Harnessing relationships for domain-specific subgraph extraction: A recommendation use case

Lalithsena, Sarasi; Kapanipathi, Pavan; Sheth, Amit P.

doi:10.1109/bigdata.2016.7840663

Cited by 9 publications

(6 citation statements)

References 15 publications

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“…A related approach (Lalithsena et al [2016]) focuses on identifying "a minimal domainspecific subgraph by utilizing statistics and semantic-based metrics". It targets DBpedia as a knowledge base and focuses on identifying entities and relationships strongly associated with a domain of interest.…”

Section: Related Work On Knowledge Extraction From Dbpediamentioning

confidence: 99%

Automatic generation of questions from DBpedia

Rocha

Faron-Zucker

Giboin

et al. 2020

IJCEELL

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The production of Educational quizzes is a time-consuming task that can be automated by taking advantage of existing knowledge bases available on the Web of Linked Open Data (LOD) such as DBpedia. For these quizzes to be useful to learners, their questions must be generated in compliance with the knowledge and skills necessary to master for each subject and school year according to the official educational standards. We present an approach that exploits structured knowledge bases that contain the knowledge and skills, from which it selects a set of DBpedia resources relevant to a specific school subject and year. This set of resources is enriched with additional related resources, through the NEFCE heuristic. Finally, question generation strategies are applied to the graph generated with this set of resulting resources. Likewise, we provide an evaluation of two knowledge bases and of the proposed NEFCE heuristics.

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Section: Related Work On Knowledge Extraction From Dbpediamentioning

confidence: 99%

Automatic generation of questions from DBpedia

Rocha

Faron-Zucker

Giboin

et al. 2020

IJCEELL

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“…We generated H i from DBpedia [1] starting with a "root node" concept and recursively extracting all the concepts connected with "skos:broader" or "subject" relationships. One can use a domain-graph generation tool such as [15] to generate such a hierarchical graph. Note that each node v i ∈ V is a list of concepts indicating attribute values a part of the example hierarchical knowledge graph as shown in Figure 2.…”

Section: Definitions and Notationsmentioning

confidence: 99%

“…E.g., all the concepts subsumed by "Cities in Ohio" along with "Cities in Ohio" provides a context "Ohio". Such knowledge graphs can be generated automatically with demonstrated benefit to applications such as personalization [15]. HKGs provide complementary realworld information regarding communities or clusters that may not be explicit in the network but are nevertheless useful in finding and characterizing communities.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Graph Enhanced Community Detection and Characterization

Bhatt

Padhee

Sheth

et al. 2019

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining

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Recent studies show that by combining network topology and node attributes, we can better understand community structures in complex networks. However, existing algorithms do not explore "contextually" similar node attribute values, and therefore may miss communities defined with abstract concepts. We propose a community detection and characterization algorithm that incorporates the contextual information of node attributes described by multiple domain-specific hierarchical concept graphs. The core problem is to find the context that can best summarize the nodes in communities, while also discovering communities aligned with the context summarizing communities. We formulate the two intertwined problems, optimal community-context computation, and community discovery, with a coordinate-ascent based algorithm that iteratively updates the nodes' community label assignment with a community-context and computes the best context summarizing nodes of each community. Our unique contributions include (1) a composite metric on Informativeness and Purity criteria in searching for the best context summarizing nodes of a community; (2) a node similarity measure that incorporates the context-level similarity on multiple node attributes; and (3) an integrated algorithm that drives community structure discovery by appropriately weighing edges. Experimental results on public datasets show nearly 20 percent improvement on F-measure and Jaccard for discovering underlying community structure over the current state-of-the-art of community detection methods. Community structure characterization was also accurate to find appropriate community types for four datasets. Moreover, our algorithm yields insightful community structures that explain the contextual relationships among communities, which helps us better understand two real-world applications of social networks.

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“…This highlights the need to identify and select relevant knowledge bases such as the linked open data cloud, and extract the relevant portion of the knowledge from broad coverage sources such as Wikipedia and DBpedia. We are working on automatically indexing the domains of the knowledge bases [17] and exploiting the semantics of the entities and their relationships to select relevant portions of a knowledge base [18].…”

Section: Challenges In Creating and Using Knowledge Basesmentioning

confidence: 99%

Knowledge will propel machine understanding of content

Sheth

Perera

Wijeratne

et al. 2017

Proceedings of the International Conference on Web Intelligence

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Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.

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Harnessing relationships for domain-specific subgraph extraction: A recommendation use case

Cited by 9 publications

References 15 publications

Automatic generation of questions from DBpedia

Automatic generation of questions from DBpedia

Knowledge Graph Enhanced Community Detection and Characterization

Knowledge will propel machine understanding of content

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