Rafael Angarita, M. Rukoz, Maude Manouvrier, Yudith Cardinale
{"title":"A knowledge-based approach for self-healing service-oriented applications","authors":"Rafael Angarita, M. Rukoz, Maude Manouvrier, Yudith Cardinale","doi":"10.1145/3012071.3012100","DOIUrl":"https://doi.org/10.1145/3012071.3012100","url":null,"abstract":"In the context of service-oriented applications, the self-healing property provides reliable execution in order to support failures and assist automatic recovery techniques. This paper presents a knowledge-based approach for self-healing Composite Service (CS) applications. A CS is an application composed by a set of services interacting each other and invoked on the Web. Our approach is supported by Service Agents, which are in charge of the CS fault-tolerance execution control, making decisions about the selection of recovery and proactive strategies. Service Agents decisions are based on the information they have about the whole application, about themselves, and about what it is expected and what it is really happening at run-time. Hence, application knowledge for decision making comprises off-line precomputed global and local information, user QoS preferences, and propagated actual run-time information. Our approach is evaluated experimentally using a case study.","PeriodicalId":294250,"journal":{"name":"Proceedings of the 8th International Conference on Management of Digital EcoSystems","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123354579","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":"Towards an automatic analyze and standardization of unstructured data in the context of big and linked data","authors":"Hammou Fadili, C. Jouis","doi":"10.1145/3012071.3012103","DOIUrl":"https://doi.org/10.1145/3012071.3012103","url":null,"abstract":"Unstructured data refers to information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Many studies confirm that around 80--90% of all produced information is in unstructured form. So this kind of content, rich and most importantly too precious, must be integrated and taken into consideration for processing and exploitation: extraction of relevant information from heterogeneous textual data. The goal of the research described here is to present an approach for automating the detection and the extraction of meaning from unstructured Web using its normalized part: Web of data & Linked Open data (LOD) such as RDF WordNet, DBpedia, etc. The process follows a \"cyclical process\" that consists of two phases (a) creating & generating normalized smart data by the experts or automatically, (b) exploiting the created data in (a), as \"validated expert data\", to analyze the Big Data and generate automatically new ones by learning from Linked Open Data (LOD). The approach is based on a range of linguistic and ontological techniques, in the context of Big Data. A software, EC3, is being implemented and at LIP6. EC3 is actually tested on very large corpuses on electronic supports, provided by the labex OBVIL (http://obvil.paris-sorbonne.fr) and the BNF (National Library of France).","PeriodicalId":294250,"journal":{"name":"Proceedings of the 8th International Conference on Management of Digital EcoSystems","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126451316","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":"Collection of HerbalMedicinalProperty relation extracted from texts","authors":"C. Pechsiri, Onuma Moolwat","doi":"10.1145/3012071.3012075","DOIUrl":"https://doi.org/10.1145/3012071.3012075","url":null,"abstract":"This research aims to collect the extracted HerbalMedicinalProperty relations from downloaded herbal-plant documents for creating the herbal-medicinal-property-network based representation. An HerbalMedicinalProperty relation is a semantic relation between one herbal-plant-component concept and several herbal-medicinal-property-concept expressions on texts and vice versa. An herbal-plant-component occurrence is a noun-phrase expression and each herbal-medicinal-property- concept occurrence is an event expression by a verb-phrase of EDU (an Elementary Discourse Unit or a simple sentence). The herbal-medicinal-property-network based representation benefits a recommendation system of solving health-problems on web-boards. The research has two main problems: 1) how to extract HerbalMedicinalProperty relations from the documents, and 2) how to collect the HerbalMedicinalProperty relations for creating the herbal-medicinal-property-network based representation. Therefore, we propose applying a co-occurrence of N-Words (or N-Word-Co) including N-Word-Co size learning on the verb phrase to identify several medicinal-property-concept EDU occurrences over the documents after the linguistic phenomena has been applied to solve the herbal-plant-component concepts. The extracted HerbalMedicinalProperty relations are then collected as a matrix of herbal-plant names, herbal-plant components, and herbal-medicinal properties for creating the herbal-medicinal-property-network based representation. The research results provide the high precision of the HerbalMedicinalProperty-relation extraction from the documents.","PeriodicalId":294250,"journal":{"name":"Proceedings of the 8th International Conference on Management of Digital EcoSystems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126977812","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":"ADSEng: a model-based methodology for autonomous digital service engineering","authors":"Dhaminda B. Abeywickrama, E. Ovaska","doi":"10.1145/3012071.3012072","DOIUrl":"https://doi.org/10.1145/3012071.3012072","url":null,"abstract":"In digital service