@misc{huang2021sinetym, 
    author = {Huang, Yihua}, 
    title = {{Comparative Analysis Toolset for Chinese Dialects}}, 
    year = {2021}, 
    howpublished = {\url{https://github.com/lernanto/sinetym}}, 
    note = {Retrieved March 26, 2023.} 
}

@misc{2011ccr, 
    author = {Huang, Rongpei and Yang, Xiufang and He, Daan}, 
    title = {{Chinese Character Readings}}, 
    year = {2011}, 
    howpublished = {\url{https://xiaoxue.iis.sinica.edu.tw/ccr/#}}, 
    note = {Retrieved March 26, 2023.} 
}

https://xiaoxue.iis.sinica.edu.tw/ccr/#

@misc{zhou2020nk2028,
  author = {nk2028},
  title = {{Qieyun-js}},
  year = {2020},
  howpublished = {\url{https://github.com/nk2028}},
  note = {Retrieved March 26, 2023.}
}

@inproceedings{mcinnes2017accelerated,
  title={Accelerated Hierarchical Density Based Clustering},
  author={McInnes, Leland and Healy, John},
  booktitle={Data Mining Workshops (ICDMW), 2017 IEEE International Conference on},
  pages={33--42},
  year={2017},
  organization={IEEE}
}

@article{mcinnes2018umap-software,
  title={UMAP: Uniform Manifold Approximation and Projection},
  author={McInnes, Leland and Healy, John and Saul, Nathaniel and Grossberger, Lukas},
  journal={The Journal of Open Source Software},
  volume={3},
  number={29},
  pages={861},
  year={2018}
}
@article{2018arXivUMAP,
     author = {{McInnes}, L. and {Healy}, J. and {Melville}, J.},
     title = "{UMAP: Uniform Manifold Approximation
     and Projection for Dimension Reduction}",
     journal = {ArXiv e-prints},
     archivePrefix = "arXiv",
     eprint = {1802.03426},
     primaryClass = "stat.ML",
     keywords = {Statistics - Machine Learning,
                 Computer Science - Computational Geometry,
                 Computer Science - Learning},
     year = 2018,
     month = feb,
}

@article{pykeen2021software,
    author = {Ali, Mehdi and Berrendorf, Max and Hoyt, Charles Tapley and Vermue, Laurent and Sharifzadeh, Sahand and Tresp, Volker and Lehmann, Jens},
    journal = {Journal of Machine Learning Research},
    number = {82},
    pages = {1--6},
    title = {{PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings}},
    url = {https://jmlr.org/papers/v22/20-825.html},
    volume = {22},
    year = {2021}
}
@article{pykeen2021benchmarking,
  author = {Ali, Mehdi and Berrendorf, Max and Hoyt, Charles Tapley and Vermue, Laurent and Galkin, Mikhail and Sharifzadeh, Sahand and Fischer, Asja and Tresp, Volker and Lehmann, Jens},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title = {{Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models under a Unified Framework}},
  year = {2021},
  pages = {1-1},
  doi = {10.1109/TPAMI.2021.3124805},
  url = {https://doi.org/10.1109/TPAMI.2021.3124805}
}
@article{tilsen_localizing_2021,
	title = {Localizing category-related information in speech with multi-scale analyses},
	volume = {16},
	issn = {1932-6203},
	url = {https://dx.plos.org/10.1371/journal.pone.0258178},
	doi = {10.1371/journal.pone.0258178},
	abstract = {Measurements of the physical outputs of speech—vocal tract geometry and acoustic energy—are high-dimensional, but linguistic theories posit a low-dimensional set of categories such as phonemes and phrase types. How can it be determined when and where in high-dimensional articulatory and acoustic signals there is information related to theoretical categories? For a variety of reasons, it is problematic to directly quantify mutual information between hypothesized categories and signals. To address this issue, a multi-scale analysis method is proposed for localizing category-related information in an ensemble of speech signals using machine learning algorithms. By analyzing how classification accuracy on unseen data varies as the temporal extent of training input is systematically restricted, inferences can be drawn regarding the temporal distribution of category-related information. The method can also be used to investigate redundancy between subsets of signal dimensions. Two types of theoretical categories are examined in this paper: phonemic/gestural categories and syntactic relative clause categories. Moreover, two different machine learning algorithms were examined: linear discriminant analysis and neural networks with long short-term memory units. Both algorithms detected category-related information earlier and later in signals than would be expected given standard theoretical assumptions about when linguistic categories should influence speech. The neural network algorithm was able to identify category-related information to a greater extent than the discriminant analyses.},
	language = {en},
	number = {10},
	urldate = {2023-03-25},
	journal = {PLOS ONE},
	author = {Tilsen, Sam and Kim, Seung-Eun and Wang, Claire},
	editor = {Lancia, Leonardo},
	month = oct,
	year = {2021},
	pages = {e0258178},
	file = {Tilsen 等 - 2021 - Localizing category-related information in speech .pdf:files/7/Tilsen 等 - 2021 - Localizing category-related information in speech .pdf:application/pdf},
}

