because knowledge graphs are typically incomplete, it is useful to perform knowledge graph completion or link prediction, i.e. predict whether a relationship not in the knowledge graph is likely to be true.

When to use the Knowledge Graphs


This paper serves as a comprehensive survey of embedding models of entities and relationships for knowledge graph completion, summarizing up-to-date experimental results on standard benchmark datasets and pointing out potential future research directions.

Purpose of this survey

Knowledge graphs are collections of real-world triples, where each triple or fact (h, r, t) in KGs represents some relation r between a head entity h and a tail entity t. KGs can thus be formalized as directed multi-relational graphs, where nodes correspond to entities and edges linking the nodes encode various kinds of relationships.

Objects in the Knowledge graphs

Other KG Completion Model:

The Path Ranking Algorithm (PRA) is a random walk inference technique which was proposed to predict a new relationship between two entities in KGs

This model is able to estimate the probability of an unseen triple as a combination of weighted random walks that follow different paths linking the head entity and tail entity in the KG

Specific models

(i) Combining logical rules which contain rich background information and KG triples in a unified KG completion framework, e.g. jointly embedding KGs and logical rules

 (ii) Recent embedding models for KG completion hold a closed-world assumption where the KGs are fixed (i.e. new entities might not be added easily), therefore it would be worth exploring open-world KG completion models to connect unseen entities to the existing KGs. 

(iii) Investigating efficient approaches which can be applied to large-scale KGs of millions of entities and relation

Future research direction

University of Michigan - Ann Arbor

NLG, a subfield of artificial intelligence (AI), is a software process that automatically transforms data into plain-English content.

Natural language generation

Autocorrection is an automatic text validation tool commonly found in your phone's and computer's text editing interfaces. This tool can correct spelling errors and/or contextual errors depending on the algorithms.

Autocorrection

The technique (Shannon, 1951) generates a word in a sequence given the previous words, using cross-entropy as the loss function and perplexity as the evaluation method. The sequence ends once it reaches a predefined length. Each softmax layer is to decide the next word based on the current hidden state in the RNN component. The initial input token(s) would depend on the NLP task's nature (e.g. machine translation, question answering, summarization). 

RNN's Approach to Text Generation in NLP (Autoregressive Generation)

inform code-switching in multilingual contexts

A Survey of Code-switching: Linguistic and Social Perspectives for Language Technologie

Hämäläinen, M., & Alnajjar, K. (2021). Human Evaluation of Creative NLG Systems: An Interdisciplinary Survey on Recent Papers. arXiv preprint arXiv:2108.00308.

Human Evaluation of Creative NLG Systems: An Interdisciplinary Survey on Recent Papers

- To present a comprehensive overview of NLG developments since 2000.
- To provide researchers with synthesis and pointers to relevant research.
- To introduce the field to researchers who are less familiar.

Goals of the NLG survey

- Content determination:  Deciding which information to include in the text under construction.
- Text structuring: Determining in which order information will be presented in the text.
- Sentence aggregation: Deciding which information to present in individual sentences.
- Lexicalisation:  Finding the right words and phrases to express information.
- Referring expression generation:  Selecting the words and phrases to identify domain objects.

NLG tasks.

GRL Book

A survey of embedding models of entities and relationships for knowledge graph completion

In Natural Language Processing, text generation tasks that use existing text as input can be broadly divided into two main categories. The first is text completion, which involves continuing to write based on the provided text. The second is text transformation, which focuses on converting the input text into a new, different text.

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