Julien Aimonier-Davat, H. Skaf-Molli, P. Molli, Arnaud Grall, Thomas Minier
Getting complete results when processing aggregate queries on public SPARQL endpoints is challenging, mainly due to the application of quotas. Although Web preemption supports processing of aggregate queries online, on preemptable SPARQL servers, data transfer is still very large when processing count-distinct aggregate queries. In this paper, it is shown that count-distinct aggregate queries can be approximated with low data transfer by extending the partial aggregation operator with HyperLogLog++ sketches. Experimental results demonstrate that the proposed approach outperforms existing approaches by orders of magnitude in terms of the amount of data transferred.
{"title":"Online approximative SPARQL query processing for COUNT-DISTINCT queries with web preemption","authors":"Julien Aimonier-Davat, H. Skaf-Molli, P. Molli, Arnaud Grall, Thomas Minier","doi":"10.3233/sw-222842","DOIUrl":"https://doi.org/10.3233/sw-222842","url":null,"abstract":"Getting complete results when processing aggregate queries on public SPARQL endpoints is challenging, mainly due to the application of quotas. Although Web preemption supports processing of aggregate queries online, on preemptable SPARQL servers, data transfer is still very large when processing count-distinct aggregate queries. In this paper, it is shown that count-distinct aggregate queries can be approximated with low data transfer by extending the partial aggregation operator with HyperLogLog++ sketches. Experimental results demonstrate that the proposed approach outperforms existing approaches by orders of magnitude in terms of the amount of data transferred.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"98 1","pages":"735-755"},"PeriodicalIF":3.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86286155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Patricia Martín-Chozas, Karen Vázquez-Flores, Pablo Calleja, Elena Montiel-Ponsoda, V. Rodríguez-Doncel
Domain-specific terminologies play a central role in many language technology solutions. Substantial manual effort is still involved in the creation of such resources, and many of them are published in proprietary formats that cannot be easily reused in other applications. Automatic term extraction tools help alleviate this cumbersome task. However, their results are usually in the form of plain lists of terms or as unstructured data with limited linguistic information. Initiatives such as the Linguistic Linked Open Data cloud (LLOD) foster the publication of language resources in open structured formats, specifically RDF, and their linking to other resources on the Web of Data. In order to leverage the wealth of linguistic data in the LLOD and speed up the creation of linked terminological resources, we propose TermitUp, a service that generates enriched domain specific terminologies directly from corpora, and publishes them in open and structured formats. TermitUp is composed of five modules performing terminology extraction, terminology post-processing, terminology enrichment, term relation validation and RDF publication. As part of the pipeline implemented by this service, existing resources in the LLOD are linked with the resulting terminologies, contributing in this way to the population of the LLOD cloud. TermitUp has been used in the framework of European projects tackling different fields, such as the legal domain, with promising results. Different alternatives on how to model enriched terminologies are considered and good practices illustrated with examples are proposed.
{"title":"TermitUp: Generation and enrichment of linked terminologies","authors":"Patricia Martín-Chozas, Karen Vázquez-Flores, Pablo Calleja, Elena Montiel-Ponsoda, V. Rodríguez-Doncel","doi":"10.3233/sw-222885","DOIUrl":"https://doi.org/10.3233/sw-222885","url":null,"abstract":"Domain-specific terminologies play a central role in many language technology solutions. Substantial manual effort is still involved in the creation of such resources, and many of them are published in proprietary formats that cannot be easily reused in other applications. Automatic term extraction tools help alleviate this cumbersome task. However, their results are usually in the form of plain lists of terms or as unstructured data with limited linguistic information. Initiatives such as the Linguistic Linked Open Data cloud (LLOD) foster the publication of language resources in open structured formats, specifically RDF, and their linking to other resources on the Web of Data. In order to leverage the wealth of linguistic data in the LLOD and speed up the creation of linked terminological resources, we propose TermitUp, a service that generates enriched domain specific terminologies directly from corpora, and publishes them in open and structured formats. TermitUp is composed of five modules performing terminology extraction, terminology post-processing, terminology enrichment, term relation validation and RDF publication. As part of the pipeline implemented by this service, existing resources in the LLOD are linked with the resulting terminologies, contributing in this way to the population of the LLOD cloud. TermitUp has been used in the framework of European projects tackling different fields, such as the legal domain, with promising results. Different alternatives on how to model enriched terminologies are considered and good practices illustrated with examples are proposed.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"6 1","pages":"967-986"},"PeriodicalIF":3.0,"publicationDate":"2022-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72819715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reusing ontologies for new purposes, or adapting them to new use-cases, is frequently difficult. In our experiences, we have found this to be the case for several reasons: (i) differing representational granularity in ontologies and in use-cases, (ii) lacking conceptual clarity in potentially reusable ontologies, (iii) lack and difficulty of adherence to good modeling principles, and (iv) a lack of reuse emphasis and process support available in ontology engineering tooling. In order to address these concerns, we have developed the Modular Ontology Modeling (MOMo) methodology, and its supporting tooling infrastructure, CoModIDE (the Comprehensive Modular Ontology IDE – “commodity”). MOMo builds on the established eXtreme Design methodology, and like it emphasizes modular development and design pattern reuse; but crucially adds the extensive use of graphical schema diagrams, and tooling that support them, as vehicles for knowledge elicitation from experts. In this paper, we present the MOMo workflow in detail, and describe several useful resources for executing it. In particular, we provide a thorough and rigorous evaluation of CoModIDE in its role of supporting the MOMo methodology’s graphical modeling paradigm. We find that CoModIDE significantly improves approachability of such a paradigm, and that it displays a high usability.
