Representing Complex Knowledge for Exploration and Recommendation: The Case of Classical Music Information

2020 ◽  
Author(s):  
Pasquale Lisena ◽  
Raphäel Troncy
Keyword(s):  
Search Engine ◽  
Recommender System ◽  
Classical Music ◽  
Knowledge Graph ◽  
Conversational Agent ◽  
Good Practices ◽  
Complex Information ◽  
Main Challenge ◽  
Complex Knowledge ◽  
Music Information

In Digital Humanities, one of the main challenge consists in capturing the structure of complex information in data models and ontologies, in particular when connections between terms are not trivial. This is typically the case for librarian music data. In this chapter, we provide some good practices for representing complex knowledge using the DOREMUS ontology as an exemplary case. We also show various applications of a Knowledge Graph leveraging on the ontology, ranging from an exploratory search engine, a recommender system and a conversational agent enabling to answer classical music questions.

scholarly journals Knowledge Graphs for COVID-19: An Exploratory Review of the Current Landscape

2021 ◽  
Vol 11 (4) ◽  
pp. 300
Author(s):  
Avishek Chatterjee ◽  
Cosimo Nardi ◽  
Cary Oberije ◽  
Philippe Lambin
Keyword(s):  
Information Needs ◽  
Drug Repurposing ◽  
Research Literature ◽  
Google Scholar ◽  
Knowledge Graph ◽  
Search Term ◽  
Complex Information ◽  
Big Picture ◽  
Improving Patient Care ◽  
Keyword Searches

Background: Searching through the COVID-19 research literature to gain actionable clinical insight is a formidable task, even for experts. The usefulness of this corpus in terms of improving patient care is tied to the ability to see the big picture that emerges when the studies are seen in conjunction rather than in isolation. When the answer to a search query requires linking together multiple pieces of information across documents, simple keyword searches are insufficient. To answer such complex information needs, an innovative artificial intelligence (AI) technology named a knowledge graph (KG) could prove to be effective. Methods: We conducted an exploratory literature review of KG applications in the context of COVID-19. The search term used was “covid-19 knowledge graph”. In addition to PubMed, the first five pages of search results for Google Scholar and Google were considered for inclusion. Google Scholar was used to include non-peer-reviewed or non-indexed articles such as pre-prints and conference proceedings. Google was used to identify companies or consortiums active in this domain that have not published any literature, peer-reviewed or otherwise. Results: Our search yielded 34 results on PubMed and 50 results each on Google and Google Scholar. We found KGs being used for facilitating literature search, drug repurposing, clinical trial mapping, and risk factor analysis. Conclusions: Our synopses of these works make a compelling case for the utility of this nascent field of research.

scholarly journals Leveraging Domain Context for Question Answering Over Knowledge Graph

2019 ◽  
Vol 4 (4) ◽  
pp. 323-335 ◽  
Author(s):  
Peihao Tong ◽  
Qifan Zhang ◽  
Junjie Yao
Keyword(s):  
Information Needs ◽  
Question Answering ◽  
Knowledge Graph ◽  
New Approach ◽  
Rich Domain ◽  
Complex Information ◽  
Question And Answer ◽  
Knowledge Graphs ◽  
The Rich ◽  
Matching Models

Abstract With the growing availability of different knowledge graphs in a variety of domains, question answering over knowledge graph (KG-QA) becomes a prevalent information retrieval approach. Current KG-QA methods usually resort to semantic parsing, search or neural matching models. However, they cannot well tackle increasingly long input questions and complex information needs. In this work, we propose a new KG-QA approach, leveraging the rich domain context in the knowledge graph. We incorporate the new approach with question and answer domain context descriptions. Specifically, for questions, we enrich them with users’ subsequent input questions within a session and expand the input question representation. For the candidate answers, we equip them with surrounding context structures, i.e., meta-paths within the targeting knowledge graph. On top of these, we design a cross-attention mechanism to improve the question and answer matching performance. An experimental study on real datasets verifies these improvements. The new approach is especially beneficial for specific knowledge graphs with complex questions.

