Conjunctive Query Answering for the Description Logic SHIQ

Conjunctive queries play an important role as an expressive query language for Description Logics (DLs). Although modern DLs usually provide for transitive roles, conjunctive query answering over DL knowledge bases is only poorly understood if transitive roles are admitted in the query. In this paper, we consider unions of conjunctive queries over knowledge bases formulated in the prominent DL SHIQ and allow transitive roles in both the query and the knowledge base. We show decidability of query answering in this setting and establish two tight complexity bounds: regarding combined complexity, we prove that there is a deterministic algorithm for query answering that needs time single exponential in the size of the KB and double exponential in the size of the query, which is optimal. Regarding data complexity, we prove containment in co-NP

Unions of conjunctive queries in SHOQ

Conjunctive queries play an important role as an expressive query language in Description Logics (DLs). Decision procedures for expressive Description Logics are, however, only recently emerging and it is still an open question whether answering conjunctive queries is decidable for the DL SHOIQ that underlies the OWL DL standard. In fact, no decision procedure was known for expressive DLs that contain nominals. In this paper, we close this gap by providing a decision procedure for entailment of unions of conjunctive queries in SHOQ. Our algorithm runs in deterministic time single exponential in the size of the knowledge base and double exponential in the size of the query, which is the same as for SHIQ. Our procedure also shows that SHOQ knowledge base consistency is indeed ExpTime-complete, which was, to the best of our knowledge, always conjectured but never proved

On Maintaining Semantic Networks: Challenges, Algorithms, Use Cases

PURPOSE: Knowledge workers are confronted with a massive load of data from heterogeneous sources, making it difficult for them to discover information relevant in the context of their daily tasks. As a particular challenge, enterprise information needs to be aligned with business processes. In previous work, the authors introduced the Semantic Network (SN) approach for bridging this gap, i.e., for discovering explicit relations between enterprise information and business processes. What has been neglected so far, however, is SN maintenance, which is required to keep an SN consistent, complete, and up-to-date. The paper tackles this issue and extends the SN approach with methods and algorithms for enabling SN maintenance. DESIGN/METHODOLOGY/APPROACH: The paper illustrates an approach for SN maintenance. Specifically, the authors show how an SN evolves over time, classify properties of objects and relations captured in an SN, and show how these properties can be maintained. An empirical evaluation, which is based on synthetic and real-world data, investigates the performance, scalability and practicability of the proposed algorithms. FINDINGS: The authors prove the feasibility of the introduced algorithms in terms of runtime performance with a proof-of-concept implementation. Further, a real-world case from the automotive domain confirms the applicability of the SN maintenance approach. ORIGINALITY/VALUE: As opposed to existing work, the presented approach allows for the automated and consistent maintenance of SNs. Furthermore, the applicability of the presented SN maintenance approach is validated in the context of a real-world scenario as well as two business cases

Maintaining Semantic Networks: Challenges and Algorithms

Knowledge workers are confronted with a massive load of data from numerous heterogeneous sources, making it difficult for them to identify the information relevant for performing their tasks. Particularly challenging is the alignment of information with business processes. In previous work, we introduced a Semantic Network (SN) for bridging this gap, i.e., for identifying relations between information and business processes. What has been neglected so far is the maintenance of an SN in order to keep the SN consistent, complete and up-to-date. This paper tackles this issue and extends our approach with algorithms dealing with the maintenance of an SN. For this purpose, we identify and classify properties of objects and relations captured in an SN and show how these properties can be maintained. We use a case from the automotive domain in order to demonstrate and validate the feasibility and applicability of our maintenance framework

Coupling tableau algorithms for expressive description logics with completion-based saturation procedures

Abstract. Nowadays, saturation-based reasoners for the OWL EL profile are able to handle large ontologies such as SNOMED very efficiently. However, saturation-based reasoning procedures become incomplete if the ontology is extended with axioms that use features of more expressive Description Logics, e.g., disjunctions. Tableau-based procedures, on the other hand, are not limited to a specific OWL profile, but even highly optimised reasoners might not be efficient enough to handle large ontologies such as SNOMED. In this paper, we present an approach for tightly coupling tableau-and saturation-based procedures that we implement in the OWL DL reasoner Konclude. Our detailed evaluation shows that this combination significantly improves the reasoning performance on a wide range of ontologies

Current and Future Challenges in Knowledge Representation and Reasoning

Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade

Small talk is more than chit-chat: Exploiting structures of casual conversations for a virtual agent

Mattar N, Wachsmuth I. Small talk is more than chit-chat: Exploiting structures of casual conversations for a virtual agent. In: Glimm B, Krüger A, eds. KI 2012: Advances in Artificial Intelligence. Lecture Notes in Computer Science. Vol 7526. Berlin, Heidelberg: Springer Berlin Heidelberg; 2012: 119-130

The Energy Management Adviser at EDF

Abstract. The EMA (Energy Management Adviser) aims to produce personalised energy saving advice for EDF’s customers. The advice takes the form of one or more ‘tips’, and personalisation is achieved using se-mantic technologies: customers are described using RDF, an OWL on-tology provides a conceptual model of the relevant domain (housing, environment, and so on) and the different kinds of tips, and SPARQL query answering is used to identify relevant tips. The current prototype provides tips to more than 300,000 EDF customers in France at least twice a year. The main challenges for our future work include providing a timely service for all of the 35 million EDF customers in France, simpli-fying the system’s maintenance, and providing new ways for interacting with customers such as via a Web site.