{"title":"Modular ontology modeling","authors":"C. Shimizu, K. Hammar, P. Hitzler","doi":"10.3233/sw-222886","DOIUrl":"https://doi.org/10.3233/sw-222886","url":null,"abstract":"Reusing ontologies for new purposes, or adapting them to new use-cases, is frequently difficult. In our experiences, we have found this to be the case for several reasons: (i) differing representational granularity in ontologies and in use-cases, (ii) lacking conceptual clarity in potentially reusable ontologies, (iii) lack and difficulty of adherence to good modeling principles, and (iv) a lack of reuse emphasis and process support available in ontology engineering tooling. In order to address these concerns, we have developed the Modular Ontology Modeling (MOMo) methodology, and its supporting tooling infrastructure, CoModIDE (the Comprehensive Modular Ontology IDE – “commodity”). MOMo builds on the established eXtreme Design methodology, and like it emphasizes modular development and design pattern reuse; but crucially adds the extensive use of graphical schema diagrams, and tooling that support them, as vehicles for knowledge elicitation from experts. In this paper, we present the MOMo workflow in detail, and describe several useful resources for executing it. In particular, we provide a thorough and rigorous evaluation of CoModIDE in its role of supporting the MOMo methodology’s graphical modeling paradigm. We find that CoModIDE significantly improves approachability of such a paradigm, and that it displays a high usability.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"24 1","pages":"459-489"},"PeriodicalIF":3.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75414993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sebastian Höffner, R. Porzel, Maria M. Hedblom, M. Pomarlan, Vanja Sophie Cangalovic, Johannes Pfau, J. Bateman, R. Malaka
Going from natural language directions to fully specified executable plans for household robots involves a challenging variety of reasoning steps. In this paper, a processing pipeline to tackle these steps for natural language directions is proposed and implemented. It uses the ontological Socio-physical Model of Activities (SOMA) as a common interface between its components. The pipeline includes a natural language parser and a module for natural language grounding. Several reasoning steps formulate simulation plans, in which robot actions are guided by data gathered using human computation. As a last step, the pipeline simulates the given natural language direction inside a virtual environment. The major advantage of employing an overarching ontological framework is that its asserted facts can be stored alongside the semantics of directions, contextual knowledge, and annotated activity models in one central knowledge base. This allows for a unified and efficient knowledge retrieval across all pipeline components, providing flexibility and reasoning capabilities as symbolic knowledge is combined with annotated sub-symbolic models.
