Distributed representation learning for knowledge graphs with entity descriptions

Fan, Miao; Zhou, Qiang; Zheng, Thomas Fang; Grishman, Ralph

doi:10.1016/j.patrec.2016.09.005

Cited by 41 publications

(16 citation statements)

References 3 publications

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“…The alignment by entity description has been proved better than the other two alignment mechanisms in the experiment. RLKB [28] proposes a method of jointly embedding the entities, relations and words in entity descriptions in the same vector space. Following Jointly, RLKB designs the L K based on TransE to measure the fitness of facts.…”

Section: Joint Embedding Of the Texts And Factsmentioning

confidence: 99%

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Utilizing Textual Information in Knowledge Graph Embedding: A Survey of Methods and Applications

Cong

Huang

2020

IEEE Access

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Techniques that map the entities and relations of the knowledge graph (KG) into a low-dimensional continuous space are called KG embedding or knowledge representation learning. However, most existing techniques learn the embeddings based on the facts in KG alone, suffering from the issues of imperfection and spareness of KG. Recently, the research on textual information in KG embedding has attracted much attention due to the rich semantic information supplied by the texts. Thus, in this paper, a survey of techniques for textual information based KG embedding is proposed. Firstly, we introduce the techniques for encoding the textual information to represent the entities and relations from perspectives of encoding models and scoring functions, respectively. Secondly, methods for incorporating the textual information in the existing embedding techniques are summarized. Thirdly, we discuss the training procedure of textual information based KG embedding techniques. Finally, applications of KG embedding with textual information in the specific tasks such as KG completion in zero-shot scenario, multilingual entity alignment, relation extraction and recommender system are explored. We hope that this survey will give insights to researchers into textual information based KG embedding. INDEX TERMS Knowledge graph embedding, textual information, text-based embedding, text-improved embedding, embedding-based applications.

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Section: Joint Embedding Of the Texts And Factsmentioning

confidence: 99%

“…Jointly [26] and RKLB [28] use negative sampling to simplify the loss function. The likelihood-based loss is tough to do the normalization for the millions of normalizers and the large number of candidate-entities influence the efficiency of training phrase.…”

Section: B Negative Samplingmentioning

confidence: 99%

Utilizing Textual Information in Knowledge Graph Embedding: A Survey of Methods and Applications

Cong

Huang

2020

IEEE Access

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“…There have been a number of previous studies on knowledge base completion. One promising approach in the studies is to use knowledge graph embedding [13][14][15][16]. Knowledge graph embedding represents all entities and relations of knowledge facts as low-dimensional vectors.…”

Section: Related Workmentioning

confidence: 99%

An Approach to Knowledge Base Completion by a Committee-Based Knowledge Graph Embedding

2020

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Knowledge bases such as Freebase, YAGO, DBPedia, and Nell contain a number of facts with various entities and relations. Since they store many facts, they are regarded as core resources for many natural language processing tasks. Nevertheless, they are not normally complete and have many missing facts. Such missing facts keep them from being used in diverse applications in spite of their usefulness. Therefore, it is significant to complete knowledge bases. Knowledge graph embedding is one of the promising approaches to completing a knowledge base and thus many variants of knowledge graph embedding have been proposed. It maps all entities and relations in knowledge base onto a low dimensional vector space. Then, candidate facts that are plausible in the space are determined as missing facts. However, any single knowledge graph embedding is insufficient to complete a knowledge base. As a solution to this problem, this paper defines knowledge base completion as a ranking task and proposes a committee-based knowledge graph embedding model for improving the performance of knowledge base completion. Since each knowledge graph embedding has its own idiosyncrasy, we make up a committee of various knowledge graph embeddings to reflect various perspectives. After ranking all candidate facts according to their plausibility computed by the committee, the top-k facts are chosen as missing facts. Our experimental results on two data sets show that the proposed model achieves higher performance than any single knowledge graph embedding and shows robust performances regardless of k. These results prove that the proposed model considers various perspectives in measuring the plausibility of candidate facts.

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“…Reasoning [9] assumes a more prominent part in the process of knowledge representation [10]. Therefore finding the occurrence of a concept and getting their relation with other concept fetches the model [11].…”

Section: Algorithm For Traversing Nodesmentioning

confidence: 99%

A Knowledge Representation Model using Concept-Relation Graph

P.¹,

Selwyn²

2019

IJEAT

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Huge volume of relevant and irrelevant information is available from different sources for gaining knowledge about a system. If the required data is in a structured form, then the fact can be easily understood otherwise the process becomes laborious and results are vague after intense analysis. In this paper, we are proposing a framework for fetching knowledge from unstructured source of data. The algorithms proposed identifies the concepts and separates the concepts and relation words, enables to add new concepts and also modify the old concept word with new concept and locate the concept. This Concept-Relation Model enables the system to work according to the users’ connivance and deliveries accurate knowledge. This model does not manipulate or interpret the information provided but only represent and share the desired knowledge.

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Distributed representation learning for knowledge graphs with entity descriptions

Cited by 41 publications

References 3 publications

Utilizing Textual Information in Knowledge Graph Embedding: A Survey of Methods and Applications

Utilizing Textual Information in Knowledge Graph Embedding: A Survey of Methods and Applications

An Approach to Knowledge Base Completion by a Committee-Based Knowledge Graph Embedding

A Knowledge Representation Model using Concept-Relation Graph

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