Efficient Computation of Semantically Cohesive Subgraphs for Keyword-Based Knowledge Graph Exploration

Shi, Yuxuan; Cheng, Gong; Tran, Trung-Kien; Kharlamov, Evgeny; Shen, Yulin

doi:10.1145/3442381.3449900

Cited by 13 publications

(5 citation statements)

References 35 publications

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“…For future work, our methodology will be expanded to include the evaluation of RDF2Vec alongside other embedders like TransE, TransR, RotatE, etc. [11,27]. Furthermore, we aim to investigate additional ML methodologies, such as techniques for learning to rank [28] and deep neural networks.…”

Section: Discussionmentioning

confidence: 99%

Constructing Semantic Summaries Using Embeddings

Trouli,

Papadakis,

Kondylakis

2024

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The increase in the size and complexity of large knowledge graphs now available online has resulted in the emergence of many approaches focusing on enabling the quick exploration of the content of those data sources. Structural non-quotient semantic summaries have been proposed in this direction that involve first selecting the most important nodes and then linking them, trying to extract the most useful subgraph out of the original graph. However, the current state of the art systems use costly centrality measures for identifying the most important nodes, whereas even costlier procedures have been devised for linking the selected nodes. In this paper, we address both those deficiencies by first exploiting embeddings for node selection, and then by meticulously selecting approximate algorithms for node linking. Experiments performed over two real-world big KGs demonstrate that the summaries constructed using our method enjoy better quality. Specifically, the coverage scores obtained were 0.8, 0.81, and 0.81 for DBpedia v3.9 and 0.94 for Wikidata dump 2018, across 20%, 25%, and 30% summary sizes, respectively. Additionally, our method can compute orders of magnitude faster than the state of the art.

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Section: Discussionmentioning

confidence: 99%

Constructing Semantic Summaries Using Embeddings

Trouli,

Papadakis,

Kondylakis

2024

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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.…”

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confidence: 99%