Constructing topical hierarchies in heterogeneous information networks

Wang, Chi; Liu, Jialu; Desai, Nihit; Danilevsky, Marina

doi:10.1007/s10115-014-0777-4

Cited by 26 publications

(22 citation statements)

References 15 publications

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“…All of these work focused on topic hierarchy construction from a text corpus, while our fucus is incremental learning from news events. We first present an recursive algorithm for topic hierarchy construction similar to [16,25] based on a new topic model EETM and then propose an incremental hierarchical topic alignment algorithm to incrementally integrate the topic hierarchies to learn the target comprehensive topic hierarchy. The recursive algorithm is much more efficient than hierarchical topic models and the incremental algorithm can deal with new coming events easily.…”

Section: Topic Hierarchy Constructionmentioning

confidence: 99%

“…However, in this work, a topic can be represented by multityped objects, i.e., words, entities and entity types. Since entity types are not semantically meaningful, we consider word-word co-occurrence (W-W), entity-entity co-occurrence (E-E) and word-entity co-occurrence (W-E) in topics [16]. Suppose a topic is described using N words W ðtÞ and N entities, E ðtÞ ¼ fe In this part, we present a set of experiments designed to evaluate the efficacy of subtopic discovery (i.e., Steps 2-3 in Section 3.1).…”

Section: Evaluation Measuresmentioning

confidence: 99%

“…Therefore, in this work, we propose a simple two-phrase framework to address the problem. In the first phase, similar to [16], we present an efficient algorithm to recursively construct topic hierarchies for each event based on a novel topic model EETM (Event Entity Topic Model). The model models a topic as three distributions: one over words, one over named entities and one over named entity types.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Incremental learning from news events

Shao

et al. 2015

Knowledge-Based Systems

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Section: Topic Hierarchy Constructionmentioning

confidence: 99%

Section: Evaluation Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Incremental learning from news events

Shao

et al. 2015

Knowledge-Based Systems

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“…And Chen et al [81] propose a probabilistic generative model to simultaneously achieve clustering and ranking on a heterogeneous network with arbitrary schema. To make use of both textual information and heterogeneous linked entities, Wang et al [82] develop a clustering and ranking algorithm to automatically construct multi-typed topical hierarchies. What's more, Qiu et al [83] propose an algorithm OcdRank to combine overlapping community detection and community-member ranking together in directed heterogeneous social networks.…”

Section: B Clusteringmentioning

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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“…The output is a hierarchy of topics (concepts) where each topic in the tree is a coherent theme, represented by either a single word of a set of words. In recent years, hierarchical topic model research has focused on identifying a hierarchical tree of topics within documents [71,66,107,92,100]. Blei et al.…”

Section: Introductionmentioning

confidence: 99%

Structured sentiment analysis in social media

Almars¹

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In recent years, social media platforms, such as Twitter and Webio, have become popular sources of information on the web. These platforms contain a wealth of valuable information about user opinions, user interests, events and more. People typically use these platforms to discuss different topics, share their opinions about them and engage in question-andanswer sessions. For example, regarding smartphones, users might discuss the main aspects of a smartphone, such as the overall design, battery capacity, screen size and camera. The natural hierarchical structure of those concepts is often hidden in social media. Discovering the hidden structure can helps users understand people' preference to a certain topic at different levels of granularity, and show the reasons why they prefer this topic. Over the past decade, research on hierarchical topic models has shown considerable progress. However, these studies may not always be directly applicable to social media due to the shortness and the shallow meaning of social media messages.There are three major challenges when dealing with social media texts. Firstly, compared with traditionally long texts, social media texts suffer from sparsity, and this issue may result in an incomprehensible and incorrect concept hierarchy. Secondly, social media contains useful information such as social opinions and information about users. Most existing methods perform a flat sentiment analysis on each extracted aspects independently, and ignore the concept hierarchy. In fact, we need to make the sentiment analysis finegrained in order to simultaneously extract the aspects and summarise people' opinions on those discovered aspects. Thirdly, the current models only discover the concept hierarchy ignoring the community structure of users. Maintaining the consistency of user's interest on several communities according to various topics and sentiment information is a challenging problem.In this thesis, the limitations of the existing work are addressed and effective solutions are proposed. First, in order to discover the hierarchical structure of social media content, a novel approach called the context coherence model (CCM) is proposed. It recursively top ii down: (1) organizes the concepts discussed by users in social media texts; and (2) identifies the hierarchical relations among concepts. In the CCM, a new measurement called context coherence is introduced that analyses words in social media texts and determines the similarities among them. Then, the hierarchical relationship between words is determined by recursively partitioning the whole corpus into smaller parts according to the similarity results. Finally, a merging operation is performed to find similar words, group them under the same topic and remove duplicated topics. The approach is evaluated on two real-world data sets. The experiments show that the proposed approach can effectively reveal the hidden structure in social media.Opinions are now reflected in social media on a wide range of topics: trends in pop music, fashion...

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Constructing topical hierarchies in heterogeneous information networks

Cited by 26 publications

References 15 publications

Incremental learning from news events

Incremental learning from news events

A survey of heterogeneous information network analysis

Structured sentiment analysis in social media

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