Tutorial Day

Url:  http://2015.ruleml.org/tutorials.html

The tutorial papers are published in: Wolfgang, Faber and Adrian, Paschke: Reasoning Web. Web Logic Rules, 11th International Summer School 2015, Springer LNCS 9203, Berlin, Germany, July 31- August 4, 2015, Tutorial Lectures. http://www.csw.inf.fu-berlin.de/rw2015/proceedings.html

The RuleML tutorial day will be on August 2nd.

The TPTP World - Infrastructure for Automated Reasoning

by Geoff Sutcliffe, University of Miami, USA

The TPTP World is a well known and established infrastructure that supports research, development, and deployment of Automated Theorem Proving (ATP) systems for classical logics. The data, standards, and services provided by the TPTP World have made it increasingly easy to build, test, and apply ATP technology. This tutorial reviews the core features of the TPTP World, describes key service components of the TPTP World and how to use them, presents some successful applications, and gives an overview of planned developments.

[Homepage Geoff Sutcliffe] [Tutorial Paper] [Slides]

PSOA RuleML: Integrated Object-Relational Data and Rules

by Harold Boley, University of New Brunswick, Canada

Suppose you are working on a project using SQL queries over relational data and then proceeding to SPARQL queries over graph data to be used as a metadata repository. Or, vice versa, on a project complementing SPARQL with SQL for querying an evolving mass-data store. Or, on a project using SQL and SPARQL from the beginning. In all of these projects, object-relational interoperability issues may arise.

Indeed, both on intranets and on the Internet, most data is stored in one of two paradigms: As relations (predicate-centered), e.g. in the SQL-queried Deep Web, vs. graphs (object-centered), e.g. in the SPARQL-queried Semantic Web. This divide has also led to separate relational vs. object-centered rule paradigms for processing the data (e.g., for inferencing/reasoning with them). Projects involving both relations and graphs are thus impeded by the paradigm boundaries, from modeling to implementation. These boundaries can be bridged or even dissolved by languages combining the relational and object-centered paradigms for data as well as rules.

A heterogeneous combination (an amalgamation), as in F-logic and W3C RIF, allows atomic formulas in both the relational and object-centered language paradigms for data atoms as well as rules, possibly mixed within the same rule.

A homogeneous combination (an integration), as in Positional-Slotted, Object-Applicative (PSOA) RuleML, blends the relational and object-centered atomic formulas themselves into a uniform kind of atom, allowing paradigm-internal transformation of data as well as rules. Data, i.e. ground facts, include (table-row-like) relational atoms with positional arguments and (graph-node-like) object-centered atoms with an Object IDentifier (OID) and slotted arguments (for the node's outgoing labeled edges). Rules can use non-ground (variable-containing) versions of all of these atoms anywhere in their premises and conclusions.

Generally, the object-relational integration is achieved by permitting a relation application to have an OID ‒ typed by the relation as its class ‒ and, orthogonally, the relation's arguments to be positional or slotted. The resulting positional-slotted, object-applicative (psoa) atoms can be employed as

  1. predicate-centered, positional atoms without an OID and with an ‒ ordered ‒ sequence of arguments,
  2. predicate-centered, slotted atoms without an OID and with an ‒ unordered ‒ multi-set of slots (each being a pair of a name and a filler),
  3. object-centered, positional atoms (shelves) with an OID and with a sequence of arguments,
  4. object-centered, slotted atoms (frames) with an OID and with a multi-set of slots.

In the Family Example, the psoa atoms applied as binary predicates in the premise (1.) derive a psoa atom used as a typed frame in the conclusion (4.), whose OID can be generated on-the-fly for each invocation of the conclusion-existential rule.

PSOA RuleML is a Horn-logic language (optionally, with equality) that reduces the number of RIF-BLD terms by generalizing its positional and slotted (named-argument) terms as well as its frame terms and class memberships. It can be extended in various ways, e.g. with Negation As Failure (NAF), augmenting RuleML's MYNG configurator for the syntax and adapting the RIF-FLD-specified NAF dialects for the semantics. Conversely, PSOA RuleML is being developed as a module that is pluggable into larger (RuleML) logic languages, thus making them likewise object-relational.

