User-friendly Comparison of Similarity Algorithms on Wikidata

Ilievski, Filip; Szekely, Pedro; Satyukov, Gleb; Singh, Rajesh

doi:10.48550/arxiv.2108.05410

Cited by 2 publications

(5 citation statements)

References 8 publications

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“…Composite embeddings Considering that KG and LM embeddings may provide complementary insights for similarity [40], we create two composite embeddings. Composite-all combines all our embedding models: two translation models (TransE and ComplEx), two random-walk models (Deepwalk, S-Deepwalk), and two LMs (Abstract and Lexicalize).…”

Section: Embedding Combinationmentioning

confidence: 99%

“…We use scikit-learn for supervised learning. We use KGTK's similarity API [40] to obtain scores for the metrics Class, Jiang Conrath, and TopSim. 8…”

Section: Implementation Detailsmentioning

confidence: 99%

“…At the same time, other children of tale include oldfashioned tale, cumulative tale, urban tale, and Zeichengeschichte (a German television genre), whose similarity with fairy tale is lower, as each of these siblings describes a different aspect of a tale: its location, ethnic tradition, form, or genre. To quantify this phenomenon, we show similarity scores with the KGTK similarity GUI [40] between fairy tale, and its parent (tale) and siblings in Fig. 4.…”

Section: What Knowledge Is Most Beneficial For Retrofitting?mentioning

confidence: 99%

“…Fig.4. Similarity scores with the KGTK similarity GUI[40] between fairy tale, and its parent (tale) and siblings.…”

mentioning

confidence: 99%

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A study of concept similarity in Wikidata

Ilievski,

Shenoy,

Chalupsky

et al. 2024

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Robust estimation of concept similarity is crucial for applications of AI in the commercial, biomedical, and publishing domains, among others. While the related task of word similarity has been extensively studied, resulting in a wide range of methods, estimating concept similarity between nodes in Wikidata has not been considered so far. In light of the adoption of Wikidata for increasingly complex tasks that rely on similarity, and its unique size, breadth, and crowdsourcing nature, we propose that conceptual similarity should be revisited for the case of Wikidata. In this paper, we study a wide range of representative similarity methods for Wikidata, organized into three categories, and leverage background information for knowledge injection via retrofitting. We measure the impact of retrofitting with different weighted subsets from Wikidata and ProBase. Experiments on three benchmarks show that the best performance is achieved by pairing language models with rich information, whereas the impact of injecting knowledge is most positive on methods that originally do not consider comprehensive information. The performance of retrofitting is conditioned on the selection of high-quality similarity knowledge. A key limitation of this study, similar to prior work lies in the limited size and scope of the similarity benchmarks. While Wikidata provides an unprecedented possibility for a representative evaluation of concept similarity, effectively doing so remains a key challenge.

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Section: Embedding Combinationmentioning

confidence: 99%

“…We use scikit-learn for supervised learning. We use KGTK's similarity API [40] to obtain scores for the metrics Class, Jiang Conrath, and TopSim. 8…”

Section: Implementation Detailsmentioning

confidence: 99%

Section: What Knowledge Is Most Beneficial For Retrofitting?mentioning

confidence: 99%

“…Fig.4. Similarity scores with the KGTK similarity GUI[40] between fairy tale, and its parent (tale) and siblings.…”

mentioning

confidence: 99%

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A study of concept similarity in Wikidata

Ilievski,

Shenoy,

Chalupsky

et al. 2024

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show abstract

“…10 For Wikidata, we used Falcon 2.0 (Sakor et al, 2020) 11 for entity linking. We collected entity embeddings from KGTK (Ilievski et al, 2021) 12 (the text version); we also reported the results using RDF2vec (Ristoski et al, 2019) 13 (the sg_200_5_5_15_4_500 version) for comparison.…”

Section: Explicit Knowledge-enhanced Retrievalmentioning

confidence: 99%

An Empirical Investigation of Implicit and Explicit Knowledge-Enhanced Methods for Ad Hoc Dataset Retrieval

Luo,

Chen,

Zhang

et al. 2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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Ad hoc dataset retrieval has become an important way of finding data on the Web, where the underlying problem is how to measure the relevance of a dataset to a query. State-ofthe-art solutions for this task are still lexical methods, which cannot capture semantic similarity. Semantics-aware knowledge-enhanced retrieval methods, which achieved promising results on other tasks, have yet to be systematically studied on this specialized task. To fill the gap, in this paper, we present an empirical investigation of the task where we implement and evaluate, on two test collections, a set of implicit and explicit knowledge-enhancement retrieval methods in various settings. Our results reveal the unique features of the task and suggest an interpolation of different kinds of methods as the current best practice.

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User-friendly Comparison of Similarity Algorithms on Wikidata

Cited by 2 publications

References 8 publications

A study of concept similarity in Wikidata

A study of concept similarity in Wikidata

An Empirical Investigation of Implicit and Explicit Knowledge-Enhanced Methods for Ad Hoc Dataset Retrieval

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