@article{list_potential_2017,
	title = {The {Potential} of {Automatic} {Word} {Comparison} for {Historical} {Linguistics}},
	volume = {12},
	issn = {1932-6203},
	url = {https://dx.plos.org/10.1371/journal.pone.0170046},
	doi = {10.1371/journal.pone.0170046},
	abstract = {The amount of data from languages spoken all over the world is rapidly increasing. Traditional manual methods in historical linguistics need to face the challenges brought by this influx of data. Automatic approaches to word comparison could provide invaluable help to pre-analyze data which can be later enhanced by experts. In this way, computational approaches can take care of the repetitive and schematic tasks leaving experts to concentrate on answering interesting questions. Here we test the potential of automatic methods to detect etymologically related words (cognates) in cross-linguistic data. Using a newly compiled database of expert cognate judgments across five different language families, we compare how well different automatic approaches distinguish related from unrelated words. Our results show that automatic methods can identify cognates with a very high degree of accuracy, reaching 89\% for the best-performing method Infomap. We identify the specific strengths and weaknesses of these different methods and point to major challenges for future approaches. Current automatic approaches for cognate detection—although not perfect—could become an important component of future research in historical linguistics.},
	language = {en},
	number = {1},
	urldate = {2023-03-25},
	journal = {PLOS ONE},
	author = {List, Johann-Mattis and Greenhill, Simon J. and Gray, Russell D.},
	editor = {Berwick, Robert C},
	month = jan,
	year = {2017},
	pages = {e0170046},
}

@article{steiner_pipeline_2011,
	title = {A {Pipeline} for {Computational} {Historical} {Linguistics}},
	volume = {1},
	issn = {2210-5824, 2210-5832},
	url = {https://brill.com/view/journals/ldc/1/1/article-p89_4.xml},
	doi = {10.1163/221058211X570358},
	abstract = {Abstract
              There are many parallels between historical linguistics and molecular phylogenetics. In this paper we describe an algorithmic pipeline that mimics, as closely as possible, the traditional workflow of language reconstruction known as the comparative method. The pipeline consists of suitably modified algorithms based on recent research in bioinformatics, which are adapted to the specifics of linguistic data. This approach can alleviate much of the laborious research needed to establish proof of historical relationships between languages. Equally important to our proposal is that each step in the workflow of the comparative method is implemented independently, so language specialists have the possibility to scrutinize intermediate results. We have used our pipeline to investigate two groups of languages, the Tsezic languages of the Caucasus and the Mataco-Guaicuruan languages of South America, based on the lexical data from the Intercontinental Dictionary Series (IDS). The results of these tests show that the current approach is a viable and useful extension to historical linguistic research.},
	language = {en},
	number = {1},
	urldate = {2023-03-25},
	journal = {Language Dynamics and Change},
	author = {Steiner, Lydia and Cysouw, Michael and Stadler, Peter},
	year = {2011},
	pages = {89--127},
	file = {Steiner 等 - 2011 - A Pipeline for Computational Historical Linguistic.pdf:files/16/Steiner 等 - 2011 - A Pipeline for Computational Historical Linguistic.pdf:application/pdf},
}

