Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

Kaskade: Graph Views for Efficient Graph Analytics

Graphs are an increasingly popular way to model real-world entities and relationships between them, ranging from social networks to data lineage graphs and biological datasets. Queries over these large graphs often involve expensive subgraph traversals and complex analytical computations. These real-world graphs are often substantially more structured than a generic vertex-and-edge model would suggest, but this insight has remained mostly unexplored by existing graph engines for graph query optimization purposes. Therefore, in this work, we focus on leveraging structural properties of graphs and queries to automatically derive materialized graph views that can dramatically speed up query evaluation. We present KASKADE, the first graph query optimization framework to exploit materialized graph views for query optimization purposes. KASKADE employs a novel constraint-based view enumeration technique that mines constraints from query workloads and graph schemas, and injects them during view enumeration to significantly reduce the search space of views to be considered. Moreover, it introduces a graph view size estimator to pick the most beneficial views to materialize given a query set and to select the best query evaluation plan given a set of materialized views. We evaluate its performance over real-world graphs, including the provenance graph that we maintain at Microsoft to enable auditing, service analytics, and advanced system optimizations. Our results show that KASKADE substantially reduces the effective graph size and yields significant performance speedups (up to 50X), in some cases making otherwise intractable queries possible

INDEMICS: An Interactive High-Performance Computing Framework for Data Intensive Epidemic Modeling

We describe the design and prototype implementation of Indemics (_Interactive; Epi_demic; _Simulation;)—a modeling environment utilizing high-performance computing technologies for supporting complex epidemic simulations. Indemics can support policy analysts and epidemiologists interested in planning and control of pandemics. Indemics goes beyond traditional epidemic simulations by providing a simple and powerful way to represent and analyze policy-based as well as individual-based adaptive interventions. Users can also stop the simulation at any point, assess the state of the simulated system, and add additional interventions. Indemics is available to end-users via a web-based interface. Detailed performance analysis shows that Indemics greatly enhances the capability and productivity of simulating complex intervention strategies with a marginal decrease in performance. We also demonstrate how Indemics was applied in some real case studies where complex interventions were implemented

Enhance DBMS capabilities using semantic data modelling approach.

