Semantic Querying (SQ) is emerging as an attractive approach for retrieval of data from relational and other conceptually similar databases, targeting users with limited or no technical expertise. Using SQ queries can be formulated using terminologies from a specific domain, which are then either translated in real time into the equivalent SQL queries, or executed against a materialised semantic database obtained by transforming the source relational data. This approach is suitable for non-technical users who are familiar with describing a domain using the terminologies in an ontology but lack expertise in writing SQL queries over complex relational schemas. As an alternative to the existing methods, we implement the vision of SQ on relational databases by deploying Semantic Web services over one or more databases and querying them with SPARQL queries. The approach based on manual Semantic Web service writing under-utilises easily manageable declarative mappings between source data schemas and domain ontologies. In this paper we introduce the Valet SADI framework to automate the creation of SADI Semantic Web services from declarative mappings, and demonstrate that Valet SADI, together with SADI query engines, establishes semantic querying as a viable, economical and user friendly way of querying relational databases.

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.

The IEEE RAS Ontologies for Robotics and Automation Working Group is dedicated to developing a methodology for knowledge representation and reasoning in robotics and automation. As part of this working group, the Autonomous Robots sub-group is tasked with developing ontology modules for autonomous robots. This paper describes the work in progress on the development of ontologies for autonomous systems. For autonomous systems, the focus is on the cooperation, coordination, and communication of multiple unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and autonomous underwater vehicles (AUVs). The ontologies serve as a framework for working out concepts of employment with multiple vehicles for a variety of operational scenarios with emphasis on collaborative and cooperative missions.

Automated matchmaking is increasingly used in e-marketplaces. A participant is presented with a ranked list of coun- terpart profiles of other participants. A participant profile consists of a set of hard (inflexible) and soft (flexible) con- straints. For two participant profiles, corresponding pairs of constraints are compared. We propose compromise match- ing wherein one or both constraints of a pair are soft and their values do not match exactly. We demonstrate com- promise matching with a home rental use case with data obtained from an existing e-marketplace.

We propose a semantics-enabled layered policy architecture for the exchange and management of multiple policies created by different policy languages on the Web. This architecture consists of four layers: Unifying Logic (UNL), Policy Interchange Format (PIF), Privacy Protection/DRM (PPD), and Domain Specific Applications (DSA). A meta-Policy Interchange Format (meta-PIF) layer is also introduced, side by side with the corresponding PIF layer, allowing agents in the facilitator to provide uniform services of interchange, reconciliation, and combination of policies. This SemPIF architecture extends W3C's Semantic Web architecture to permit the reuse of earlier work. A scenario of agents in the facilitator employing SemPIF for Digital Rights Management (DRM) and privacy protection policies on digital library subscription services will be demonstrated.

The W3C Rule Interchange Format (RIF) is a forthcoming standard for exchanging rules among different systems and developing intelligent rule-based applications for the Semantic Web. The RIF architecture is conceived as a family of languages, called dialects. A RIF dialect is a rule-based language with an XML syntax and a well-defined semantics. The RIF Basic Logic Dialect (RIF-BLD) semantically corresponds to a Horn rule language with equality. RIF-BLD has a number of syntactic extensions with respect to traditional textbook Horn logic, which include F-logic frames and predicates with named arguments. RIF-BLD is also well integrated with the relevant Web standards. It provides Internationalized Resource Identifiers (IRIs), XML Schema datatypes, and is aligned with RDF and OWL. This paper is a guide to the essentials of RIF-BLD, its syntax, semantics, and XML serialization. At the same time, some important RIF-BLD features are omitted due to the space limitations, including datatypes, built-ins, and the integration with RDF and OWL.

The success of matchmaking systems largely depends on how effectively product/service descriptions (profiles) of participants are modelled. We formalize the multifaceted expectations and interests of participants as 'constraints' in those profiles. We identify and explicitly define the relevant types of constraints. We propose a new knowledge representation (KR) model for Web-based matchmaking systems that can represent these constraints. We present a system that implements the proposed KR model, exemplifying its features and evaluating its performance.

Risk identification in home care is a key issue in ambient assisted living today. This paper introduces a bionic approach to risk identification in home care based on simple, inexpensive and diverse sensors. Neuro-symbolic perception combines characteristics of neural and symbolic information processing. We represent knowledge relevant for home care as an ontology and apply this knowledge in a neuro-symbolic network to identify risks. A top-level ontology about the assisted living of elderly people is proposed as part of a use case for risk identification in home care.

Uniting ontologies and rules has become a central topic in the Semantic Web. Bridging the discrepancy between these two knowledge representations, this paper introduces DatalogDL as a family of hybrid languages, where Datalog rules are parameterized by various DL (description logic) languages ranging from to . Making DatalogDL a decidable system with complexity of EXPTIME, we propose independent properties in the DL body as the restriction to hybrid rules, and weaken the safeness condition to balance the trade‐off between expressivity and reasoning power. Building on existing well‐developed techniques, we present a principled approach to enrich (RuleML) rules with information from (OWL) ontologies, and develop a prototype system combining a rule engine (OO jDREW) with a DL reasoner (RACER).

We present a browser-extending Semantic Web extraction system that maps HTML documents to tables and, where possible, to rules. First, the basic data extractor ViPER distills and reorganizes semi-structured information into a tabular data structure, which can again be browsed and/or submitted to further machine processing. Second, exemplifying the latter, the extended knowledge extractor Rex ViPER mines the resulting tables for structural properties and functional dependencies. Rules are generated to obtain a more compact and manageable, often also enriched, knowledge representation. The resulting fully structured information, RuleML-serialized facts and rules, can be stored along with the orginal documents, queried by rule engines such as OO jDREW and FLORID, and interchanged between Web Services. Thus Rex ViPER contributes to automating the construction of a machine-processable Semantic Web.

This paper discusses the approach taken by the Rule Markup Language (RuleML) Initiative towards a general Web rule language framework and relates it to the MDA and UML by the Object Management Group (OMG). It also presents the abstract syntax of RuleML 0.85 as a MOF/UML model and considers the possibility to integrate RuleML with OCL and Action Semantics.

A tree similarity algorithm for match‐making of agents in e‐Business environments is presented. Product/service descriptions of seller and buyer agents are represented asnode‐labeled, arc‐labeled, arc‐weightedtrees. A similarity algorithm for such trees is developed as the basis for semantic match‐making in a virtual marketplace. The trees are exchanged using an XML serialization in Object‐Oriented RuleML. Correspondingly, we use the declarative language Relfun to implement the similarity algorithm as a parameterized, recursive functional program. Three main recursive functions perform a top‐down traversal of trees and the bottom‐up computation of similarity. Results from our experiments aiming to match buyers and sellers are found to be effective and promising for e‐Business/e‐Learning environments. The algorithm can be applied in all environments where weighted trees are used.