@inproceedings{nerbonne_computing_2007,
	address = {Prauge, Czech Republic},
	title = {Computing and historical phonology},
	url = {http://portal.acm.org/citation.cfm?doid=1626516.1626517},
	doi = {10.3115/1626516.1626517},
	abstract = {We introduce the proceedings from the workshop ‘Computing and Historical Phonology: 9th Meeting of the ACL Special Interest Group for Computational Morphology and Phonology’.},
	language = {en},
	urldate = {2023-03-25},
	booktitle = {Proceedings of {Ninth} {Meeting} of the {ACL} {Special} {Interest} {Group} in {Computational} {Morphology} and {Phonology} on - {SigMorPhon} '07},
	publisher = {Association for Computational Linguistics},
	author = {Nerbonne, John and Ellison, T. Mark and Kondrak, Grzegorz},
	year = {2007},
	pages = {1--5},
	file = {Nerbonne 等 - 2007 - Computing and historical phonology.pdf:files/18/Nerbonne 等 - 2007 - Computing and historical phonology.pdf:application/pdf},
}

@article{batsuren_large_2022,
	title = {A large and evolving cognate database},
	volume = {56},
	issn = {1574-020X, 1574-0218},
	url = {https://link.springer.com/10.1007/s10579-021-09544-6},
	doi = {10.1007/s10579-021-09544-6},
	abstract = {We present CogNet, a large-scale, automatically-built database of sensetagged cognates—words of common origin and meaning across languages. CogNet is continuously evolving: its current version contains over 8 million cognate pairs over 338 languages and 35 writing systems, with new releases already in preparation. The paper presents the algorithm and input resources used for its computation, an evaluation of the result, as well as a quantitative analysis of cognate data leading to novel insights on language diversity. Furthermore, as an example on the use of large-scale cross-lingual knowledge bases for improving the quality of multilingual applications, we present a case study on the use of CogNet for bilingual lexicon induction in the framework of cross-lingual transfer learning.},
	language = {en},
	number = {1},
	urldate = {2023-03-25},
	journal = {Language Resources and Evaluation},
	author = {Batsuren, Khuyagbaatar and Bella, Gábor and Giunchiglia, Fausto},
	month = mar,
	year = {2022},
	pages = {165--189},
	file = {Batsuren 等 - 2022 - A large and evolving cognate database.pdf:files/142/Batsuren 等 - 2022 - A large and evolving cognate database.pdf:application/pdf},
}

@inproceedings{batsuren_morphynet_2021,
	address = {Online},
	title = {{MorphyNet}: a {Large} {Multilingual} {Database} of {Derivational} and {Inflectional} {Morphology}},
	shorttitle = {{MorphyNet}},
	url = {https://aclanthology.org/2021.sigmorphon-1.5},
	doi = {10.18653/v1/2021.sigmorphon-1.5},
	abstract = {Large-scale morphological databases provide essential input to a wide range of NLP applications. Inflectional data is of particular importance for morphologically rich (agglutinative and highly inflecting) languages, and derivations can be used, e.g. to infer the semantics of out-of-vocabulary words. Extending the scope of state-of-the-art multilingual morphological databases, we announce the release of MorphyNet, a high-quality resource with 15 languages, 519k derivational and 10.1M inflectional entries, and a rich set of morphological features. MorphyNet was extracted from Wiktionary using both hand-crafted and automated methods, and was manually evaluated to be of a precision higher than 98\%. Both the resource generation logic and the resulting database are made freely available and are reusable as stand-alone tools or in combination with existing resources.},
	urldate = {2023-03-25},
	booktitle = {Proceedings of the 18th {SIGMORPHON} {Workshop} on {Computational} {Research} in {Phonetics}, {Phonology}, and {Morphology}},
	publisher = {Association for Computational Linguistics},
	author = {Batsuren, Khuyagbaatar and Bella, Gábor and Giunchiglia, Fausto},
	month = aug,
	year = {2021},
	pages = {39--48},
	file = {Full Text PDF:files/200/Batsuren 等 - 2021 - MorphyNet a Large Multilingual Database of Deriva.pdf:application/pdf},
}


