ISWC2019 Papers

Artificial Intelligence Technology Semantic Web Technology Reasoning Technology Collecting AI Conference Papers Ontology Technology Machine Learning Digital Transformation Technology Knowledge Information Processing Technology
ISWC2019 Papers

From ISWC2019, an international conference on Semantic Web technology, one of the artificial intelligence technologies for handling knowledge information. In the previous article, we discussed ISWC2018.

ISWC2019 was held in Auckland (New Zealand), and similar to the previous ISWC2018, there were many reports on knowledge (graph) data and their application to Q&A systems, etc.

One report that caught our attention was “Logical Semantics Approach for Data Modeling in XBRL Taxonomies” (the technical areas of XBRL are Financial Reporting, Natural Language, and Taxonomy Modeling). He mentioned that XBRL’s future contribution to semantic technology will be in the utilization of large-scale data (including AI) in these technical areas), “Knowledge-based geospatial data integration and visualization with Semantic Web technologies” (a report on the implementation of geovisualization using Semantic Web technologies for geospatial information processing. The report describes the recent increase in demand for Spatial Data Infrastructure (SDI) in Europe and the U.S., and its contribution as a methodology for handling ontologies in geospatial space.) The report also includes “How to make latent factors interpretable by feeding Factorization machines with knowledge graphs” (a demonstration of the Hybrid Factorization Machine (kaHFM). It mentions the validation of how to initialize latent factors for the Factorization Machine using knowledge graphs to train interpretable models), “Summarizing News Articles using Question-and-Answer Pairs via Learning” (Google research presentation on the use of semantic technology in Q&A systems. The system mines questions from data associated with news stories and learns questions directly from the story content. This is the first demonstration of a learning-based approach to generating structured summaries of news stories with question-answer pairs to capture important and interesting aspects of a news story. The validation data is from the SQuAD dataset 2). and “Using a Knowledge Graph of Scenes to Enable the Search of Autonomous DrivingData” (describes a demonstration at Bosch using semantic technology. The intended use of the data is to provide large-scale data for automated driving technology. Benefits include improved capability to represent, integrate, and query automated driving data. It is also anticipated that data scientists and engineers within various projects and departments will be able to reuse data from each other’s applications), “Using Event Graph to Improve Question Answering in E-commerce Customer Service” (describing AliMe, an intelligent assistant that provides a question-and-answer service, which can answer more than 90% of the millions of questions per day. This session proposed an Event Graph that provides a reasoning mechanism to obtain accurate answers to the question types “why”, “wherefore”, “what if”, and “how next”. Events are properties of choices of situations that can happen, and the baseline knowledge graph is generated using WIKIDATA, DBpedia, YAGO, etc.), “Querying Enterprise Knowledge Graph With Natural Language “(Describes research on interactive interfaces to large enterprise knowledge graphs. He calls this mechanism Yugen (a deep learning-based interactive AI that answers user questions). They are already working on cold start challenges and domain-specific data detection challenges for enterprises, etc. Yugen is voice-based, so the advantage is that the cost of learning a specific query language is reduced), “Product Classification Using Microdata Annotations” (which described the task of automatically classifying products into universal categories using markup data published on the Web (in this case, RDF and Microdata). The challenge would be to handle the information needed for classification (e.g., treatment of individual websites, consistency across websites, site-specific product labels, etc.). This will be an example of using RDF as input data for deep learning), “Difficulty-controllable Multi-hop Question Generation From Knowledge Graphs” (neural network-based He explained multi-hop questioning. A multi-hop question is a question that can only be answered by hopping a node in the graph structure two or more times. As a solution, he mentioned that he had implemented an encoder-decoder model conditional on the difficulty level and was able to generate complex questions via a large knowledge graph. However, it was difficult to achieve and seemed to require future research), “QaldGen: Towards Microbenchmarking of Question Answering Systems Over Knowledge Graphs” (testing domain-specific Q&A systems He mentioned that micro-benchmarking is necessary because of the time and effort required to generate questions when trying to do so. QaldGen was proposed as a framework for generating questions useful for micro-benchmarking), etc.

