http://www.umbertostraccia.it/cs/download/papers/JWS08/JWS08.pdf

Managing uncertainty and vagueness in description logics for the Semantic Web

In this paper, we introduce a family of expressive extensions of Datalog, called Datalog+/-, as a new paradigm for query answering over ontologies. The Datalog+/- family admits existentially quantified variables in rule heads, and has suitable restrictions to ensure highly efficient ontology querying. We show in particular that Datalog+/- generalizes the DL-Lite family of tractable description logics, which are the most common tractable ontology languages in the context of the Semantic Web and databases. We also show how stratified negation can be added to Datalog+/- while keeping ontology querying tractable. Furthermore, the Datalog+/- family is of interest in its own right and can, moreover, be used in various contexts such as data integration and data exchange.

/pdf/a-general-datalog-based-framework-for-tractable-query-1zyz34puul.pdf

A general datalog-based framework for tractable query answering over ontologies

Mining valuable hidden knowledge from large-scale data relies on the support of reasoning technology. Knowledge graphs, as a new type of knowledge representation, have gained much attention in natural language processing. Knowledge graphs can effectively organize and represent knowledge so that it can be efficiently utilized in advanced applications. Recently, reasoning over knowledge graphs has become a hot research topic, since it can obtain new knowledge and conclusions from existing data. Herein we review the basic concept and definitions of knowledge reasoning and the methods for reasoning over knowledge graphs. Specifically, we dissect the reasoning methods into three categories: rule-based reasoning, distributed representation-based reasoning and neural network-based reasoning. We also review the related applications of knowledge graph reasoning, such as knowledge graph completion, question answering, and recommender systems. Finally, we discuss the remaining challenges and research opportunities for knowledge graph reasoning.

A review: Knowledge reasoning over knowledge graph

A general Datalog-based framework for tractable query answering over ontologies

Existing semantic search tools have been primarily designed to enhance the performance of traditional search technologies but with little support for ordinary end users who are not necessarily familiar with domain specific semantic data, ontologies, or SQL-like query languages. This paper presents SemSearch, a search engine, which pays special attention to this issue by providing several means to hide the complexity of semantic search from end users and thus make it easy to use and effective.

/pdf/semsearch-a-search-engine-for-the-semantic-web-bc359bvrz6.pdf

SemSearch: a search engine for the semantic web

With the increasing use of ontologies in Semantic Web and enterprise knowledge management, it is critical to develop scalable and efficient ontology management systems In this paper, we present Minerva, a storage and inference system for large-scale OWL ontologies on top of relational databases It aims to meet scalability requirements of real applications and provide practical reasoning capability as well as high query performance The method combines Description Logic reasoners for the TBox inference with logic rules for the ABox inference Furthermore, it customizes the database schema based on inference requirements User queries are answered by directly retrieving materialized results from the back-end database The effective integration of ontology inference and storage is expected to improve reasoning efficiency, while querying without runtime inference guarantees satisfactory response time Extensive experiments on University Ontology Benchmark show the high efficiency and scalability of Minerva system.

https://www.dis.uniroma1.it/%7Edegiacom/didattica/semingsoft/SIS05-06/seminari-studenti/07-04-04%20-%20SIS%20-%20Antonello%20Ercoli%20Alessandro%20Pezzullo%20-%20IBM%20Minerva/articoli/Minerva/Minerva_A_Scalable_OWL_Ontology_Repository.pdf

Minerva: a scalable OWL ontology storage and inference system

Semantic Portal is the next generation of web portals that are powered by Semantic Web technologies for improved information sharing and exchange for a community of users. Current methods of searching in Semantic Portals are limited to keyword-based search using information retrieval (IR) techniques, ontology-based formal query and reasoning, or a simple combination of the two. In this paper, we propose an enhanced model that tightly integrates IR with formal query and reasoning to fully utilize both textual and semantic information for searching in Semantic Portals. The model extends the search capabilities of existing methods and can answer more complex search requests. The ideas in a fuzzy description logic (DL) IR model and a formal DL query method are employed and combined in our model. Based on the model, a semantic search service is implemented and evaluated. The evaluation shows very large improvements over existing methods.

/pdf/an-enhanced-model-for-searching-in-semantic-portals-z2jin8rh8t.pdf

An enhanced model for searching in semantic portals

In the semantic web context,the formal representation of knowledge is not resourceful while the informal one with uncertainty prevails. In order to provide an uncertainty reasoning service for semantic web applications, we propose a probabilistic extension of Description Logic, namely Probabilistic Description Logic Program (PDLP). In this paper, we introduce the syntax and intensional semantics of PDLP, and present a fast reasoning algorithm making use of Logic Programming techniques. This extension is expressive, lightweight, and intuitive. Based on this extension, we implement a PDLP reasoner, and apply it into practical use: Tourism Ontology Uncertainty Reasoning system (TOUR). The TOUR system uses PDLP reasoner to make favorite travel plans on top of an integrated tourism ontology, which describes travel cites and services with their evaluation.

Providing an uncertainty reasoning service for semantic web application

With the increasing use of ontologies in Semantic Web and enterprise knowledge management, it is critical to develop scalable and efficient ontology management systems. In this paper, we present Minerva, a storage and inference system for large-scale OWL ontologies on top of relational databases. It aims to meet scalability requirements of real applications and provide practical reasoning capability as well as high query performance. The method combines Description Logic reasoners for the TBox inference with logic rules for the ABox inference. Furthermore, it customizes the database schema based on inference requirements. User queries are answered by directly retrieving materialized results from the back-end database. The effective integration of ontology inference and storage is expected to improve reasoning efficiency, while querying without runtime inference guarantees satisfactory response time. Extensive experiments on University Ontology Benchmark show the high efficiency and scalability of Minerva system.

Minerva : A Scalable OWL Ontology Storage and Inference System

With the explosive emerged Web services, the Web becomes a world of information and applications. So portals whose goal is presenting a structured view onto the Web should provide users with a single point access to multiple types of not only information but also services. Recently, many systems have been developed using ontology-driven approaches to automatically generate and manage Web portals. However, all of them lack the integration of Web services. In this paper, we describe SPortS, an OWL-DL(Web Ontology Language-Description Logics) based portal generation system that integrates semantic Web services into generated portals at the presentation level, the data level and the function level. We present the portal generation process, the query decomposition and the service recommendation algorithms through which semantic Web services are integrated tightly into the generated portal.

Jian Zhou

Papers

Minerva: a scalable OWL ontology storage and inference system

An enhanced model for searching in semantic portals

Providing an uncertainty reasoning service for semantic web application

Minerva : A Scalable OWL Ontology Storage and Inference System

Integrating web services into ontology-based web portal