ecosystems (DSEs), business stakeholders provide the most important driving factors and managing them is a challenge. It requires systems and services to handle uncertainty. Uncertainty in DSEs can be attributed to several factors; for example, dynamic nature and the unknown deployment environment, and change and evolution of requirements. Therefore, there is a need for novel software engineering methods and tools to handle these uncertainties in DSEs. In this regard, valuable lessons can be learnt from the autonomic computing (AC) paradigm and systems that are characterized by self-* properties. This paper proposes a novel, systematic service engineering methodology called ADSEng for ecosystem-based engineering of autonomous digital services. In the current research, the means of handling uncertainty from requirements to architecture and running systems are investigated. To do this, two interrelated research problems are studied: reflexivity that is realized using AC techniques, and evolvability of the ecosystem, supported by automated transformations. Our main contributions are: (i) a modeling methodology from uncertainty specification to runtime models and (ii) quality-driven adaptation patterns embodied by digital services. The paper also presents key lessons learnt from the research experience thus far.","PeriodicalId":294250,"journal":{"name":"Proceedings of the 8th International Conference on Management of Digital EcoSystems","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127072511","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}
Sarah Zenasni, E. Kergosien, M. Roche, M. Teisseire
{"title":"Extracting new spatial entities and relations from short messages","authors":"Sarah Zenasni, E. Kergosien, M. Roche, M. Teisseire","doi":"10.1145/3012071.3012079","DOIUrl":"https://doi.org/10.1145/3012071.3012079","url":null,"abstract":"In the past few years, texts have become an important spatial data resource, in addition to maps, satellite images and GPS. Electronic written texts used in mediated interactions, especially short messages (SMS, tweets, etc.), have triggered the emergence of new ways of writing. Extracting information from such short messages, which represent a rich source of information and opinion, is highly important due to the new and challenging text style. Short messages are, however, difficult to analyze because of their brief, unstructured and informal nature. The work presented in this paper is aimed at extracting spatial information from two authentic corpora of SMS and tweets in French in order to take advantage of the vast amount of geographical knowledge expressed in diverse natural language texts. We propose a process in which, firstly, we extract new spatial entities (e.g. Monpelier, Montpel are associated with the place name Montpellier). Secondly, we identify new spatial relations that precede these spatial entities (e.g. sur, par, etc.). Finally, we propose a general pattern for discovering spatial relations (e.g. SR+ Preposition). The task is very challenging and complex due to the specificity of short messages language, which is based on weakly standardized modes of writing (lexical creation, massive use of abbreviations, textual variants, etc.). The experiments that were carried out on the two corpora 88milSMS and Tweets highlight the efficiency of our proposed strategy for identifying new kinds of spatial entities and relations.","PeriodicalId":294250,"journal":{"name":"Proceedings of the 8th International Conference on Management of Digital EcoSystems","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134326070","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}
Melkamu Beyene, P. Portier, Solomon Atnafu, S. Calabretto
{"title":"Dataset linking in a multilingual linked open data context","authors":"Melkamu Beyene, P. Portier, Solomon Atnafu, S. Calabretto","doi":"10.1145/3012071.3012090","DOIUrl":"https://doi.org/10.1145/3012071.3012090","url":null,"abstract":"Although, the syntactical and structural heterogeneities among inter-language linked open data (LOD) data sources bring many challenges, entity co-reference resolution in a multilingual linked open data (MLOD) setting is not well studied. In this research, a three phase approach is proposed. First, statistical relational learning (SRL) with factorization of three way tensor is used to compute structural similarity between entities. Second, textual data from the Web of documents is associated in order to increase our knowledge of entities. Through a latent Dirichlet allocation (LDA), entities' textual data is projected into a cross-lingual topic space. This cross-lingual topic space is used to find textual similarities between entities. Third, a belief aggregation strategy is used to combine the structural and textual similarity results into a global similarity score. We have shown by experiments that our algorithm out-performs state of the art approaches based on tensor decomposition for the task of entity co-reference resolution in a MLOD setting.","PeriodicalId":294250,"journal":{"name":"Proceedings of the 8th International Conference on Management of Digital EcoSystems","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124660538","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":"A novel taxonomy of opportunities and risks in massively multiplayer online role playing games","authors":"Benjamin Sanders, Jims Marchang","doi":"10.1145/3012071.3012094","DOIUrl":"https://doi.org/10.1145/3012071.3012094","url":null,"abstract":"The explosion and rapid embrace of Massively Multiplayer Online Role Playing Games (MMORPG) has provided players with unique, fully immersive three