{"title":"Deep understanding of everyday activity commands for household robots","authors":"Sebastian Höffner, R. Porzel, Maria M. Hedblom, M. Pomarlan, Vanja Sophie Cangalovic, Johannes Pfau, J. Bateman, R. Malaka","doi":"10.3233/sw-222973","DOIUrl":"https://doi.org/10.3233/sw-222973","url":null,"abstract":"Going from natural language directions to fully specified executable plans for household robots involves a challenging variety of reasoning steps. In this paper, a processing pipeline to tackle these steps for natural language directions is proposed and implemented. It uses the ontological Socio-physical Model of Activities (SOMA) as a common interface between its components. The pipeline includes a natural language parser and a module for natural language grounding. Several reasoning steps formulate simulation plans, in which robot actions are guided by data gathered using human computation. As a last step, the pipeline simulates the given natural language direction inside a virtual environment. The major advantage of employing an overarching ontological framework is that its asserted facts can be stored alongside the semantics of directions, contextual knowledge, and annotated activity models in one central knowledge base. This allows for a unified and efficient knowledge retrieval across all pipeline components, providing flexibility and reasoning capabilities as symbolic knowledge is combined with annotated sub-symbolic models.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"48 1","pages":"895-909"},"PeriodicalIF":3.0,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76785106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Armaselu, E. Apostol, Anas Fahad Khan, Chaya Liebeskind, Barbara McGillivray, Ciprian-Octavian Truică, A. Utka, G. Oleškevičienė, M. Erp
This paper presents an overview of the LL(O)D and NLP methods, tools and data for detecting and representing semantic change, with its main application in humanities research. The paper’s aim is to provide the starting point for the construction of a workflow and set of multilingual diachronic ontologies within the humanities use case of the COST Action Nexus Linguarum, European network for Web-centred linguistic data science, CA18209. The survey focuses on the essential aspects needed to understand the current trends and to build applications in this area of study.
{"title":"LL(O)D and NLP perspectives on semantic change for humanities research","authors":"F. Armaselu, E. Apostol, Anas Fahad Khan, Chaya Liebeskind, Barbara McGillivray, Ciprian-Octavian Truică, A. Utka, G. Oleškevičienė, M. Erp","doi":"10.3233/sw-222848","DOIUrl":"https://doi.org/10.3233/sw-222848","url":null,"abstract":"This paper presents an overview of the LL(O)D and NLP methods, tools and data for detecting and representing semantic change, with its main application in humanities research. The paper’s aim is to provide the starting point for the construction of a workflow and set of multilingual diachronic ontologies within the humanities use case of the COST Action Nexus Linguarum, European network for Web-centred linguistic data science, CA18209. The survey focuses on the essential aspects needed to understand the current trends and to build applications in this area of study.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"9 1","pages":"1051-1080"},"PeriodicalIF":3.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86198143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mehwish Alam, D. Buscaldi, Michael Cochez, Francesco Osborne, Diego Reforgiato Recupero, H. Sack
Mehwish Alam a,b,*, Davide Buscaldi c, Michael Cochez d, Francesco Osborne e, Diego Reforgiato Recupero f and Harald Sack a,b a FIZ-Karlsruhe, Leibniz Institute for Information Infrastructure, Karlsruhe, Germany b Karlsruhe Institute of Technology, Karlsruhe, Germany E-mails: mehwish.alam@kit.edu, harald.sack@kit.edu c LIPN, Université Sorbonne Paris Nord, France E-mail: buscaldi@lipn.fr d University of Amsterdam, the Netherlands E-mail: m.cochez@vu.nl e KMi, The Open University, UK E-mail: francesco.osborne@open.ac.uk f Department of Mathematics and Computer Science, University of Cagliari, Italy E-mail: diego.reforgiato@unica.it
Mehwish Alam a,b，*， Davide Buscaldi c, Michael Cochez d, Francesco Osborne e, Diego Reforgiato Recupero f和Harald Sack a,b a fizz -Karlsruhe，德国卡尔斯鲁厄莱布尼茨信息基础设施研究所，德国卡尔斯鲁厄卡尔斯鲁厄理工学院，德国卡尔斯鲁厄e -mail: mehwish.alam@kit.edu, harald.sack@kit.edu c LIPN，法国巴黎北部索邦大学e - buscaldi@lipn.fr d荷兰阿姆斯特丹大学e - m.cochez@vu.nl e - KMi，英国开放大学e -mail:francesco.osborne@open.ac.uk f意大利卡利亚里大学数学与计算机科学系E-mail: diego.reforgiato@unica.it