@article{steiner_pipeline_2011,
	title = {A {Pipeline} for {Computational} {Historical} {Linguistics}},
	volume = {1},
	issn = {2210-5824, 2210-5832},
	url = {https://brill.com/view/journals/ldc/1/1/article-p89_4.xml},
	doi = {10.1163/221058211X570358},
	abstract = {Abstract
              There are many parallels between historical linguistics and molecular phylogenetics. In this paper we describe an algorithmic pipeline that mimics, as closely as possible, the traditional workflow of language reconstruction known as the comparative method. The pipeline consists of suitably modified algorithms based on recent research in bioinformatics, which are adapted to the specifics of linguistic data. This approach can alleviate much of the laborious research needed to establish proof of historical relationships between languages. Equally important to our proposal is that each step in the workflow of the comparative method is implemented independently, so language specialists have the possibility to scrutinize intermediate results. We have used our pipeline to investigate two groups of languages, the Tsezic languages of the Caucasus and the Mataco-Guaicuruan languages of South America, based on the lexical data from the Intercontinental Dictionary Series (IDS). The results of these tests show that the current approach is a viable and useful extension to historical linguistic research.},
	language = {en},
	number = {1},
	urldate = {2023-03-25},
	journal = {Language Dynamics and Change},
	author = {Steiner, Lydia and Cysouw, Michael and Stadler, Peter},
	year = {2011},
	pages = {89--127},
	file = {Steiner 等 - 2011 - A Pipeline for Computational Historical Linguistic.pdf:D\:\\4.文库\\Zot\\storage\\QJF3ITR6\\Steiner 等 - 2011 - A Pipeline for Computational Historical Linguistic.pdf:application/pdf},
}

@inproceedings{li_representation_2020,
	address = {Online},
	title = {Representation {Learning} for {Discovering} {Phonemic} {Tone} {Contours}},
	url = {https://www.aclweb.org/anthology/2020.sigmorphon-1.26},
	doi = {10.18653/v1/2020.sigmorphon-1.26},
	abstract = {Tone is a prosodic feature used to distinguish words in many languages, some of which are endangered and scarcely documented. In this work, we use unsupervised representation learning to identify probable clusters of syllables that share the same phonemic tone. Our method extracts the pitch for each syllable, then trains a convolutional autoencoder to learn a low-dimensional representation for each contour. We then apply the mean shift algorithm to cluster tones in high-density regions of the latent space. Furthermore, by feeding the centers of each cluster into the decoder, we produce a prototypical contour that represents each cluster. We apply this method to spoken multi-syllable words in Mandarin Chinese and Cantonese and evaluate how closely our clusters match the ground truth tone categories. Finally, we discuss some difﬁculties with our approach, including contextual tone variation and allophony effects.},
	language = {en},
	urldate = {2023-03-25},
	booktitle = {Proceedings of the 17th {SIGMORPHON} {Workshop} on {Computational} {Research} in {Phonetics}, {Phonology}, and {Morphology}},
	publisher = {Association for Computational Linguistics},
	author = {Li, Bai and Xie, Jing Yi and Rudzicz, Frank},
	year = {2020},
	pages = {217--223},
	file = {Li 等 - 2020 - Representation Learning for Discovering Phonemic T.pdf:D\:\\4.文库\\Zot\\storage\\R8QQRNEW\\Li 等 - 2020 - Representation Learning for Discovering Phonemic T.pdf:application/pdf},
}

@article{ma2022survey,
  author = {Ma, Han and Tang, Roubing and Zhang, Yi and Zhang, Qiaoling},
  title = {Survey on Speech Recognition},
  journal = {Computer Systems and Applications},
  volume = {31},
  number = {1},
  pages = {1-10},
  year = {2022},
  url = {http://www.c-s-a.org.cn/1003-3254/8323.html},
  language = {Chinese}
}