This tutorial first reviews object-relational combinations with a focus on the PSOA RuleML integration. It then explores the integration semantics with systematic examples in the presentation syntaxes of F-logic, RIF, and RuleML/POSL, supported by Grailog visualization and serialized in RuleML/XML. Next, it presents a use case of bidirectional SQL-PSOA-SPARQL transformation (schema/ontology mapping). The tutorial then formalizes the first-order model-theoretic semantics, blending (OID-over-)slot distribution, as in RIF, with integrated psoa terms, as in RuleML. Finally, it surveys the PSOATransRun implementations spearheaded by Gen Zou, translating PSOA RuleML knowledge bases and queries to TPTP (PSOA2TPTP) and Prolog (PSOA2Prolog).

[Introducing RuleML] [Tutorial Paper] [Slides]

Powerful Practical Semantic Rules in Rulelog: Fundamentals and Recent Progress


by Benjamin Grosof       Michael Kifer       Paul Fodor
Coherent Knowledge     Stony Brook        Stony Brook
Systems, USA                University, USA    University, USA

In this tutorial, we cover the fundamental concepts and recent progress in the area of Rulelog, a leading approach to semantic rules knowledge representation and reasoning. Rulelog is expressively powerful, computationally affordable, and has capable efficient implementations. A large subset of Rulelog is in draft as an industry standard to be submitted to RuleML and W3C as a dialect of Rule Interchange Format (RIF).

"Textual" Rulelog, in which Rulelog is closely combined with natural language processing by using Rulelog to interpret and generate English, is a key area of ongoing research and development (R&D).

Rulelog extends well-founded declarative logic programs (LP) with:

  • strong meta-reasoning, including higher-order syntax (Hilog), reification, and rule id's (within the logical language)
  • explanations of inferences
  • efficient higher-order defaults, including "argumentation rules"
  • flexible probabilistic reasoning, including evidential probabilities and tight integration with inductive machine learning
  •      this is a key area of recent technology progress and ongoing R&D
  • bounded rationality, including restraint ‒ a "control knob" to ensure that the computational complexity of inference is worst-case polynomial time
  • "omni-directional" disjunction in the head (of a rule)
  • existential quantifiers (mixed with universal quantifiers) in the head
  • sound tight integration of first-order-logic ontologies including OWL
  • frame syntax, similar to RDF triples and object-orientation
  • and several other lesser features, including aggregation operators and integrity constraints

Implementation techniques for Rulelog inferencing include transformational compilations and extensions of LP "tabling" algorithms.   "Tabling" here includes: smart cacheing of conclusions; and incrementally revising the cached conclusions when rules are dynamically added or deleted. "Tabling" is thus a mixture of backward-direction and forward-direction inferencing. There are both open-source and commercial tools for Rulelog that vary in their range of expressive completeness and of user convenience.   They are interoperable with databases and spreadsheets, and complement inductive machine learning and natural language processing techniques.

The most complete system today for Rulelog is Ergo from Coherent Knowledge Systems. Using Ergo, we will illustrate that Rulelog technology has applications in a wide range of tasks and domains in business, government, and science. We will tour areas of recent applications progress that include: legal/policy compliance, e.g., in financial services; education/tutoring; and e-commerce marketing. This tutorial will provide a comprehensive and up-to-date introduction to these developments and to the fundamentals of the key technologies and outstanding research issues involved.

[Tutorial Paper][Slides]

Legal Norms Modelling with LegalRuleML (OASIS)


 Tara Athan Guido Governatori   Monica Palmirani   Adrian Paschke Adam Wyner
Atan Services NICTA CIRSFID, UniBo Freie Univ. Berlin Uni. Aberdeen


This tutorial presents the principles of the OASIS LegalRuleML applied to the legal domain and discuss why, how, and when LegalRuleML is well-suited for modelling norms. To provide a framework of reference, we present a comprehensive list of requirements for devising rule interchange languages that capture the peculiarities of legal rule modelling in support of legal reasoning. The tutorial comprises syntactic, semantic, and pragmatic foundations, a LegalRuleML primer, a comparison with related other approaches, as well as use case examples from the legal domain.

The tutorial includes the following topics:

  • defeasibility of rules and defeasible logic;
  • deontic operators (e.g., obligations, permissions, prohibitions, rights);
  • temporal management of the rules and temporal expressions within the rules
  • qualification of norms (constitutive, prescriptive, etc.);
  • jurisdiction of norms;
  • isomorphism between rules and natural language normative provisions;
  • identification of constituent parts of the norm;
  • authorial tracking of the rules;
  • how to model alternatives formalization of norms.

The various concepts and constructions will be illustrated by examples taken from concrete real life use cases.

[Tutorial Paper] [Slides]