Knowledge Graph Identification

Pujara, Jay; Miao, Hui; Getoor, Lise; Cohen, William W.

doi:10.1007/978-3-642-41335-3_34

Cited by 243 publications

(169 citation statements)

References 9 publications

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“…This means that we treat ontological knowledge as uncertain. Similarly to us, Pujara et al [13] used PSL to infer knowledge graphs about real-world entities from noisy extractors, and, in particular, the assimilation of lexical similarities and ontological evidence proves to be crucial in de-noising extracted graphs.…”

Section: Related Workmentioning

confidence: 99%

Structuring Linked Data Search Results Using Probabilistic Soft Logic

Alshukaili

Fernandes

Paton

2016

Lecture Notes in Computer Science

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Abstract. On-the-fly generation of integrated representations of Linked Data (LD) search results is challenging because it requires successfully automating a number of complex subtasks, such as structure inference and matching of both instances and concepts, each of which gives rise to uncertain outcomes. Such uncertainty is unavoidable given the semantically heterogeneous nature of web sources, including LD ones. This paper approaches the problem of structuring LD search results as an evidencebased one. In particular, the paper shows how one formalism (viz., probabilistic soft logic (PSL)) can be exploited to assimilate different sources of evidence in a principled way and to beneficial effect for users. The paper considers syntactic evidence derived from matching algorithms, semantic evidence derived from LD vocabularies, and user evidence, in the form of feedback. The main contributions are: sets of PSL rules that model the uniform assimilation of diverse kinds of evidence, an empirical evaluation of how the resulting PSL programs perform in terms of their ability to infer structure for integrating LD search results, and, finally, a concrete example of how populating such inferred structures for presentation to the end user is beneficial, besides enabling the collection of feedback whose assimilation further improves search result presentation.

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Section: Related Workmentioning

confidence: 99%

Structuring Linked Data Search Results Using Probabilistic Soft Logic

Alshukaili

Fernandes

Paton

2016

Lecture Notes in Computer Science

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“…More importantly, in contrast to Markov Logic, PSL avoids the hard combinatorial optimization problem and instead provides scalable inference with guarantees on solution quality. This advantage has proven crucial also for applications of PSL in knowledge graph identification [15] and data fusion [16], [17].…”

Section: Related Workmentioning

confidence: 99%

“…Using these notions, we define our probabilistic optimization problem using probabilistic soft logic (PSL) [14], a scalable probabilistic programming language based on weighted logical rules. PSL has been used successfully for a variety of data and knowledge integration problems, including knowledge graph identification [15] and data fusion [16], [17]. It however did not support the kind of open world reasoning required for mapping selection, where we need to express constraints over the existence of elements in a set satisfying certain conditions, namely, st tgds in the mapping explaining tuples in the data example, and furthermore, preferences over these elements are available.…”

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confidence: 99%

A Collective, Probabilistic Approach to Schema Mapping Using Diverse Noisy Evidence

Kimmig

Memory

Miller

et al. 2019

IEEE Trans. Knowl. Data Eng.

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Abstract-We propose a probabilistic approach to the problem of schema mapping. Our approach is declarative, scalable, and extensible. It builds upon recent results in both schema mapping and probabilistic reasoning and contributes novel techniques in both fields. We introduce the problem of schema mapping selection, that is, choosing the best mapping from a space of potential mappings, given both metadata constraints and a data example. As selection has to reason holistically about the inputs and the dependencies between the chosen mappings, we define a new schema mapping optimization problem which captures interactions between mappings as well as inconsistencies and incompleteness in the input. We then introduce Collective Mapping Discovery (CMD), our solution to this problem using state-of-the-art probabilistic reasoning techniques. Our evaluation on a wide range of integration scenarios, including several real-world domains, demonstrates that CMD effectively combines data and metadata information to infer highly accurate mappings even with significant levels of noise.

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“…Wellknown knowledge graph systems include Google Knowledge Graph, Probase, DBPedia, YAGO, and TrueKnowledge. In a knowledge graph, entities are denoted as nodes or datapoints, categories are their labels, and relationships are directed links between these datapoints [32]. However, in most data mining applications, the labels of many datapoints are missing, and it is often prohibitively laborintensive and time-consuming to collect their labels.…”

Section: Introductionmentioning

confidence: 99%

FLAG: Faster Learning on Anchor Graph with Label Predictor Optimization

Wang

Hao

et al. 2022

IEEE Trans. Big Data

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Abstract-Knowledge graphs have received intensive research interests. When the labels of most nodes or datapoints are missing, anchor graph and hierarchical anchor graph models can be employed. With an anchor graph or hierarchical anchor graph, we only need to optimize the labels of the coarsest anchors, and the labels of datapoints can be inferred from these anchors in a coarse-tofine manner. The complexity of optimization is therefore reduced to a cubic cost with respect to the number of the coarsest anchors. However, to obtain a high accuracy when a data distribution is complex, the scale of this anchor set still needs to be large, which thus inevitably incurs an expensive computational burden. As such, a challenge in scaling up these models is how to efficiently estimate the labels of these anchors while keeping classification performance. To address this problem, we propose a novel approach that adds an anchor label predictor in the conventional anchor graph and hierarchical anchor graph models. In the proposed approach, the labels of the coarsest anchors are not directly optimized, and instead, we learn a label predictor which estimates the labels of these anchors with their spectral representations. The predictor is optimized with a regularization on all datapoints based on a hierarchical anchor graph, and we show that its solution only involves the inversion of a small-size matrix. Built upon the anchor hierarchy, we design a sparse intra-layer adjacency matrix over these anchors, which can simultaneously accelerate spectral embedding and enhance effectiveness. Our approach is named Faster Learning on Anchor Graph (FLAG) as it improves conventional anchor-graph-based methods in terms of efficiency. Experiments on a variety of publicly available datasets with sizes varying from thousands to millions of samples demonstrate the effectiveness of our approach.

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Knowledge Graph Identification

Cited by 243 publications

References 9 publications

Structuring Linked Data Search Results Using Probabilistic Soft Logic

Structuring Linked Data Search Results Using Probabilistic Soft Logic

A Collective, Probabilistic Approach to Schema Mapping Using Diverse Noisy Evidence

FLAG: Faster Learning on Anchor Graph with Label Predictor Optimization

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