Keyword-Based Knowledge Graph Exploration Based on Quadratic Group Steiner Trees

Shi, Yuxuan; Cheng, Gong; Tran, Trung-Kien; Tang, Jie; Kharlamov, Evgeny

doi:10.24963/ijcai.2021/215

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…The first one is known as Mondial [204] which is a database containing geographical information in English from different web sources. This benchmark is often used as an academic benchmark [205][206][207] and is available in several formats. We are interested in the relational format of the database by considering the relational schema mondialschema.sql 8 .…”

Section: Use Casementioning

confidence: 99%

A general approach for of schema matching problem: case of databases

Labreche

Lorca

Montarnal

et al. 2022

Preprint

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The problem of schema matching is one of the main tasks in several processes dealing with databases, data transformation and data integration. Schema matching involves the generation and linking of heterogeneous schemas. This heterogeneity makes the implementation of a solution difficult and the sources of heterogeneity are multiple. The need for a methodology that can adapt to the largest number of points of variation is necessary. In this paper, we propose a flexible, global and generic approach to meet this need where the main idea is to exploit as much information available in the schemas and this through the implementation of federated interoperability that allows the interpretation of information on the fly. To this end, the approach uses graph theory, more specifically hypergraphs and optimization models for the modelling of schemas and the automation of the solution of the schema matching problem by converting it into a graph matching problem. Our contributions include the proposal of an optimization model to add specific constraints to the schema matching problem, as well as the propagation algorithms to propagate the matching relationships between the source and target schemas. The approach is finally evaluated on two use cases, one academic and one industrial.

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Section: Use Casementioning

confidence: 99%

A general approach for of schema matching problem: case of databases

Labreche

Lorca

Montarnal

et al. 2022

Preprint

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“…Existing keyword query systems [1][2][3][4][5][6][7][8][9][10][11][12] enable users to query information in knowledge graph by returning the subgraph containing the keywords, but they may return unwanted answers because there are too many possible interpretations. For query "Feng_xiaogang films", it is not easy to answer this query since there are too many paths between the entity "Feng_xiaogang" and the instances of the type "Film" (e.g., the paths "-direct/starringIn-,""-direct-," "-starringIn-," "-award-," "-FilmDirector," "-spouse-x-starringIn-" as shown in Figure 6).…”

Section: Introductionmentioning

confidence: 99%

“…They can be divided into two categories: 1) data index. The prevalent approaches [1][2][3][4][5][6][7][8] building on dedicated indexing techniques aim at finding substructures that connect the data elements which match the keywords. With the explosive growth of knowledge graph, it is obvious that the dedicated data index will be faced with bottleneck, especially knowledge graph with billions of triples.…”

Section: Introductionmentioning

confidence: 99%

Scalable Interactive Keyword Query Interface over Knowledge Graph

Hu,

Duan,

Zhang

2023

Fuzzy Systems and Data Mining IX

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goes here. Existing keyword query systems over knowledge graph can produce interesting results and are easy to use. However, they cannot handle the ambiguities that have matches in the knowledge graph, namely, multiple interpretations may be correct so that they cannot determine which interpretation is what the user expects. And they cannot scale to handle the knowledge graphs with more than billions of triples or thousands of types/predicates. On the one hand, we construct an interactive interface in which the above ambiguities will resort to the user. To enhance the user experience, we formalize the interaction problem and then propose an algorithm to find a best scheme of interaction (i.e., a verifying sequence with lowest interaction times and candidates) based on the dependency relations between mappings. On the other hand, we propose a new schema graph, i.e., type-predicate graph, which has good scalability while containing complete information for building query graph. No matter how large the knowledge graph is, the size of type-predicate graph is always very small because its size depends on the number of types and predicates whose number are far less than that of triples in knowledge graph. Finally, we have demonstrated our contributions with several well-directed experiments over real datasets (DBpedia and Yago).

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“…Data like XML files, KGs [12,13] provide an efficient foundation for querying information of interest via clearly defined formats. SPARQL queries or keywords are used to query data [14][15][16][17] for inspection, information summary, and diagnostics. Data search outputs datasets, databases, or snippets of datasets [18][19][20][21] and relies on the metadata-based query, KG summarisation, natural language-based search [22], or even the content-based search, which Bosch is researching on.…”

mentioning

confidence: 99%