@inproceedings{shain_measuring_2019,
	address = {Minneapolis, Minnesota},
	title = {Measuring the perceptual availability of phonological features during language acquisition using unsupervised binary stochastic autoencoders},
	url = {http://aclweb.org/anthology/N19-1007},
	doi = {10.18653/v1/N19-1007},
	abstract = {In this paper, we deploy binary stochastic neural autoencoder networks as models of infant language learning in two typologically unrelated languages (Xitsonga and English). We show that the drive to model auditory percepts leads to latent clusters that partially align with theory-driven phonemic categories. We further evaluate the degree to which theorydriven phonological features are encoded in the latent bit patterns, ﬁnding that some (e.g. [±approximant]), are well represented by the network in both languages, while others (e.g. [±spread glottis]) are less so. Together, these ﬁndings suggest that many reliable cues to phonemic structure are immediately available to infants from bottom-up perceptual characteristics alone, but that these cues must eventually be supplemented by top-down lexical and phonotactic information to achieve adult-like phone discrimination. Our results also suggest differences in degree of perceptual availability between features, yielding testable predictions as to which features might depend more or less heavily on top-down cues during child language acquisition.},
	language = {en},
	urldate = {2023-03-25},
	booktitle = {Proceedings of the 2019 {Conference} of the {North}},
	publisher = {Association for Computational Linguistics},
	author = {Shain, Cory and Elsner, Micha},
	year = {2019},
	pages = {69--85},
	file = {networkx_reference.pdf:D\:\\4.文库\\Zot\\storage\\WVC6H2D6\\networkx_reference.pdf:application/pdf;Shain 和 Elsner - 2019 - Measuring the perceptual availability of phonologi.pdf:D\:\\4.文库\\Zot\\storage\\HBCHHSMH\\Shain 和 Elsner - 2019 - Measuring the perceptual availability of phonologi.pdf:application/pdf},
}

@article{batsuren_large_2022,
	title = {A large and evolving cognate database},
	volume = {56},
	issn = {1574-020X, 1574-0218},
	url = {https://link.springer.com/10.1007/s10579-021-09544-6},
	doi = {10.1007/s10579-021-09544-6},
	abstract = {We present CogNet, a large-scale, automatically-built database of sensetagged cognates—words of common origin and meaning across languages. CogNet is continuously evolving: its current version contains over 8 million cognate pairs over 338 languages and 35 writing systems, with new releases already in preparation. The paper presents the algorithm and input resources used for its computation, an evaluation of the result, as well as a quantitative analysis of cognate data leading to novel insights on language diversity. Furthermore, as an example on the use of large-scale cross-lingual knowledge bases for improving the quality of multilingual applications, we present a case study on the use of CogNet for bilingual lexicon induction in the framework of cross-lingual transfer learning.},
	language = {en},
	number = {1},
	urldate = {2023-03-25},
	journal = {Language Resources and Evaluation},
	author = {Batsuren, Khuyagbaatar and Bella, Gábor and Giunchiglia, Fausto},
	month = mar,
	year = {2022},
	pages = {165--189},
	file = {Batsuren 等 - 2022 - A large and evolving cognate database.pdf:D\:\\4.文库\\Zot\\storage\\BP3EQSQ3\\Batsuren 等 - 2022 - A large and evolving cognate database.pdf:application/pdf},
}