Details are given below. Translated with www.DeepL.com/Translator (free version)

research track
Decentralized Indexing over a Network of RDF Peers
Datalog Materialisation in Distributed RDF Stores with Dynamic Data Exchange
How to Make Latent Factors Interpretable by Feeding Factorization Machines with Knowledge Graphs
Observing LOD Using Equivalent Set Graphs: It Is Mostly Flat and Sparsely Linked
Optimizing Horn-SHIQ Reasoning for OBDA
Using a KG-Copy Network for Non-goal Oriented Dialogues
Canonicalizing Knowledge Base Literals
Bag Semantics of DL-Lite with Functionality Axioms
Validating Shacl Constraints over a Sparql Endpoint
Mapping Factoid Adjective Constraints to Existential Restrictions over Knowledge Bases
Mining Significant Maximum Cardinalities in Knowledge Bases
HapPenIng: Happen, Predict, Infer—Event Series Completion in a Knowledge Graph
Uncovering the Semantics of Wikipedia Categories
Qsearch: Answering Quantity Queries from Text
A Worst-Case Optimal Join Algorithm for SPARQL
Knowledge Graph Consolidation by Unifying Synonymous Relationships
Skyline Queries over Knowledge Graphs
Detecting Influences of Ontology Design Patterns in Biomedical Ontologies
Popularity-Driven Ontology Ranking Using Qualitative Features
Incorporating Literals into Knowledge Graph Embeddings
Extracting Novel Facts from Tables for Knowledge Graph Completion
Difficulty-Controllable Multi-hop Question Generation from Knowledge Graphs
Type Checking Program Code Using SHACL
Decentralized Reasoning on a Network of Aligned Ontologies with Link Keys
Ontology Completion Using Graph Convolutional Networks
Non-parametric Class Completeness Estimators for Collaborative Knowledge Graphs—The Case of Wikidata
Pretrained Transformers for Simple Question Answering over Knowledge Graphs
Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs
THOTH: Neural Translation and Enrichment of Knowledge Graphs
Entity Enabled Relation Linking
SHACL Constraints with Inference Rules
Query-Based Entity Comparison in Knowledge Graphs Revisited
Anytime Large-Scale Analytics of Linked Open Data
Absorption-Based Query Answering for Expressive Description Logics
TransEdge: Translating Relation-Contextualized Embeddings for Knowledge Graphs
Unsupervised Discovery of Corroborative Paths for Fact Validation
RDF Explorer: A Visual SPARQL Query Builder
Capturing Semantic and Syntactic Information for Link Prediction in Knowledge Graphs
A Framework for Evaluating Snippet Generation for Dataset Search
Summarizing News Articles Using Question-and-Answer Pairs via Learning
Product Classification Using Microdata Annotations
Truthful Mechanisms for Multi Agent Self-interested Correspondence Selection
The KEEN Universe
VLog: A Rule Engine for Knowledge Graphs
ArCo: The Italian Cultural Heritage Knowledge Graph
Making Study Populations Visible Through Knowledge Graphs
LC-QuAD 2.0: A Large Dataset for Complex Question Answering over Wikidata and DBpedia
SEO: A Scientific Events Data Model
DBpedia FlexiFusion the Best of Wikipedia > Wikidata > Your Data
The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of 
The RealEstateCore Ontology
FoodKG: A Semantics-Driven Knowledge Graph for Food Recommendation
BTC-2019: The 2019 Billion Triple Challenge Dataset
Extending the YAGO2 Knowledge Graph with Precise Geospatial Knowledge
The SEPSES Knowledge Graph: An Integrated Resource for Cybersecurity
SemanGit: A Linked Dataset from git
Squerall: Virtual Ontology-Based Access to Heterogeneous and Large Data Sources
List.MID: A MIDI-Based Benchmark for Evaluating RDF Lists
A Scalable Framework for Quality Assessment of RDF Datasets
QaldGen: Towards Microbenchmarking of Question Answering Systems over Knowledge Graphs
Sparklify: A Scalable Software Component for Efficient Evaluation of SPARQL Queries over Distributed RDF Datasets
ClaimsKG: A Knowledge Graph of Fact-Checked Claims
CoCoOn: Cloud Computing Ontology for IaaS Price and Performance Comparison


In use track
Semantically-Enabled Optimization of Digital Marketing Campaigns
An End-to-End Semantic Platform for Nutritional Diseases Management
VLX-Stories: Building an Online Event Knowledge Base with Emerging Entity Detection
Personalized Knowledge Graphs for the Pharmaceutical Domain
Use of OWL and Semantic Web Technologies at Pinterest
An Assessment of Adoption and Quality of Linked Data in European Open Government Data
Easy Web API Development with SPARQL Transformer
Benefit Graph Extraction from Healthcare Policies
Knowledge Graph Embedding for Ecotoxicological Effect Prediction
Improving Editorial Workflow and Metadata Quality at Springer Nature
A Pay-as-you-go Methodology to Design and Build Enterprise Knowledge Graphs from Relational Databases

In the next article, we will discuss ISWC2020.

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