by Yip Wai Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 1990.Bibliography: leaves 132-135.ABSTRACTACKNOWLEDGEMENTSPART IChapter 1 --- OVERVIEW ON SEMANTIC DATA MODELLING APPROACH … --- p.1Chapter 2 --- SCOPE OF RESEARCH --- p.4Chapter 3 --- CONCEPTUAL STRUCTURE OF SAM* --- p.7Chapter 3.1 --- Concepts and Associations --- p.7Chapter 3.1.1 --- Membership Association --- p.8Chapter 3.1.2 --- Aggregation Association --- p.8Chapter 3.1.3 --- Generalization Association --- p.9Chapter 3.1.4 --- Interaction Association --- p.10Chapter 3.1.5 --- Composition Association --- p.11Chapter 3.1.6 --- Cross-Product Association --- p.12Chapter 3.1.7 --- Summary Association --- p.13Chapter 3.2 --- An Example --- p.14Chapter 3.3 --- Occurrences --- p.15PART IIChapter 4 --- SYSTEM OVERVIEW --- p.17Chapter 4.1 --- System Objectives --- p.17Chapter 4.1.1 --- Data Level --- p.17Chapter 4.1.2 --- Meta-Data Level --- p.18Chapter 4.2 --- System Characteristics --- p.19Chapter 4.3 --- Design Considerations --- p.20Chapter 5 --- IMPLEMENTATION CONSIDERATIONS --- p.23Chapter 5.1 --- Introduction --- p.23Chapter 5.2 --- Data Definition Language for Schema --- p.24Chapter 5.3 --- Construction of Directed Acyclic Graph --- p.27Chapter 5.4 --- Query Manipulation Language --- p.28Chapter 5.4.1 --- Semantic Manipulation Language --- p.29Chapter 5.4.1.1 --- Locate Concepts --- p.30Chapter 5.4.1.2 --- Retrieve Information About Concepts --- p.30Chapter 5.4.1.3 --- Find a Path Between Two Concepts --- p.31Chapter 5.4.2 --- Occurrence Manipulation Language --- p.32Chapter 5.5 --- Examples --- p.35Chapter 6 --- RESULTS AND DISCUSSIONS --- p.41Chapter 6.1 --- Allow Non-Homogeneity of Facts about Entities --- p.41Chapter 6.2 --- Field Name is Information --- p.42Chapter 6.3 --- Description of Group of Information --- p.43Chapter 6.4 --- Explicitly Description of Interaction --- p.43Chapter 6.5 --- Information about Entities --- p.44Chapter 6.6 --- Automatically Joining Tables --- p.45Chapter 6.7 --- Automatically Union Tables --- p.45Chapter 6.8 --- Automatically Select Tables --- p.46Chapter 6.9 --- Ambiguity --- p.47Chapter 6.10 --- Normalization --- p.47Chapter 6.11 --- Update --- p.50PART IIIChapter 7 --- SCHEMA VERIFICATION --- p.55Chapter 7.1 --- Introduction --- p.55Chapter 7.2 --- Need of Schema Verification --- p.57Chapter 7.3 --- Integrity Constraint Handling Vs Schema Verification --- p.58Chapter 8 --- AUTOMATIC THEOREM PROVING --- p.60Chapter 8.1 --- Overview --- p.60Chapter 8.2 --- A Discussion on Some Automatic Theorem Proving Methods --- p.61Chapter 8.2.1 --- Resolution --- p.61Chapter 8.2.2 --- Natural Deduction --- p.63Chapter 8.2.3 --- Tableau Proof Methods --- p.65Chapter 8.2.4 --- Connection Method --- p.67Chapter 8.3 --- Comparison of Automatic Theorem Proving Methods --- p.70Chapter 8.3.1 --- Proof Procedure --- p.70Chapter 8.3.2 --- Overhead --- p.70Chapter 8.3.3 --- Unification --- p.71Chapter 8.3.4 --- Heuristics --- p.72Chapter 8.3.5 --- Getting Lost --- p.73Chapter 8.4 --- The Choice of Tool for Schema Verification --- p.73Chapter 9 --- IMPROVEMENT OF CONNECTION METHOD --- p.77Chapter 9.1 --- Motivation of Improving Connection Method --- p.77Chapter 9.2 --- Redundancy Handled by the Original Algorithm --- p.78Chapter 9.3 --- Design Philosophy of the Improved Version --- p.82Chapter 9.4 --- Primary Connection Method Algorithm --- p.83Chapter 9.5 --- AND/OR Connection Graph --- p.89Chapter 9.6 --- Graph Traversal Procedure --- p.91Chapter 9.7 --- Elimination Redundancy Using AND/OR Connection Graph --- p.94Chapter 9.8 --- Further Improvement on Graph Traversal --- p.96Chapter 9.9 --- Comparison with Original Connection Method Algorithm --- p.97Chapter 9.10 --- Application of Connection Method to Schema Verification --- p.98Chapter 9.10.1 --- Express Constraint in Well Formed Formula --- p.98Chapter 9.10.2 --- Convert Formula into Negation Normal Form --- p.101Chapter 9.10.3 --- Verification --- p.101PART IVChapter 10 --- FURTHER DEVELOPMENT --- p.103Chapter 10.1 --- Intelligent Front-End --- p.103Chapter 10.2 --- On Connection Method --- p.104Chapter 10.3 --- Many-Sorted Calculus --- p.104Chapter 11 --- CONCLUSION --- p.107APPENDICESChapter A --- COMPARISON OF SEMANTIC DATA MODELS --- p.110Chapter B --- CONSTRUCTION OP OCCURRENCES --- p.111Chapter C --- SYNTAX OF DDL FOR THE SCHEMA --- p.113Chapter D --- SYNTAX OF SEMANTIC MANIPULATION LANGUAGE --- p.116Chapter E --- TESTING SCHEMA FOR FUND INVESTMENT DBMS --- p.118Chapter F --- TESTING SCHEMA FOR STOCK INVESTMENT DBMS --- p.121Chapter G --- CONNECTION METHOD --- p.124Chapter H --- COMPARISON BETWEEN RESOLUTION AND CONNECTION METHOD --- p.128REFERENCES --- p.13

Product Knowledge Graph Embedding for E-commerce

In this paper, we propose a new product knowledge graph (PKG) embedding approach for learning the intrinsic product relations as product knowledge for e-commerce. We define the key entities and summarize the pivotal product relations that are critical for general e-commerce applications including marketing, advertisement, search ranking and recommendation. We first provide a comprehensive comparison between PKG and ordinary knowledge graph (KG) and then illustrate why KG embedding methods are not suitable for PKG learning. We construct a self-attention-enhanced distributed representation learning model for learning PKG embeddings from raw customer activity data in an end-to-end fashion. We design an effective multi-task learning schema to fully leverage the multi-modal e-commerce data. The Poincare embedding is also employed to handle complex entity structures. We use a real-world dataset from grocery.walmart.com to evaluate the performances on knowledge completion, search ranking and recommendation. The proposed approach compares favourably to baselines in knowledge completion and downstream tasks