@inproceedings{abboud_boxe_2020,
	title = {{BoxE}: {A} {Box} {Embedding} {Model} for {Knowledge} {Base} {Completion}},
	volume = {33},
	shorttitle = {{BoxE}},
	url = {https://proceedings.neurips.cc/paper/2020/hash/6dbbe6abe5f14af882ff977fc3f35501-Abstract.html},
	abstract = {Knowledge base completion (KBC) aims to automatically infer missing facts by exploiting information already present in a knowledge base (KB). A promising approach for KBC is to embed knowledge into latent spaces and make predictions from learned embeddings.  However, existing embedding models are subject to at least one of the following limitations: (1) theoretical inexpressivity, (2) lack of support for prominent inference patterns (e.g., hierarchies), (3) lack of support for KBC over higher-arity relations, and (4) lack of support for incorporating logical rules. Here, we propose a spatio-translational embedding model, called BoxE, that simultaneously addresses all these limitations.  BoxE embeds entities as points, and relations as a set of hyper-rectangles (or boxes), which spatially characterize basic logical properties. This seemingly simple abstraction yields a fully expressive model offering a natural encoding for many desired logical properties. BoxE can both capture and inject rules from rich classes of rule languages, going well beyond individual inference patterns.  By design, BoxE naturally applies to higher-arity KBs. We conduct a detailed experimental analysis, and show that BoxE achieves state-of-the-art performance, both on benchmark knowledge graphs and on more general KBs, and we empirically show the power of integrating logical rules.},
	urldate = {2023-03-25},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Abboud, Ralph and Ceylan, Ismail and Lukasiewicz, Thomas and Salvatori, Tommaso},
	year = {2020},
	pages = {9649--9661},
	file = {Full Text PDF:D\:\\4.文库\\Zot\\storage\\F5I5Y8ZV\\Abboud 等 - 2020 - BoxE A Box Embedding Model for Knowledge Base Com.pdf:application/pdf},
}

@inproceedings{begus_modeing_2020,
	address = {New York, New York},
	title = {Modeing unsupervised phonetic and phonological learning in {Generative} {Adversarial} {Phonology}},
	url = {https://aclanthology.org/2020.scil-1.5},
	urldate = {2023-03-25},
	booktitle = {Proceedings of the {Society} for {Computation} in {Linguistics} 2020},
	publisher = {Association for Computational Linguistics},
	author = {Begus, Gasper},
	month = jan,
	year = {2020},
	pages = {38--48},
	file = {Full Text PDF:D\:\\4.文库\\Zot\\storage\\Y35BJW59\\Begus - 2020 - Modeing unsupervised phonetic and phonological lea.pdf:application/pdf},
}


@inproceedings{bordes_translating_2013,
	title = {Translating {Embeddings} for {Modeling} {Multi}-relational {Data}},
	volume = {26},
	url = {https://proceedings.neurips.cc/paper_files/paper/2013/hash/1cecc7a77928ca8133fa24680a88d2f9-Abstract.html},
	abstract = {We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces. Our objective is to propose a canonical model which is easy to train, contains a reduced number of parameters and can scale up to very large databases. Hence, we propose, TransE, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities. Despite its simplicity, this assumption proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases. Besides, it can be successfully trained on a large scale data set with 1M entities, 25k relationships and more than 17M training samples.},
	urldate = {2023-04-03},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Bordes, Antoine and Usunier, Nicolas and Garcia-Duran, Alberto and Weston, Jason and Yakhnenko, Oksana},
	year = {2013},
	file = {Full Text PDF:D\:\\4.文库\\Zot\\storage\\II4IGADS\\Bordes 等 - 2013 - Translating Embeddings for Modeling Multi-relation.pdf:application/pdf},
}

@misc{sun_rotate_2019,
	title = {{RotatE}: {Knowledge} {Graph} {Embedding} by {Relational} {Rotation} in {Complex} {Space}},
	shorttitle = {{RotatE}},
	url = {http://arxiv.org/abs/1902.10197},
	abstract = {We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links. The success of such a task heavily relies on the ability of modeling and inferring the patterns of (or between) the relations. In this paper, we present a new approach for knowledge graph embedding called RotatE, which is able to model and infer various relation patterns including: symmetry/antisymmetry, inversion, and composition. Specifically, the RotatE model defines each relation as a rotation from the source entity to the target entity in the complex vector space. In addition, we propose a novel self-adversarial negative sampling technique for efficiently and effectively training the RotatE model. Experimental results on multiple benchmark knowledge graphs show that the proposed RotatE model is not only scalable, but also able to infer and model various relation patterns and significantly outperform existing state-of-the-art models for link prediction.},
	urldate = {2023-04-03},
	publisher = {arXiv},
	author = {Sun, Zhiqing and Deng, Zhi-Hong and Nie, Jian-Yun and Tang, Jian},
	month = feb,
	year = {2019},
	note = {arXiv:1902.10197 [cs, stat]},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {arXiv Fulltext PDF:D\:\\4.文库\\Zot\\storage\\H6JYUKJL\\Sun 等 - 2019 - RotatE Knowledge Graph Embedding by Relational Ro.pdf:application/pdf;arXiv.org Snapshot:D\:\\4.文库\\Zot\\storage\\FPKZ3X9E\\1902.html:text/html},
}