FuhSen: A Federated Hybrid Search Engine for Building a Knowledge Graph On-Demand (Short Paper)

2016 ◽  
pp. 752-761 ◽  
Author(s):  
Diego Collarana ◽  
Mikhail Galkin ◽  
Christoph Lange ◽  
Irlán Grangel-González ◽  
Maria-Esther Vidal ◽  
...  
Keyword(s):  
Search Engine ◽  
Short Paper ◽  
Knowledge Graph ◽  
Hybrid Search ◽  
On Demand

scholarly journals Knowledge Graph Construction and Applications for Web Search and Beyond

2019 ◽  
Vol 1 (4) ◽  
pp. 333-349 ◽  
Author(s):  
Peilu Wang ◽  
Hao Jiang ◽  
Jingfang Xu ◽  
Qi Zhang
Keyword(s):  
Search Engine ◽  
Intelligent Systems ◽  
Large Scale ◽  
Web Search ◽  
Question Answering ◽  
Knowledge Graph ◽  
Graph Databases ◽  
Distributed Search ◽  
Knowledge Based ◽  
Dialog System

Knowledge graph (KG) has played an important role in enhancing the performance of many intelligent systems. In this paper, we introduce the solution of building a large-scale multi-source knowledge graph from scratch in Sogou Inc., including its architecture, technical implementation and applications. Unlike previous works that build knowledge graph with graph databases, we build the knowledge graph on top of SogouQdb, a distributed search engine developed by Sogou Web Search Department, which can be easily scaled to support petabytes of data. As a supplement to the search engine, we also introduce a series of models to support inference and graph based querying. Currently, the data of Sogou knowledge graph that are collected from 136 different websites and constantly updated consist of 54 million entities and over 600 million entity links. We also introduce three applications of knowledge graph in Sogou Inc.: entity detection and linking, knowledge based question answering and knowledge based dialog system. These applications have been used in Web search products to help user acquire information more efficiently.

Hybrid Recommender System for Music Information Retrieval

2020 ◽  
Vol 17 (9) ◽  
pp. 4145-4149
Author(s):  
A. N. Myna ◽  
K. Deepthi ◽  
Samvrudhi V. Shankar
Keyword(s):  
Recommender System ◽  
New Technologies ◽  
User Preferences ◽  
Easy Access ◽  
Acoustical Properties ◽  
Integral Role ◽  
User Data ◽  
Data History ◽  
Music Information ◽  
Hybrid Recommender

Music plays an integral role in our lives as the most popular type of recreation. With the advent of new technologies such as Internet and portable media players, large amount of music data is available online which can be distributed and easily made available to people. Enormous amount of music data is released every year by several artists with songs varying in features, genre and so on. Because of this, a need for reliable and easy access of songs based on user preferences is necessary. The recommender system focuses on generating playlists based on the physical, perceptual and acoustical properties of the song (content based filtering approach), or on commonalities between users on a particular basis like ratings or user data history (collaborative filtering). The system thus developed is a hybrid music recommender tool which creates a user centric suggestion system accompanied by feature extraction which in turn enhances the accuracy of music recommendations.

scholarly journals Conversations with Search Engines: SERP-based Conversational Response Generation

2021 ◽  
Vol 39 (4) ◽  
pp. 1-29
Author(s):  
Pengjie Ren ◽  
Zhumin Chen ◽  
Zhaochun Ren ◽  
Evangelos Kanoulas ◽  
Christof Monz ◽  
...  
Keyword(s):  
Natural Language ◽  
Search Engines ◽  
Information Needs ◽  
State Of The Art ◽  
User Studies ◽  
Future Research ◽  
System Response ◽  
List Type ◽  
Conversational Agent ◽  
Complex Information

