In this half-day tutorial, we cover the fundamental concepts, key technologies, emerging applications, recent progress, and outstanding research issues in the area of Rulelog, a leading approach to fully semantic rule-based knowledge representation and reasoning (KRR). Rulelog matches well many of the requirements of cognitive computing. It combines deep logical/probabilistic reasoning tightly with natural language processing (NLP), and complements machine learning (ML). Rulelog interoperates and composes well with graph databases, relational databases, spreadsheets, XML, and expressively simpler rule/ontology systems and can orchestrate overall hybrid KRR. Developed mainly since 2005, Rulelog is much more expressively powerful than the previous state-of-the-art practical KRR approaches, yet is computationally affordable. It is fully semantic and has capable efficient implementations that leverage methods from logic programming and databases, including dependency-aware smart caching and a dynamic compilation stack architecture.
Grosof et. al inference engine, we will illustrate Rulelog’s applications in deep reasoning and representing complex knowledge such as policies, regulations/contracts, science, and terminology mappings across a wide range of tasks and domains in business, government, and academe. Examples include: legal/policy compliance, e.g., in financial services; financial reporting/accounting; health care treatment guidance and insurance; education/tutoring; security/confidentiality policies; and e-commerce marketing.
In this tutorial on Rulelog as it is implemented in Ergo, we will cover some unique
KRR features, such as:
– frame-based object-oriented frame syntax [
Much of this tutorial will be dedicated to applications of Ergo, both horizontally (e.g., policy-based decisions, info Integration, analytics, human-computer interaction (HCI), search, business intelligence, risk management) and vertically (e-commerce and marketing, financial services, personalized e-learning, security and defense, biomedical, insurance, Internet of Things (IoT), social media sharing policies).
Finally, we will discuss open research topics in Ergo, such as, authoring rules starting from NL, distributed reasoning, optimization of uncertainty reasoning, equality, aggregates, integration with ASP, constraint solving, and classical logic, hypotheticals, abduction and integration with ML.
The goal of the tutorial is for the audience to walk away with an understanding of Rulelogs key innovative logical and inferencing concepts, its broad applicability, its overall advantages and limitations, a sample of some specific application areas, and its open research topics.
The intended audience for this tutorial is the rules and reasoning community (all of the RuleML+RR audience) and the assumed background of the participants is only the basics of first-order-logic and relational databases. Knowledge of declarative logic programs, XML, RDF, and SPARQL will be helpful but not required.