PARIS: A Peer-to-Peer Architecture for Large-Scale Semantic Data Integration

Comito, Carmela; Patarin, Simon; Talia, Domenico

doi:10.1007/978-3-540-71661-7_15

Cited by 8 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, this study used the creation of a cognitive script to extract the semantic meanings of the findings given in natural language text for creating a graph-based repository to facilitate the analysis by using an intelligent Python script. The dataset of the cognitive script from the sample research articles consists of unstructured data, and the techniques employed are different from other related studies that apply data integration techniques using grids and peer-to-peer networks on distributed or heterogeneous datasets (Comito et al 2007(Comito et al , 2004. We develop an innovative Python program to generate Cypher statements from cognitive script to create nodes and edges in Neo4j.…”

Section: Literature Reviewmentioning

confidence: 99%

“…We develop an innovative Python program to generate Cypher statements from cognitive script to create nodes and edges in Neo4j. Furthermore, queries are developed by using SQL in existing works (Comito et al 2007), while our study employs knowledge map modelling queries, which offers visual output to facilitate easy navigation and deep understanding of the domain topic.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Knowledge graph model development for knowledge discovery in dementia research using cognitive scripting and next-generation graph-based database: a design science research approach

Fahd

Miao

Miah

et al. 2022

Soc. Netw. Anal. Min.

View full text Add to dashboard Cite

Recent studies report doubling numbers of deaths due to dementia. With such an escalating mortality rate related to cognitive decline diseases, like dementia, timely information on contributing factors and knowledge discovery from evidence-based repositories is warranted. A large amount of scholarly knowledge extracted from research findings on dementia can be understood only using human intelligence for arriving at quality inferences. Due to the unstructured data presented in such a massive dataset of scientific articles available online, gaining insights from the knowledge hidden in the literature is complex and time-consuming. Hence, there is a need for developing a knowledge management model to create, query and maintain a knowledge repository of key elements and their relationships extracted from scholarly articles in a structured manner. In this paper, an innovative knowledge discovery computing model to process key findings from unstructured data from scholarly articles by using the design science research (DSR) methodology is proposed. The solution caters to a novel composition of the cognitive script of crucial knowledge related to dementia and its subsequent transformation from unstructured into a structured format using graph-based next-generation infrastructures. The computing model contains three phases to assist the research community to have a better understanding of the related knowledge in the existing unstructured research articles: (i) article collection and construction of cognitive script, (ii) generation of Cypher statements (a knowledge graph query language) and (iii) creation of graph-based repository and visualization. The performance of the computing model is demonstrated by visualizing the outcome of various search criteria in the form of nodes and their relationships. Our results also demonstrate the effectiveness of visual query and navigation highlighting its usability.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Knowledge graph model development for knowledge discovery in dementia research using cognitive scripting and next-generation graph-based database: a design science research approach

Fahd

Miao

Miah

et al. 2022

Soc. Netw. Anal. Min.

View full text Add to dashboard Cite

show abstract

“…. Some researchers begin to introduce P2P concept into data management and data integration area, and raise P2P data integration solution, such as schema based P2P data integration [25], Pizza [26], PARIS [27], Hyperion [28], and previous version of SGII [29][30] [31] proposed by the writer.…”

Section: Semantic P2p Mapping Based Eiimentioning

confidence: 99%

A Review of Enterprise Information Integration from Content Level and System Level Perspectives

Zhou¹,

Yang²,

Wang³

et al. 2010

IJACT

View full text Add to dashboard Cite

To gain insight into increasingly intricate business, and deal with highly complex problem situations, an enterprise needs a more generic, standardized, pervasive and scalable infrastructure to fully leverage the information from different data sources, applications, and environments at both system level and content level. Based on a discussion of basic problem of EII (enterprise information integration), this paper reviews a state of the art of existing enterprise information integration solutions from content level integration and system level integration perspectives. We evaluate existing approaches to content level integration by comparing the method used to describe and explain the data, and the topology structure of the data connected. We evaluate existing approaches to system level integration by in-depth discussion how the systems are coupled. Based on the results of our analysis we summarize the state-of-the-art in enterprise information integration and give the tendencies of further research.

show abstract

“…This approach imposes the peers in the network to conform their data to local schemas derived by inheritance from a set of base schemas. A similar approach is also adopted in [5] where the resulting unstructured network coexists with a DHT-based structured one in an hybrid topology. In [15], a metric distance for Language Models is adopted to compare the peers' local collections of documents.…”

Section: Related Work and Conclusionmentioning

confidence: 99%

Semantic peer, here are the neighbors you want!

Penzo

Lodi

Mandreoli

et al. 2008

Proceedings of the 11th International Conference on Extending Database Technology: Advances in Database Technology

View full text Add to dashboard Cite

Peer Data Management Systems (PDMSs) have been introduced as a solution to the problem of large-scale sharing of semantically rich data. A PDMS consists of semantic peers connected through semantic mappings. Querying a PDMS may lead to very poor results, because of the semantic degradation due to the approximations given by the traversal of the semantic mappings, thus leading to the problem of how to boost a network of mappings in a PDMS.In this paper we propose a strategy for the incremental maintenance of a flexible network organization that clusters together peers which are semantically related in Semantic Overlay Networks (SONs), while maintaining a high degree of node autonomy. Semantic features, a summarized representation of clusters, are stored in a "light" structure which effectively assists a newly entering peer when choosing its semantically closest overlay networks. Then, each peer is supported in the selection of its own neighbors within each overlay network according to two policies: Range-based selection and k-NN selection. For both policies, we introduce specific algorithms which exploit a distributed indexing mechanism for efficient network navigation. The proposed approach has been implemented in a prototype where its effectiveness and efficiency have been extensively tested.

show abstract

PARIS: A Peer-to-Peer Architecture for Large-Scale Semantic Data Integration

Cited by 8 publications

References 18 publications

Knowledge graph model development for knowledge discovery in dementia research using cognitive scripting and next-generation graph-based database: a design science research approach

Knowledge graph model development for knowledge discovery in dementia research using cognitive scripting and next-generation graph-based database: a design science research approach

A Review of Enterprise Information Integration from Content Level and System Level Perspectives

Semantic peer, here are the neighbors you want!

Contact Info

Product

Resources

About