scholarly journals Enhancing Profile and Context Aware Relevant Food Search through Knowledge Graphs

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1228 ◽  
Author(s):  
Unai Zulaika ◽  
Asier Gutiérrez ◽  
Diego López-de-Ipiña
Keyword(s):  
Natural Language ◽  
Knowledge Graph ◽  
Human Intelligence ◽  
Context Aware ◽  
Food Items ◽  
Usable Information ◽  
Cognitive Services ◽  
Knowledge Graphs ◽  
Ibm Watson ◽  
Graph Based Model

Foodbar is a Cloud-based gastroevaluation solution, leveraging IBM Watson cognitive services. It brings together machine and human intelligence to enable cognitive gastroevaluation of “tapas” or “pintxos” , i.e., small miniature bites or dishes. Foodbar matchmakes users’ profiles, preferences and context against an elaborated knowledge graph based model of user and machine generated information about food items. This paper reasons about the suitability of this novel way of modelling heterogeneous, with diverse degree of veracity, information to offer more stakeholder satisfying knowledge exploitation solutions, i.e., those offering more relevant and elaborated, directly usable, information to those that want to take decisions regarding food in miniature. An evaluation of the information modelling power of such approach is performed highlighting why such model can offer better more relevant and enriched answers to natural language questions posed by users.

BUILD KNOWLEDGE GRAPH FROM HETEROGENEOUS DOCUMENTS

2021 ◽  
Vol 47 (05) ◽  
Author(s):  
NGUYỄN CHÍ HIẾU
Keyword(s):  
Information Retrieval ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
Semantic Analysis ◽  
Knowledge Graph ◽  
Question Answering Systems ◽  
Knowledge Graphs

Knowledge Graphs are applied in many fields such as search engines, semantic analysis, and question answering in recent years. However, there are many obstacles for building knowledge graphs as methodologies, data and tools. This paper introduces a novel methodology to build knowledge graph from heterogeneous documents.  We use the methodologies of Natural Language Processing and deep learning to build this graph. The knowledge graph can use in Question answering systems and Information retrieval especially in Computing domain

Graph and Neural Network-Based Intelligent Conversation System

2019 ◽  
pp. 339-357 ◽  
Author(s):  
Anuja Arora ◽  
Aman Srivastava ◽  
Shivam Bansal
Keyword(s):  
Deep Learning ◽  
Natural Language ◽  
System Approach ◽  
Domain Knowledge ◽  
Research Work ◽  
Graph Model ◽  
Machine Intelligence ◽  
Knowledge Graph ◽  
Feature Engineering ◽  
Graph Based Model

The conventional approach to build a chatbot system uses the sequence of complex algorithms and productivity of these systems depends on order and coherence of algorithms. This research work introduces and showcases a deep learning-based conversation system approach. The proposed approach is an intelligent conversation model approach which conceptually uses graph model and neural conversational model. The proposed deep learning-based conversation system uses neural conversational model over knowledge graph model in a hybrid manner. Graph-based model answers questions written in natural language using its intent in the knowledge graph and neural conversational model converses answer based on conversation content and conversation sequence order. NLP is used in graph model and neural conversational model uses natural language understanding and machine intelligence. The neural conversational model uses seq2seq framework as it requires less feature engineering and lacks domain knowledge. The results achieved through the authors' approach are competitive with solely used graph model results.

scholarly journals Few-Shot Knowledge Graph Completion

2020 ◽  
Vol 34 (03) ◽  
pp. 3041-3048 ◽  
Author(s):  
Chuxu Zhang ◽  
Huaxiu Yao ◽  
Chao Huang ◽  
Meng Jiang ◽  
Zhenhui Li ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
State Of The Art ◽  
Knowledge Graph ◽  
Graph Structure ◽  
Reference Set ◽  
Knowledge Graphs ◽  
Public Datasets ◽  
Relation Learning

