As you may recall, the built-in inference rules are predefined rules provided in each instance of Virtuoso. These rules are based on specific relationship type and entity class definitions provided by the RDF Schema (RDFS) and Web Ontology Language (OWL) ontologies. Rosati, R.: Semantic and computational benefits of securely integrating ontologies and rules. In: Fages, F., Soliman, S. (eds.) PPSWR 2005. CNLC, Band 3703, s. 50-64. Springer, Heidelberg (2005) Golbreich, C., Mercier, S.: Construction of the Dialysis and Transplantation Ontology: Benefits, Limitations, and Questions About Protected OWL. In: Workshop on Medical Applications of Protege, 7th International Protégé Conference, Bethesda (2004) Golbreich, C., Imai, A.: Combining SWRL rules and OWL ontologies with Protégé OWL Plugin, Jess, and RACER. In: 7th International Protégé Conference, Bethesda (2004) Use and techniques of ontologies and rules large overlap. Generally speaking, ontologies optimize taxonomic reasoning problems, and rule-based systems optimize reasoning problems in data.

The difference is largely a matter of style, and criteria such as available expertise, ease of adaptation to existing data, tool support, maturity and cost, etc. should be considered much more important when choosing. Drabent, W., Maluszynski, J.: Well-founded semantics for hybrid rules. In: Marchiori, M., Pan, J.Z., Marie, C. (eds.) RR 2007. CNL, Band 4524, s. 1-15. Springer, Heidelberg (2007) de Bruijn, J., Pearce, D., Polleres, A., Valverde, A.: A logic for hybrid rules. In: Proceedings Second International Conference on Rules and Rule Markup Languages for the Semantic Web (RuleML 2006), IEEE, Los Alamitos (2006), 2006.ruleml.org/online-proceedings/rule-integ.pdf Semantic inference or inference on the Semantic Web is the process of adding new data to a dataset created from existing data. That`s why it`s so powerful – no additional data needs to be collected to get new information.

This information comes in the form of new relationships that make connections in data that were not previously observed. Dameron, O., Gibaud, B., Musen, M.: Use of semantic dependencies for managing the consistency of an ontology of the anatomy of the cerebral cortex. In: KR-MED 2004 (2004) Polleres, A.: From SPARQL to rules (and back). In: Proceedings of the 16th World Wide Web Conference (WWW 2007), Banff, Canada (May 2007) Inference is based on two tools, ontologies and rules. The first describes the structural model of data as stratified into classes and subclasses, etc., and the second dictates the laws to which the data must obey. If these seem to overlap, it`s because they do – they share a lot of features. An ontology will tell you that the countries of a continent are a subset of all countries, and a rule will tell you that a city in a country must also be on the continent of that country. Golbreich, C., Dameron, O., Gibaud, B., Burgun, A.: Web ontology language requirements w.r.t. expressiveness of taxonomy and axioms in medicine. In: Fensel, D., Sycara, K., Mylopoulos, J.

(eds.) ISWC 2003. CNL, Band 2870, s. 180–194. Springer, Heidelberg (2003) When it comes to certain types of financial fraud, the first step is often to find a single malicious person or organization, but targeting them alone is rarely enough to shut down the system completely. If a bank were to examine the fraudster`s financial transactions, a number of characteristics could pass them on to other members of its fraud network – value, frequency, time, etc. From this information, we can deduce who their accomplices are and who are innocent viewers who sold them something on eBay. Therefore, the addition to the database – the derived information – is the identification of people as criminal or innocent. This chain of inference can then continue until the entire gang is identified, after which you have the ability to properly address the problem by shutting down the unit as a whole.

S-LOR (Sensor-based Linked Open Rules) is a rule-based reasoning engine and an approach to sharing and reusing interoperable rules to derive meaningful insights from sensor measurements. You can also load the SQL script into your own Virtuoso 8.0 instance to achieve the same result. Eiter, T., Ianni, G., Polleres, A., Schindlauer, R., Tompits, H.: Reasoning with rules and ontologies. In: Barahona, P., Bry, F., Franconi, E., Henze, N., Sattler, U. (eds.) Reasoning Web 2006. CNL, Band 4126, s. 93-127. Springer, Heidelberg (2006) Rules and ontologies play a key role in the layered architecture of the Semantic Web, as they are used to assign meaning and think about data on the Web. While the ontology layer of the Semantic Web is quite developed and the Web Ontology Language (OWL) has been a W3C recommendation for several years, the control layer is much less developed and is an active area of research; So far, a number of initiatives and proposals have been made, but no standards have yet been published. There are many implementations of rule engines that handle Semantic Web data in one way or another. This article provides a comprehensive, but not exhaustive, overview of these systems, describes their supported languages, and links them to theoretical approaches to the combination of rules and ontologies envisioned in the Semantic Web architecture.

In doing so, we identify the desired properties and similarities of rule languages and evaluate existing systems for their support. In addition, we study the technical problems underlying the integration of rules and ontologies and classify representative proposals for theoretical integration approaches into different categories. These are very simple cases where inference is used, but the extent of its value is limitless. From deeply networked data in a graph database, countless pieces of information can be obtained that would be overlooked without considerable additional effort. de Bruijn, J., Eiter, T., Polleres, A., Tompits, H.: On representational issues about combination of classical theories with nonmonotonic rules. In: Lang, J., Lin, F., Wang, J. (eds.) KSEM 2006. CNCL (LNAI), Band 4092, S. 1–22. Springer, Heidelberg (2006) The use of rules in conjunction with ontologies is a major challenge for the Semantic Web.

We propose a pragmatic approach to reasoning with ontologies and rules based on currently available Semantic Web standards and tools. We first realized an implementation of SWRL, the emerging OWL/RuleML combination rule standard, using the OWL Protected plugin. We then developed a protected plugin, SWRLJessTab, which allows you to calculate inferences using the Racer classifier and the Jess inference engine to argue with rules and ontologies, both represented in OWL. A small example, including an OWL ontology and a SWRL rule base, shows that all domain knowledge, that is.