Gleaning Types for Literals in RDF Triples with Application to Entity Summarization

Gunaratna, Kalpa; Thirunarayan, Krishnaprasad; Sheth, Amit P.; Cheng, Gong

doi:10.1007/978-3-319-34129-3_6

Cited by 35 publications

(77 citation statements)

References 14 publications

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“…However, datatype inference has been addressed in other contexts, such as (theoretical approaches): XML Schema Definition (XSD) [6,7,14], programming languages [4,5,11,16,31], and OWL [13,19,26,28]. Moreover, we have revised some tools available on the Web for XSD inference 2.…”

Section: Related Workmentioning

confidence: 99%

“…Although the use of knowledge and inference rules can infer datatypes where a specific information is known (e.g., type of properties, knowledge database), RDF data are not always available with its respective ontology, which makes impossible the task of detecting rules. In [13], the authors analyze two types of predicates: object property (semantic type, e.g., dbr:Barack Obama) and datatype property (syntactic type, e.g., xsd:string). They propose an approach to infer the semantic type of string literals using the word detection technique called Stanford CoreNLP 3 to identify the principal term and the UMBC 4 semantic similarity service to discover the semantic class.…”

Section: Knowledge-based Approachesmentioning

confidence: 99%

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RDF-F: RDF Datatype inFerring Framework

2018

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In the context of RDF document matching/integration, the datatype information, which is related to literal objects, is an important aspect to be analyzed in order to better determine similar RDF documents. In this paper, we present an RDF Datatype in Ferring Framework, called RDF-F, which provides two independent datatype inference processes: 1) a fourstep process consisting of (i) a predicate information analysis (i.e., deduce the datatype from existing range property), (ii) an analysis of the object value itself by a pattern-matching process (i.e., recognize the object lexical space), (iii) a semantic analysis of the predicate name and its context, and (iv) generalization of Numeric and Binary datatypes to ensure the integration; and 2) a non-ambiguous lexical-space-matching process, where literal values are inferred by the modification of their representation, following new lexical spaces. We evaluated the performance and the accuracy of both processes with datasets from DBpedia. Results show that the execution time of both indicators is linear and their accuracy can increase up to 97.10 and 99.30%, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Knowledge-based Approachesmentioning

confidence: 99%

RDF-F: RDF Datatype inFerring Framework

2018

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“…RELIN [Cheng et al ., 2011], FACES [Gunaratna et al ., 2015; 2016], LinkSum [Thalhammer et al ., 2016], SUMMARUM [Thalhammer and Rettinger, 2014], diversity-based summaries [Sydow et al ., 2013], and contextual entity summaries by mining query logs [Yan et al ., 2016] are good examples. In these approaches, RELIN, SUMMARUM, and LinkSum approaches adapt modified random surfer models (PageRank) to rank facts and then select them for summaries.…”

Section: Related Workmentioning

confidence: 99%

Relatedness-based Multi-Entity Summarization

Gunaratna

Yazdavar

Thirunarayan

et al. 2017

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

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Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple’s Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and (ii) intra-entity facts that are important and diverse. We employ a constrained knapsack problem solving approach to efficiently compute entity summaries. We perform both qualitative and quantitative experiments and demonstrate that our approach yields promising results compared to two other stand-alone state-of-the-art entity summarization approaches.

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“…Semantic data enables users or machines to comprehend and manipulate the conveyed information quickly [10]. In major knowledge graphs, semantic data describes entities by Resource Description Framework (RDF) triples, referred as triples [4].…”

Section: Introductionmentioning

confidence: 99%

AutoSUM: Automating Feature Extraction and Multi-user Preference Simulation for Entity Summarization

Wei

Liu

Zhu

et al. 2020

Advances in Knowledge Discovery and Data Mining

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With the growth of knowledge graphs, entity descriptions are becoming extremely lengthy. Entity summarization task, aiming to generate diverse, comprehensive and representative summaries for entities, has received an increasing interest recently. In most previous methods, features are usually extracted by the hand-crafted templates. Then the feature selection and multi-user preference simulation take place, depending too much on human expertise. In this paper, a novel integration method called AutoSUM is proposed for automatic feature extraction and multi-user preference simulation to overcome the drawbacks of previous methods. There are two modules in AutoSUM: extractor and simulator. The extractor module operates automatic feature extraction based on a BiLSTM with a combined input representation including word embeddings and graph embeddings. Meanwhile, the simulator module automates multi-user preference simulation based on a well-designed twophase attention mechanism (i.e., entity-phase attention and user-phase attention). Experimental results demonstrate that AutoSUM produces the state-of-the-art performance on two widely used datasets (i.e., DBpedia and LinkedMDB) in both F-measure and MAP.

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Gleaning Types for Literals in RDF Triples with Application to Entity Summarization

Cited by 35 publications

References 14 publications

RDF-F: RDF Datatype inFerring Framework

RDF-F: RDF Datatype inFerring Framework

Relatedness-based Multi-Entity Summarization

AutoSUM: Automating Feature Extraction and Multi-user Preference Simulation for Entity Summarization

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