a vast amount of online information. However, the content in the Web is typically for human consumption, and is not tailored for machine processing. The Semantic Web is hence intended to establish a machine-understandable Web, and is currently also used in many other domains and not only in the Web. The World Wide Web Consortium (W3C) has developed a number of standards around this vision. Among them is the Resource Description Framework (RDF), which is used as the data model of the Semantic Web. The W3C has also defined SPARQL as the RDF query language, RIF as the rule language, and the ontology languages RDFS and OWL to describe schemas of RDF. The usage of common ontologies increases interoperability between heterogeneous data sets, and the proprietary ontologies with the additional abstraction layer facilitate the integration of these data sets. Therefore, we can argue that the Semantic Web is ideally designed to work in heterogeneous Big Data environments. \u0000 \u0000We define Semantic Big Data as the intersection of Semantic Web data and Big Data. There are masses of Semantic Web data freely available to the public - thanks to the efforts of the linked data initiative. According to http://stats.lod2.eu/ the current freely available Semantic Web data is approximately 90 billion triples in over 3,300 datasets, many of which are accessible via SPARQL query servers called SPARQL endpoints. Everyone can submit SPARQL queries to SPARQL endpoints via a standardized protocol, where the queries are processed on the datasets of the SPARQL endpoints and the query results are sent back in a standardized format. Hence, not only Semantic Big Data is freely available, but also distributed execution environments for Semantic Big Data are freely accessible. This makes the Semantic Web an ideal playground for Big Data research. \u0000 \u0000The goal of this workshop is to bring together academic researchers and industry practitioners to address the challenges and report and exchange the research findings in Semantic Big Data, including new approaches, techniques and applications, make substantial theoretical and empirical contributions to, and significantly advance the state of the art of Semantic Big Data.","PeriodicalId":210506,"journal":{"name":"Proceedings of the International Workshop on Semantic Big Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132463125","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}
{"title":"Proceedings of The International Workshop on Semantic Big Data","authors":"","doi":"10.1145/3066911","DOIUrl":"https://doi.org/10.1145/3066911","url":null,"abstract":"","PeriodicalId":210506,"journal":{"name":"Proceedings of the International Workshop on Semantic Big Data","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131355025","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}
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