In this article, we address the problem of answering complex information needs by conducting conversations with search engines , in the sense that users can express their queries in natural language and directly receive the information they need from a short system response in a conversational manner. Recently, there have been some attempts towards a similar goal, e.g., studies on Conversational Agent s (CAs) and Conversational Search (CS). However, they either do not address complex information needs in search scenarios or they are limited to the development of conceptual frameworks and/or laboratory-based user studies. We pursue two goals in this article: (1) the creation of a suitable dataset, the Search as a Conversation (SaaC) dataset, for the development of pipelines for conversations with search engines, and (2) the development of a state-of-the-art pipeline for conversations with search engines, Conversations with Search Engines (CaSE), using this dataset. SaaC is built based on a multi-turn conversational search dataset, where we further employ workers from a crowdsourcing platform to summarize each relevant passage into a short, conversational response. CaSE enhances the state-of-the-art by introducing a supporting token identification module and a prior-aware pointer generator, which enables us to generate more accurate responses. We carry out experiments to show that CaSE is able to outperform strong baselines. We also conduct extensive analyses on the SaaC dataset to show where there is room for further improvement beyond CaSE. Finally, we release the SaaC dataset and the code for CaSE and all models used for comparison to facilitate future research on this topic.

scholarly journals Knowledge Graph Transfer Network for Few-Shot Recognition

2020 ◽  
Vol 34 (07) ◽  
pp. 10575-10582
Author(s):  
Riquan Chen ◽  
Tianshui Chen ◽  
Xiaolu Hui ◽  
Hefeng Wu ◽  
Guanbin Li ◽  
...  
Keyword(s):  
Information Transfer ◽  
Knowledge Graph ◽  
Propagation Mechanism ◽  
The Novel ◽  
Main Challenge ◽  
Training Samples ◽  
Proposed Model ◽  
Significant Performance ◽  
Structured Knowledge ◽  
Novel Concept

Few-shot learning aims to learn novel categories from very few samples given some base categories with sufficient training samples. The main challenge of this task is the novel categories are prone to dominated by color, texture, shape of the object or background context (namely specificity), which are distinct for the given few training samples but not common for the corresponding categories (see Figure 1). Fortunately, we find that transferring information of the correlated based categories can help learn the novel concepts and thus avoid the novel concept being dominated by the specificity. Besides, incorporating semantic correlations among different categories can effectively regularize this information transfer. In this work, we represent the semantic correlations in the form of structured knowledge graph and integrate this graph into deep neural networks to promote few-shot learning by a novel Knowledge Graph Transfer Network (KGTN). Specifically, by initializing each node with the classifier weight of the corresponding category, a propagation mechanism is learned to adaptively propagate node message through the graph to explore node interaction and transfer classifier information of the base categories to those of the novel ones. Extensive experiments on the ImageNet dataset show significant performance improvement compared with current leading competitors. Furthermore, we construct an ImageNet-6K dataset that covers larger scale categories, i.e, 6,000 categories, and experiments on this dataset further demonstrate the effectiveness of our proposed model.

Erasmus+ Educational Projects on eLearning and Related Methodologies

2021 ◽  
pp. 111-133
Author(s):  
María Goretti Alonso de Castro ◽  
Francisco José García-Peñalvo
Keyword(s):  
European Union ◽  
Search Engine ◽  
Good Practice ◽  
The European Union ◽  
Success Story ◽  
Teaching Process ◽  
Good Practices ◽  
Definition Of ◽  
Adequate Analysis

Collecting data from Erasmus+ projects to detect those that have been identified as good practice or success story could be very useful in order to help teachers to define successful projects in a particular field. To compile projects of interest, the Erasmus+ Project Results Platform is available, which has a database with very useful information to locate educational projects that have been funded by the European Union. The advantage of using this tool is that it has a search engine that allows anyone to look up for keywords. Moreover, it permits to define different criteria so as to identify good practices projects that could serve as a reference in order to find useful parameters to improve the teaching process. This chapter presents the main data collected from educational projects that are related to eLearning and related methodologies in the aforementioned platform. It also defines which ones will be selected so as to be able to undertake an adequate analysis that allows the definition of a methodological guide to be carried out.

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