@article{nickel_three-way_nodate,
	title = {A {Three}-{Way} {Model} for {Collective} {Learning} on {Multi}-{Relational} {Data}},
	abstract = {Relational learning is becoming increasingly important in many areas of application. Here, we present a novel approach to relational learning based on the factorization of a three-way tensor. We show that unlike other tensor approaches, our method is able to perform collective learning via the latent components of the model and provide an efﬁcient algorithm to compute the factorization. We substantiate our theoretical considerations regarding the collective learning capabilities of our model by the means of experiments on both a new dataset and a dataset commonly used in entity resolution. Furthermore, we show on common benchmark datasets that our approach achieves better or on-par results, if compared to current state-of-the-art relational learning solutions, while it is signiﬁcantly faster to compute.},
	language = {en},
	author = {Nickel, Maximilian and Tresp, Volker and Kriegel, Hans-Peter},
	file = {Nickel 等 - A Three-Way Model for Collective Learning on Multi.pdf:D\:\\4.文库\\Zot\\storage\\IXBS429E\\Nickel 等 - A Three-Way Model for Collective Learning on Multi.pdf:application/pdf},
}

@misc{yang_embedding_2015,
	title = {Embedding {Entities} and {Relations} for {Learning} and {Inference} in {Knowledge} {Bases}},
	url = {http://arxiv.org/abs/1412.6575},
	doi = {10.48550/arXiv.1412.6575},
	abstract = {We consider learning representations of entities and relations in KBs using the neural-embedding approach. We show that most existing models, including NTN (Socher et al., 2013) and TransE (Bordes et al., 2013b), can be generalized under a unified learning framework, where entities are low-dimensional vectors learned from a neural network and relations are bilinear and/or linear mapping functions. Under this framework, we compare a variety of embedding models on the link prediction task. We show that a simple bilinear formulation achieves new state-of-the-art results for the task (achieving a top-10 accuracy of 73.2\% vs. 54.7\% by TransE on Freebase). Furthermore, we introduce a novel approach that utilizes the learned relation embeddings to mine logical rules such as "BornInCity(a,b) and CityInCountry(b,c) ={\textgreater} Nationality(a,c)". We find that embeddings learned from the bilinear objective are particularly good at capturing relational semantics and that the composition of relations is characterized by matrix multiplication. More interestingly, we demonstrate that our embedding-based rule extraction approach successfully outperforms a state-of-the-art confidence-based rule mining approach in mining Horn rules that involve compositional reasoning.},
	urldate = {2023-04-03},
	publisher = {arXiv},
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@misc{trouillon_complex_2016,
	title = {Complex {Embeddings} for {Simple} {Link} {Prediction}},
	url = {http://arxiv.org/abs/1606.06357},
	doi = {10.48550/arXiv.1606.06357},
	abstract = {In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases. As in previous studies, we propose to solve this problem through latent factorization. However, here we make use of complex valued embeddings. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and antisymmetric relations. Compared to state-of-the-art models such as Neural Tensor Network and Holographic Embeddings, our approach based on complex embeddings is arguably simpler, as it only uses the Hermitian dot product, the complex counterpart of the standard dot product between real vectors. Our approach is scalable to large datasets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction benchmarks.},
	urldate = {2023-06-10},
	publisher = {arXiv},
	author = {Trouillon, Théo and Welbl, Johannes and Riedel, Sebastian and Gaussier, Éric and Bouchard, Guillaume},
	month = jun,
	year = {2016},
	note = {arXiv:1606.06357 [cs, stat]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {arXiv Fulltext PDF:D\:\\4.文库\\Zot\\storage\\HS8CBS69\\Trouillon 等 - 2016 - Complex Embeddings for Simple Link Prediction.pdf:application/pdf},
}

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