dimensional environments in which they can express themselves in a myriad of ways. Players can develop unique skill sets, share knowledge, explore and experiment with various identities and network with like-minded individuals. Evidence suggests however, that these opportunities are accompanied with a number of risks including addiction, desensitisation and threats to the privacy of personal information. This paper presents a novel Taxonomy of opportunities and risks in the specific context of Massively Multiplayer Online Role Playing Games.","PeriodicalId":294250,"journal":{"name":"Proceedings of the 8th International Conference on Management of Digital EcoSystems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130839570","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":"Socialization and language self-adaptation in digital ecosystems","authors":"Arianna D’ulizia, F. Ferri, P. Grifoni","doi":"10.1145/3012071.3012083","DOIUrl":"https://doi.org/10.1145/3012071.3012083","url":null,"abstract":"Socialization is an essential process for building any society in natural ecosystems. In recent years, effective socialization processes have been investigated also within digital ecosystems also in the perspective of common and self-adaptive languages that allowboth \"biotic\" (human) and \"abiotic\" (physical) entities to socialize. In this paper, we propose a socialization and language self-adaptation method for enabling effective communicative interaction amongdigital entities acting in a digital ecosystem. The proposed method relies on an adaptableand extensible grammatical formalism, named Digital Ecosystem Grammar (DEG), which allows digital entities, represented as a multi-agent system, to interpret the messages expressed by other entities by using interaction, learning and evolution actions.Moreover, a grammar learning algorithm is applied to provide the self-adaptation mechanisms that allow the digital environment to adapt the interaction languageaccording to new incoming messages.","PeriodicalId":294250,"journal":{"name":"Proceedings of the 8th International Conference on Management of Digital EcoSystems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128886273","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}
Edgard Costa Oliveira, Edison Ishikawa, T. H. Granja, M. V. D. A. Nunes, Lucas Hiroshi Hironouchi, Cristiano Costa de Souza, Rafael Batista Menegassi, Luciano Gois
{"title":"Designing an ontology-based Zika virus news authoring environment for the semantic web","authors":"Edgard Costa Oliveira, Edison Ishikawa, T. H. Granja, M. V. D. A. Nunes, Lucas Hiroshi Hironouchi, Cristiano Costa de Souza, Rafael Batista Menegassi, Luciano Gois","doi":"10.1145/3012071.3012093","DOIUrl":"https://doi.org/10.1145/3012071.3012093","url":null,"abstract":"This paper describes the experience of researching and teaching the conceptual and practical basis for the specification, modelling and design of an ontology-based news authoring environment for the Semantic Web, that takes into account the construction and use of an ontology of the Zika disease. Some CMSs are being adapted in order to receive semantic features, such as automatic generations of keywords, semantic annotation and tagging, content reviewing etc. We present here the infrastructure designed to foster research on semantic CMSs as well as semantic web technologies that can be integrated into an ontology-based news authoring environment.","PeriodicalId":294250,"journal":{"name":"Proceedings of the 8th International Conference on Management of Digital EcoSystems","volume":"408 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134127669","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}
Francesco Di Mauro, Paolo Pasteris, M. Sapino, K. Candan, G. Dino, P. Rossetti
{"title":"CrowdSourced semantic enrichment for participatory e-Government","authors":"Francesco Di Mauro, Paolo Pasteris, M. Sapino, K. Candan, G. Dino, P. Rossetti","doi":"10.1145/3012071.3012102","DOIUrl":"https://doi.org/10.1145/3012071.3012102","url":null,"abstract":"When making decisions impacting public utility and encouraging and/or enforcing behavioral rules, public administrators need to rely on data and knowledge supporting their choices, which can be used to better inform those citizens who will be affected by such decisions. Many open data repositories exist and can be accessed and used by both decision makers and citizens. Similarly, semantic tagging is now commonly used as a way to allow users provide their own knowledge to be associated to data. In this paper, we present a novel participatory system which allows traditional databases and semantic tagging modules coexist in the same knowledge base, and provides the users with query enrichment functionalities to enable ontology-based query expansion. We describe CroSSE, our CrowdSourced Semantic Enrichment query system architecture, define the enrichment specification language, and discuss a use case in which the proposed technology is being applied in a participatory e-government setting. The use case is in the context of our SmartGround EU funded project, in which a relational database platform is designed to collect data of interest concerning secondary raw materials from mines as well as municipality waste. CroSSE semantic enrichment architecture interacts with this platform to expand queries and results on the basis of users' domain knowledge.","PeriodicalId":294250,"journal":{"name":"Proceedings of the 8th International Conference on Management of Digital EcoSystems","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127264054","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}