Tara Athan

OASIS LegalRuleML

In this paper we present the motivation, use cases, design principles, abstract syntax, and initial core of LegalRuleML. The LegalRuleML-core is sufficiently rich for expressing legal sources, time, defeasibility, and deontic operators. An example is provided. LegalRuleMLis compared to related work.

Reaction RuleML 1.0: standardized semantic reaction rules

RuleML is a family of XML languages whose modular system of schemas permits high-precision (Web) rule interchange. The familys top-level distinction is deliberation rules vs. reaction rules. In this paper we address the Reaction RuleML subfamily of RuleML and survey related work. Reaction RuleML is a standardized rule markup/serialization language and semantic interchange format for reaction rules and rule-based event processing. Reaction rules include distributed Complex Event Processing (CEP), Knowledge Representation (KR) calculi, as well as Event-Condition-Action (ECA) rules, Production (CA) rules, and Trigger (EA) rules. Reaction RuleML 1.0 incorporates this reactive spectrum of rules into RuleML employing a system of step-wise extensions of the Deliberation RuleML 1.0 foundation.

LegalRuleML: Design Principles and Foundations

This tutorial presents the principles of the OASIS LegalRuleML applied to the legal domain and discusses 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, as well as use case examples from the legal domain.

A Representational Analysis of the API4KP Metamodel

The API for Knowledge Platforms (API4KP) provides a common abstraction interface for discovery, exploration of metadata and querying of different types of knowledge bases. It targets the basic administration services as well as the retrieval and the modification of expressions in machine-readable knowledge representation and reasoning (KRR) languages, such as RDF(S), OWL, RuleML and Common Logic, within heterogeneous and possibly distributed (multi-language, multi-nature) knowledge platforms. This paper introduces typical use cases for API4KP and, based on their ontological requirements, analyses the representational completeness and clarity of its ontological metamodel.

API4KP Metamodel: A Meta-API for Heterogeneous Knowledge Platforms

API4KP (API for Knowledge Platforms) is a standard development effort that targets the basic administration services as well as the retrieval, modification and processing of expressions in machine-readable languages, including but not limited to knowledge representation and reasoning (KRR) languages, within heterogeneous (multi-language, multi-nature) knowledge platforms. KRR languages of concern in this paper include but are not limited to RDF(S), OWL, RuleML and Common Logic, and the knowledge platforms may support one or several of these. Additional languages are integrated using mappings into KRR languages. A general notion of structure for knowledge sources is developed using monads. The presented API4KP metamodel, in the form of an OWL ontology, provides the foundation of an abstract syntax for communications about knowledge sources and environments, including a classification of knowledge source by mutability, structure, and an abstraction hierarchy as well as the use of performatives (inform, query, ...), languages, logics, dialects, formats and lineage. Finally, the metamodel provides a classification of operations on knowledge sources and environments which may be used for requests (message-passing).

Enriching Topic Models with DBpedia

Traditional Topic Modeling approaches only consider the words in the document. By using an entity-topic modeling approach and including background knowledge about the entities such as the occupation of persons, the location of organizations, the band of a musician etc., we can better cluster related documents together, and produce semantic topic models that can be represented in a knowledge base. In our approach we first reduce the text documents to a set of entities and then enrich this set with background knowledge from DBpedia. Topic modeling is performed on the enriched set of entities and various feature combinations are evaluated in order to determine the combination that achieves the best classification precision or perplexity compared to using word-based topic models alone.

A Datalog+ RuleML 1.01 Architecture for Rule-Based Data Access in Ecosystem Research

Rule-Based Data Access (RBDA) enables automated reasoning over a knowledge base (KB) as a generalized global schema for the data in local (e.g., relational or graph) databases reachable through mappings. RBDA can semantically validate, enrich, and integrate heterogeneous data sources. This paper proposes an RBDA architecture layered on Datalog+ RuleML, and uses it for the ΔForest case study on the susceptibility of forests to climate change. Deliberation RuleML 1.01 was mostly motivated by Datalog customization requirements for RBDA. It includes Datalog+ RuleML 1.01 as a standard XML serialization of Datalog+, a superlanguage of the decidable Datalog±. Datalog+ RuleML is customized into the three Datalog extensions Datalog[∃], Datalog[=], and Datalog[\(\bot\)] through MYNG, the RuleML Modular sYNtax confiGurator generating (Relax NG and XSD) schemas from language-feature selections. The ΔForest case study on climate change employs data derived from three main forest monitoring networks in Switzerland. The KB includes background knowledge about the study sites and design, e.g., abundant tree species groups, pure tree stands, and statistical independence among forest plots. The KB is used to rewrite queries about, e.g., the eligible plots for studying a particular species group. The mapping rules unfold our newly designed global schema to the three given local schemas, e.g. for the grade of forest management. The RBDA/ΔForest case study has shown the usefulness of our approach to Ecosystem Research for global schema design and demonstrated how automated reasoning can become key to knowledge modeling and consolidation for complex statistical data analysis.

LegalRuleML: from metamodel to use cases

Several XML-based standards have been proposed for describing rules (RuleML, RIF, SWRL, SBVR, etc.), or specific dialects (RuleML family [1,2]). In 2009, the Legal Knowledge Interchange Format (LKIF [4]) was proposed to extend rule languages to account for the specifics of the legal domain and to manage legal resources. To further develop the representation of the law in XML-based standards, the OASIS Legal- RuleML TC held its first technical meeting on 19 January 2012 [9]. The objective of the TC is to extend the RuleML family with features specific to the formalisation of norms, guidelines, policies, and legal reasoning [3].

Syntax reuse: XSLT as a metalanguage for knowledge representation languages

We present here MXSL, a subset of XSLT re-interpreted as a syntactic metalanguage for RuleML with operational semantics based on XSLT processing. This metalanguage increases the expressivity of RuleML knowledge bases and queries, with syntactic access to the complete XML tree through the XPath Data Model. The metalanguage is developed in an abstract manner, as a paradigm applicable to other KR languages, in XML or in other formats.