Knowledge graphs (KGs) serve as useful resources for various natural language processing applications. Previous KG completion approaches require a large number of training instances (i.e., head-tail entity pairs) for every relation. The real case is that for most of the relations, very few entity pairs are available. Existing work of one-shot learning limits method generalizability for few-shot scenarios and does not fully use the supervisory information; however, few-shot KG completion has not been well studied yet. In this work, we propose a novel few-shot relation learning model (FSRL) that aims at discovering facts of new relations with few-shot references. FSRL can effectively capture knowledge from heterogeneous graph structure, aggregate representations of few-shot references, and match similar entity pairs of reference set for every relation. Extensive experiments on two public datasets demonstrate that FSRL outperforms the state-of-the-art.

scholarly journals Challenges, Techniques, and Trends of Simple Knowledge Graph Question Answering: A Survey

Information ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 271
Author(s):  
Mohammad Yani ◽  
Adila Alfa Krisnadhi
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Common Type ◽  
Knowledge Graph ◽  
Simple Question ◽  
Comprehensive Survey ◽  
Knowledge Graphs ◽  
Comprehensive Study

Simple questions are the most common type of questions used for evaluating a knowledge graph question answering (KGQA). A simple question is a question whose answer can be captured by a factoid statement with one relation or predicate. Knowledge graph question answering (KGQA) systems are systems whose aim is to automatically answer natural language questions (NLQs) over knowledge graphs (KGs). There are varieties of researches with different approaches in this area. However, the lack of a comprehensive study to focus on addressing simple questions from all aspects is tangible. In this paper, we present a comprehensive survey of answering simple questions to classify available techniques and compare their advantages and drawbacks in order to have better insights of existing issues and recommendations to direct future works.

Knowledge Graph Generation

2020 ◽  
pp. 115-121
Author(s):  
Anjali Daisy
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Link Prediction ◽  
Computer Systems ◽  
Knowledge Graph ◽  
Semantic Models ◽  
Knowledge Graphs ◽  
Vector Representations ◽  
Numerical Representations

Nowadays, as computer systems are expected to be intelligent, techniques that help modern applications to understand human languages are in much demand. Amongst all the techniques, the latent semantic models are the most important. They exploit the latent semantics of lexicons and concepts of human languages and transform them into tractable and machine-understandable numerical representations. Without that, languages are nothing but combinations of meaningless symbols for the machine. To provide such learning representation, embedding models for knowledge graphs have attracted much attention in recent years since they intuitively transform important concepts and entities in human languages into vector representations, and realize relational inferences among them via simple vector calculation. Such novel techniques have effectively resolved a few tasks like knowledge graph completion and link prediction, and show the great potential to be incorporated into more natural language processing (NLP) applications.

scholarly journals Learning Multi-faceted Knowledge Graph Embeddings for Natural Language Processing

2017 ◽  
Author(s):  
Muhao Chen ◽  
Carlo Zaniolo
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Wide Spectrum ◽  
Knowledge Graph ◽  
Graph Embeddings ◽  
Relational Properties ◽  
Knowledge Graphs ◽  
Novel Approaches

Knowledge graphs have challenged the present embedding-based approaches for representing their multifacetedness. To address some of the issues, we have investigated some novel approaches that (i) captures multilingual transitions on different language-specific versions of knowledge, and (ii) encodes the commonly existing monolingual knowledge with important relational properties and hierarchies. In addition, we propose the use of our approaches in a wide spectrum of NLP tasks that have not been well explored by related works.

