Learning Hierarchical Relationships among Partially Ordered Objects with Heterogeneous Attributes and Links

Wang, Chi; Han, Jiawei; Li, Qi; Li, Xiang; Lin, Weitao; Ji, Heng

doi:10.1137/1.9781611972825.45

Cited by 12 publications

(7 citation statements)

References 28 publications

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“…Statistical pattern mining [El-Kishky et al, 2015;Danilevsky et al, 2014; Supervised chunking trained from Penn Treebank Topic hierarchy / Taxonomy construction Combine statistical pattern mining with information networks [Wang et al, 2014] Lexical/Syntactic patterns (e.g., COLING2014 workshop on taxonomy construction) Entity Linking Graph alignment [Li et al, 2013] TAC-KBP Entity Linking methods and Wikification Relation discovery Hierarchical clustering [Wang et al, 2012] ACE relation extraction, bootstrapping Sentiment Analysis…”

Section: Nlp Methods Phrase Mining / Chunkingmentioning

confidence: 99%

Successful Data Mining Methods for NLP

Sun

2015

Tutorials

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Historically Natural Language Processing (NLP) focuses on unstructured data (speech and text) understanding while Data Mining (DM) mainly focuses on massive, structured or semi-structured datasets. The general research directions of these two fields also have followed different philosophies and principles. For example, NLP aims at deep understanding of individual words, phrases and sentences ("micro-level"), whereas DM aims to conduct a high-level understanding, discovery and synthesis of the most salient information from a large set of documents when working on text data ("macro-level"). But they share the same goal of distilling knowledge from data. In the past five years, these two areas have had intensive interactions and thus mutually enhanced each other through many successful text mining tasks. This positive progress mainly benefits from some innovative intermediate representations such as "heterogeneous information networks" [Han et al., 2010, Sun et al., 2012b.However, successful collaborations between any two fields require substantial mutual understanding, patience and passion among researchers. Similar to the applications of machine learning techniques in NLP, there is usually a gap of at least several years between the creation of a new DM approach and its first successful application in NLP. More importantly, many DM approaches such as gSpan [Yan and Han, 2002] and RankClus [Sun et al., 2009a] have demonstrated their power on structured data. But they remain relatively unknown in the NLP community, even though there are many obvious potential applications. On the other hand, compared to DM, the NLP community has paid more attention to developing large-scale data annotations, resources, shared tasks which cover a wide range of multiple genres and multiple domains. NLP can also provide the basic building blocks for many DM tasks such as text cube construction [Tao et al., 2014]. Therefore in many scenarios, for the same approach the NLP experiment setting is often much closer to real-world applications than its DM counterpart. We would like to share the experiences and lessons from our extensive inter-disciplinary collaborations in the past five years. The primary goal of this tutorial is to bridge the knowledge gap between these two fields and speed up the transition process. We will introduce two types of DM methods: (1). those state-of-the-art DM methods that have already been proven effective for NLP; and (2). some newly developed DM methods that we believe will fit into some specific NLP problems. In addition, we aim to suggest some new research directions in order to better marry these two areas and lead to more fruitful outcomes. The tutorial will thus be useful for researchers from both communities. We will try to provide a concise roadmap of recent perspectives and results, as well as point to the related DM software and resources, and NLP data sets that are available to both research communities. OutlineWe will focus on the following three perspectives. Where do NLP and DM MeetWe will first...

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Section: Nlp Methods Phrase Mining / Chunkingmentioning

confidence: 99%

Successful Data Mining Methods for NLP

Sun

2015

Tutorials

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“…Relationship extraction is another important step to form links among objects in network. Wang et al [173] mine hidden advisor-advisee relationships from bibliographic data, and they further infer hierarchical relationships among partially ordered objects with heterogeneous attributes and links [179]. Broadly speaking, we can also extract entity and relationship to construct heterogeneous network from multimedia data and multilingual data, as we have done on text data.…”

Section: A More Complex Network Constructionmentioning

confidence: 99%

A survey of heterogeneous information network analysis

Shi

Zhang

et al. 2017

IEEE Trans. Knowl. Data Eng.

928

457

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Most real systems consist of a large number of interacting, multi-typed components, while most contemporary researches model them as homogeneous networks, without distinguishing different types of objects and links in the networks. Recently, more and more researchers begin to consider these interconnected, multi-typed data as heterogeneous information networks, and develop structural analysis approaches by leveraging the rich semantic meaning of structural types of objects and links in the networks. Compared to widely studied homogeneous network, the heterogeneous information network contains richer structure and semantic information, which provides plenty of opportunities as well as a lot of challenges for data mining. In this paper, we provide a survey of heterogeneous information network analysis. We will introduce basic concepts of heterogeneous information network analysis, examine its developments on different data mining tasks, discuss some advanced topics, and point out some future research directions. Index Termsheterogeneous information network, data mining, semi-structural data, meta path

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“…It successfully mines advisor-advisee hidden roles in the DBLP database with high accuracy. Such mechanism can be further developed to discover hierarchical relationships [26] and ontology among objects under different kinds of user-provided constraints or rules.…”

Section: Role Discovery In Information Networkmentioning

confidence: 99%

Mining heterogeneous information networks

Sun

Han

2013

SIGKDD Explor. Newsl.

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528

216

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Most objects and data in the real world are of multiple types, interconnected, forming complex, heterogeneous but often semi-structured information networks. However, most network science researchers are focused on homogeneous networks, without distinguishing different types of objects and links in the networks. We view interconnected, multityped data, including the typical relational database data, as heterogeneous information networks, study how to leverage the rich semantic meaning of structural types of objects and links in the networks, and develop a structural analysis approach on mining semi-structured, multi-typed heterogeneous information networks. In this article, we summarize a set of methodologies that can effectively and efficiently mine useful knowledge from such information networks, and point out some promising research directions.

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Learning Hierarchical Relationships among Partially Ordered Objects with Heterogeneous Attributes and Links

Cited by 12 publications

References 28 publications

Successful Data Mining Methods for NLP

Successful Data Mining Methods for NLP

A survey of heterogeneous information network analysis

Mining heterogeneous information networks

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