{"title":"Editorial of the Special Issue on Deep Learning and Knowledge Graphs","authors":"Mehwish Alam, D. Buscaldi, Michael Cochez, Francesco Osborne, Diego Reforgiato Recupero, H. Sack","doi":"10.3233/sw-223099","DOIUrl":"https://doi.org/10.3233/sw-223099","url":null,"abstract":"Mehwish Alam a,b,*, Davide Buscaldi c, Michael Cochez d, Francesco Osborne e, Diego Reforgiato Recupero f and Harald Sack a,b a FIZ-Karlsruhe, Leibniz Institute for Information Infrastructure, Karlsruhe, Germany b Karlsruhe Institute of Technology, Karlsruhe, Germany E-mails: mehwish.alam@kit.edu, harald.sack@kit.edu c LIPN, Université Sorbonne Paris Nord, France E-mail: buscaldi@lipn.fr d University of Amsterdam, the Netherlands E-mail: m.cochez@vu.nl e KMi, The Open University, UK E-mail: francesco.osborne@open.ac.uk f Department of Mathematics and Computer Science, University of Cagliari, Italy E-mail: diego.reforgiato@unica.it","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"42 1","pages":"293-297"},"PeriodicalIF":3.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90353599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the increasing popularity of knowledge graph (KG), many applications such as sentiment analysis, trend prediction, and question answering use KG for better performance. Despite the obvious usefulness of commonsense and factual information in the KGs, to the best of our knowledge, KGs have been rarely integrated into the task of answer selection in community question answering (CQA). In this paper, we propose a novel answer selection method in CQA by using the knowledge embedded in KGs. We also learn a latent-variable model for learning the representations of the question and answer, jointly optimizing generative and discriminative objectives. It also uses the question category for producing context-aware representations for questions and answers. Moreover, the model uses variational autoencoders (VAE) in a multi-task learning process with a classifier to produce class-specific representations for answers. The experimental results on three widely used datasets demonstrate that our proposed method is effective and outperforms the existing baselines significantly.
{"title":"Answer selection in community question answering exploiting knowledge graph and context information","authors":"Golshan Assadat Afzali Boroujeni, Heshaam Faili, Yadollah Yaghoobzadeh","doi":"10.3233/sw-222970","DOIUrl":"https://doi.org/10.3233/sw-222970","url":null,"abstract":"With the increasing popularity of knowledge graph (KG), many applications such as sentiment analysis, trend prediction, and question answering use KG for better performance. Despite the obvious usefulness of commonsense and factual information in the KGs, to the best of our knowledge, KGs have been rarely integrated into the task of answer selection in community question answering (CQA). In this paper, we propose a novel answer selection method in CQA by using the knowledge embedded in KGs. We also learn a latent-variable model for learning the representations of the question and answer, jointly optimizing generative and discriminative objectives. It also uses the question category for producing context-aware representations for questions and answers. Moreover, the model uses variational autoencoders (VAE) in a multi-task learning process with a classifier to produce class-specific representations for answers. The experimental results on three widely used datasets demonstrate that our proposed method is effective and outperforms the existing baselines significantly.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"4 1","pages":"339-356"},"PeriodicalIF":3.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75475447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tabular data plays an essential role in many data analytics and machine learning tasks. Typically, tabular data does not possess any machine-readable semantics. In this context, semantic table interpretation is crucial for making data analytics workflows more robust and explainable. This article proposes Tab2KG – a novel method that targets at the interpretation of tables with previously unseen data and automatically infers their semantics to transform them into semantic data graphs. We introduce original lightweight semantic profiles that enrich a domain ontology’s concepts and relations and represent domain and table characteristics. We propose a one-shot learning approach that relies on these profiles to map a tabular dataset containing previously unseen instances to a domain ontology. In contrast to the existing semantic table interpretation approaches, Tab2KG relies on the semantic profiles only and does not require any instance lookup. This property makes Tab2KG particularly suitable in the data analytics context, in which data tables typically contain new instances. Our experimental evaluation on several real-world datasets from different application domains demonstrates that Tab2KG outperforms state-of-the-art semantic table interpretation baselines.