Specifying Knowledge Graph with Data Graph, Information Graph, Knowledge Graph, and Wisdom Graph

2018 ◽  
Vol 6 (2) ◽  
pp. 10-25 ◽  
Author(s):  
Yucong Duan ◽  
Lixu Shao ◽  
Gongzhu Hu
Keyword(s):  
Natural Language ◽  
Complete Information ◽  
Knowledge Graph ◽  
Data Graph ◽  
Knowledge Graphs ◽  
Expression Form ◽  
Information Graph ◽  
Expression Mechanism

Knowledge graphs have been widely adopted, in large part owing to their schema-less nature. It enables knowledge graphs to grow seamlessly and allows for new relationships and entities as needed. A knowledge graph is a graph constructed by representing each item, entity and user as nodes, and linking those nodes that interact with each other via edges. Knowledge graphs have abundant natural semantics and can contain various and more complete information. It is an expression mechanism close to natural language. However, we still lack a unified definition and standard expression form of knowledge graph. The authors propose to clarify the expression of knowledge graph as a whole. They clarify the architecture of knowledge graph from data, information, knowledge, and wisdom aspects respectively. The authors also propose to specify knowledge graph in a progressive manner as four basic forms including data graph, information graph, knowledge graph and wisdom graph.

scholarly journals Vision–Language–Knowledge Co-Embedding for Visual Commonsense Reasoning

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2911
Author(s):  
JaeYun Lee ◽  
Incheol Kim
Keyword(s):  
Natural Language ◽  
Structural Information ◽  
Input Image ◽  
Knowledge Graph ◽  
Commonsense Reasoning ◽  
Proposed Model ◽  
Language Knowledge ◽  
Natural Language Question ◽  
Knowledge Graphs ◽  
Language Question

Visual commonsense reasoning is an intelligent task performed to decide the most appropriate answer to a question while providing the rationale or reason for the answer when an image, a natural language question, and candidate responses are given. For effective visual commonsense reasoning, both the knowledge acquisition problem and the multimodal alignment problem need to be solved. Therefore, we propose a novel Vision–Language–Knowledge Co-embedding (ViLaKC) model that extracts knowledge graphs relevant to the question from an external knowledge base, ConceptNet, and uses them together with the input image to answer the question. The proposed model uses a pretrained vision–language–knowledge embedding module, which co-embeds multimodal data including images, natural language texts, and knowledge graphs into a single feature vector. To reflect the structural information of the knowledge graph, the proposed model uses the graph convolutional neural network layer to embed the knowledge graph first and then uses multi-head self-attention layers to co-embed it with the image and natural language question. The effectiveness and performance of the proposed model are experimentally validated using the VCR v1.0 benchmark dataset.

Mobile Software Assurance Informed through Knowledge Graph Construction: The OWASP Threat of Insecure Data Storage

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Suzanna Schmeelk ◽  
Lixin Tao
Keyword(s):  
Data Storage ◽  
Program Analysis ◽  
Web Application ◽  
Security Analysis ◽  
Knowledge Graph ◽  
Healthcare Applications ◽  
Sensitive Data ◽  
Knowledge Graphs ◽  
Mobile Malware Detection ◽  
Software Assurance

Many organizations, to save costs, are movinheg to t Bring Your Own Mobile Device (BYOD) model and adopting applications built by third-parties at an unprecedented rate.  Our research examines software assurance methodologies specifically focusing on security analysis coverage of the program analysis for mobile malware detection, mitigation, and prevention.  This research focuses on secure software development of Android applications by developing knowledge graphs for threats reported by the Open Web Application Security Project (OWASP).  OWASP maintains lists of the top ten security threats to web and mobile applications.  We develop knowledge graphs based on the two most recent top ten threat years and show how the knowledge graph relationships can be discovered in mobile application source code.  We analyze 200+ healthcare applications from GitHub to gain an understanding of their software assurance of their developed software for one of the OWASP top ten moble threats, the threat of “Insecure Data Storage.”  We find that many of the applications are storing personally identifying information (PII) in potentially vulnerable places leaving users exposed to higher risks for the loss of their sensitive data.

scholarly journals TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1407
Author(s):  
Peng Wang ◽  
Jing Zhou ◽  
Yuzhang Liu ◽  
Xingchen Zhou
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Score Function ◽  
Graph Embedding ◽  
Vector Spaces ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Low Dimensional

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

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