596,053 research outputs found
Relationship based Entity Recommendation System
With the increase in usage of the internet as a place to search for information, the importance of the level of relevance of the results returned by search engines have increased by many folds in recent years. In this paper, we propose techniques to improve the relevance of results shown by a search engine, by using the kinds of relationships between entities a user is interested in. We propose a technique that uses relationships between entities to recommend related entities from a knowledge base which is a collection of entities and the relationships with which they are connected to other entities. These relationships depict more real world relationships between entities, rather than just simple “is-a” or “has-a” relationships. The system keeps track of relationships on which user is clicking and uses this click count as a preference indicator to recommend future entities. This approach is very useful in modern day semantic web searches for recommending entities of user’s interests
SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting
Learning knowledge graph (KG) embeddings is an emerging technique for a
variety of downstream tasks such as summarization, link prediction, information
retrieval, and question answering. However, most existing KG embedding models
neglect space and, therefore, do not perform well when applied to (geo)spatial
data and tasks. For those models that consider space, most of them primarily
rely on some notions of distance. These models suffer from higher computational
complexity during training while still losing information beyond the relative
distance between entities. In this work, we propose a location-aware KG
embedding model called SE-KGE. It directly encodes spatial information such as
point coordinates or bounding boxes of geographic entities into the KG
embedding space. The resulting model is capable of handling different types of
spatial reasoning. We also construct a geographic knowledge graph as well as a
set of geographic query-answer pairs called DBGeo to evaluate the performance
of SE-KGE in comparison to multiple baselines. Evaluation results show that
SE-KGE outperforms these baselines on the DBGeo dataset for geographic logic
query answering task. This demonstrates the effectiveness of our
spatially-explicit model and the importance of considering the scale of
different geographic entities. Finally, we introduce a novel downstream task
called spatial semantic lifting which links an arbitrary location in the study
area to entities in the KG via some relations. Evaluation on DBGeo shows that
our model outperforms the baseline by a substantial margin.Comment: Accepted to Transactions in GI
Measuring Expert Performance at Manually Classifying Domain Entities under Upper Ontology Classes
Classifying entities in domain ontologies under upper ontology classes is a
recommended task in ontology engineering to facilitate semantic
interoperability and modelling consistency. Integrating upper ontologies this
way is difficult and, despite emerging automated methods, remains a largely
manual task.
Little is known about how well experts perform at upper ontology integration.
To develop methodological and tool support, we first need to understand how
well experts do this task. We designed a study to measure the performance of
human experts at manually classifying classes in a general knowledge domain
ontology with entities in the Basic Formal Ontology (BFO), an upper ontology
used widely in the biomedical domain.
We conclude that manually classifying domain entities under upper ontology
classes is indeed very difficult to do correctly. Given the importance of the
task and the high degree of inconsistent classifications we encountered, we
further conclude that it is necessary to improve the methodological framework
surrounding the manual integration of domain and upper ontologies
Knowledge graph-based entity importance learning for multi-stream regression on Australian fuel price forecasting
© 2019 IEEE. A knowledge graph (KG) represents a collection of interlinked descriptions of entities. It has become a key focus for organising and utilising this type of data for applications. Many graph embedding techniques have been proposed to simplify the manipulation while preserving the inherent structure of the KG. However, scant attention has been given to the investigation of the importance of the entities (the nodes of KGs). In this paper, we propose a novel entities importance learning framework that investigates how to weight the entities and use them as a prior knowledge for solving multi-stream regression problems. The framework consists of KG feature extraction, multi-stream correlation analysis, and entity importance learning. To evaluate the proposed method, we implemented the framework based on Wikidata and applied it to Australian retail fuel price forecasting. The experiment results indicate that the proposed method reduces prediction error, which supports the weighted knowledge graph information as a means for improving machine learning model accuracy
Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
How can we estimate the importance of nodes in a knowledge graph (KG)? A KG
is a multi-relational graph that has proven valuable for many tasks including
question answering and semantic search. In this paper, we present GENI, a
method for tackling the problem of estimating node importance in KGs, which
enables several downstream applications such as item recommendation and
resource allocation. While a number of approaches have been developed to
address this problem for general graphs, they do not fully utilize information
available in KGs, or lack flexibility needed to model complex relationship
between entities and their importance. To address these limitations, we explore
supervised machine learning algorithms. In particular, building upon recent
advancement of graph neural networks (GNNs), we develop GENI, a GNN-based
method designed to deal with distinctive challenges involved with predicting
node importance in KGs. Our method performs an aggregation of importance scores
instead of aggregating node embeddings via predicate-aware attention mechanism
and flexible centrality adjustment. In our evaluation of GENI and existing
methods on predicting node importance in real-world KGs with different
characteristics, GENI achieves 5-17% higher NDCG@100 than the state of the art.Comment: KDD 2019 Research Track. 11 pages. Changelog: Type 3 font removed,
and minor updates made in the Appendix (v2
Career management perspective in public administration
An innovative public organization must be capable to access, internalize and implement the newest forms of knowledge and capitalize efficiently and effectively the potential of human resources available to it. Economic, political, social, administrative, organizational changes in the context of the global crisis impose permanent efforts aimed to ensure flexibility and to redesign the public organizational architecture, adaptation of career management systems to new conditions. Public organisational entities make substantial efforts to increase the quality of public services, performances and their innovative capacity, using as much as possible the employers’ potential and talent. The paper explores the importance of the concept, role, objectives and management of career from both individual and public organizational perspective. We try to identify the career features and its innovative role in the knowledge-based economy during the crisis, considering the fact that public services have in fact the ultimate responsibility for managing their own careers.public career management, organizational developement, innovation, crisis, knowledge-based management
Contextual information and assessor characteristics in complex question answering
The ciqa track investigates the role of interaction in answering complex questions: questions that relate two or more entities by some specified relationship. In our submission to the first ciqa track we were interested in the interplay between groups of variables: variables describing the question creators, the questions asked and the presentation of answers to the questions. We used two interaction forms - html questionnaires completed before answer assessment - to gain contextual information from the answer assessors to better understand what factors influence assessors when judging retrieved answers to complex questions. Our results indicate the importance of understanding the assessor's personal relationship to the question - their existing topical knowledge for example - and also the presentation of the answers - contextual information about the answer to aid in the assessment of the answer
- …