{"title":"Tab2KG: Semantic table interpretation with lightweight semantic profiles","authors":"Simon Gottschalk, Elena Demidova","doi":"10.3233/SW-222993","DOIUrl":"https://doi.org/10.3233/SW-222993","url":null,"abstract":"Tabular data plays an essential role in many data analytics and machine learning tasks. Typically, tabular data does not possess any machine-readable semantics. In this context, semantic table interpretation is crucial for making data analytics workflows more robust and explainable. This article proposes Tab2KG – a novel method that targets at the interpretation of tables with previously unseen data and automatically infers their semantics to transform them into semantic data graphs. We introduce original lightweight semantic profiles that enrich a domain ontology’s concepts and relations and represent domain and table characteristics. We propose a one-shot learning approach that relies on these profiles to map a tabular dataset containing previously unseen instances to a domain ontology. In contrast to the existing semantic table interpretation approaches, Tab2KG relies on the semantic profiles only and does not require any instance lookup. This property makes Tab2KG particularly suitable in the data analytics context, in which data tables typically contain new instances. Our experimental evaluation on several real-world datasets from different application domains demonstrates that Tab2KG outperforms state-of-the-art semantic table interpretation baselines.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"115 1","pages":"571-597"},"PeriodicalIF":3.0,"publicationDate":"2022-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77596896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Since the beginning of the release of open data by many countries, different methodologies for publishing linked data have been proposed. However, they seem not to be adopted by early studies exploring linked data for different reasons. In this work, we conducted a systematic mapping in the literature to synthesize the different approaches around the following topics: common steps, associated tools and practices, quality assessment validations, and evaluation of the methodology. The findings show a core set of activities, based on the linked data principles, but with additional critical steps for practical use in scale. Furthermore, although a fair amount of quality issues are reported in the literature, very few of these methodologies embed validation steps in their process. We describe an integrated overview of the different activities and how they can be executed with appropriate tools. We also present research challenges that need to be addressed in future works in this area.
{"title":"Methodologies for publishing linked open government data on the Web: A systematic mapping and a unified process model","authors":"B. Penteado, J. Maldonado, Seiji Isotani","doi":"10.3233/sw-222896","DOIUrl":"https://doi.org/10.3233/sw-222896","url":null,"abstract":"Since the beginning of the release of open data by many countries, different methodologies for publishing linked data have been proposed. However, they seem not to be adopted by early studies exploring linked data for different reasons. In this work, we conducted a systematic mapping in the literature to synthesize the different approaches around the following topics: common steps, associated tools and practices, quality assessment validations, and evaluation of the methodology. The findings show a core set of activities, based on the linked data principles, but with additional critical steps for practical use in scale. Furthermore, although a fair amount of quality issues are reported in the literature, very few of these methodologies embed validation steps in their process. We describe an integrated overview of the different activities and how they can be executed with appropriate tools. We also present research challenges that need to be addressed in future works in this area.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"125 1","pages":"585-610"},"PeriodicalIF":3.0,"publicationDate":"2022-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73770897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There is a growing need to perform real-time analytics on dynamic graphs in order to deliver the values of big data to users. An important problem from such applications is continuously identifying and monitoring critical patterns when fine-grained updates at a high velocity occur on the graphs. A lot of efforts have been made to develop practical solutions for these problems. Despite the efforts, existing algorithms showed limited running time and scalability in dealing with large and/or many graphs. In this paper, we study the problem of continuous multi-query optimization for subgraph matching over dynamic graph data. (1) We propose annotated query graph, which is obtained by merging the multi-queries into one. (2) Based on the annotated query, we employ a concise auxiliary data structure to represent partial solutions in a compact form. (3) In addition, we propose an efficient maintenance strategy to detect the affected queries for each update and report corresponding matches in one pass. (4) Extensive experiments over real-life and synthetic datasets verify the effectiveness and efficiency of our approach and confirm a two orders of magnitude improvement of the proposed solution.
{"title":"Continuous multi-query optimization for subgraph matching over dynamic graphs","authors":"Xi Wang, Qianzhen Zhang, Deke Guo, Xiang Zhao","doi":"10.3233/sw-212864","DOIUrl":"https://doi.org/10.3233/sw-212864","url":null,"abstract":"There is a growing need to perform real-time analytics on dynamic graphs in order to deliver the values of big data to users. An important problem from such applications is continuously identifying and monitoring critical patterns when fine-grained updates at a high velocity occur on the graphs. A lot of efforts have been made to develop practical solutions for these problems. Despite the efforts, existing algorithms showed limited running time and scalability in dealing with large and/or many graphs. In this paper, we study the problem of continuous multi-query optimization for subgraph matching over dynamic graph data. (1) We propose annotated query graph, which is obtained by merging the multi-queries into one. (2) Based on the annotated query, we employ a concise auxiliary data structure to represent partial solutions in a compact form. (3) In addition, we propose an efficient maintenance strategy to detect the affected queries for each update and report corresponding matches in one pass. (4) Extensive experiments over real-life and synthetic datasets verify the effectiveness and efficiency of our approach and confirm a two orders of magnitude improvement of the proposed solution.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":"41 1","pages":"601-622"},"PeriodicalIF":3.0,"publicationDate":"2022-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78534916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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