Guilin Qi

Extending description logics with uncertainty reasoning in possibilistic logic

Possibilistic logic provides a convenient tool for dealing with uncertainty and handling inconsistency. In this paper, we propose possibilistic description logics as an extension of description logics, which are a family of well‐known ontology languages. We first give the syntax and semantics of possibilistic description logics and define several inference services in possibilistic description logics. We show that these inference serviced can be reduced to the task of computing the inconsistency degree of a knowledge base in possibilistic description logics. Since possibilistic inference services suffer from the drowning problem, that is, axioms whose confidence degrees are less than or equal to the inconsistency are not used, we consider a drowning‐free variant of possibilistic inference, called linear order inference. We propose an algorithm for computing the inconsistency degree of a possibilistic description logic knowledge base and an algorithm for the linear order inference. We consider the impact of our possibilistic description logics on ontology learning and ontology merging. Finally, we implement these algorithms and provide some interesting evaluation results.

RaDON -- Repair and Diagnosis in Ontology Networks

One of the major challenges in managing networked and dynamic ontologies is to handle inconsistencies in single ontologies, and inconsistencies introduced by integrating multiple distributed ontologies. Our RaDON system provides functionalities to repair and diagnose ontology networks by extending the capabilities of existing reasoners. The system integrates several new debugging and repairing algorithms, such as a relevance-directed algorithm to meet the various needs of the users.

Zhishi.me: weaving chinese linking open data

Linking Open Data (LOD) has become one of the most important community efforts to publish high-quality interconnected semantic data. Such data has been widely used in many applications to provide intelligent services like entity search, personalized recommendation and so on. While DBpedia, one of the LOD core data sources, contains resources described in multilingual versions and semantic data in English is proliferating, there is very few work on publishing Chinese semantic data. In this paper, we present Zhishi.me, the first effort to publish large scale Chinese semantic data and link them together as a Chinese LOD (CLOD). More precisely, we identify important structural features in three largest Chinese encyclopedia sites (i.e., Baidu Baike, Hudong Baike, and Chinese Wikipedia) for extraction and propose several data-level mapping strategies for automatic link discovery. As a result, the CLOD has more than 5 million distinct entities and we simply link CLOD with the existing LOD based on the multilingual characteristic of Wikipedia. Finally, we also introduce three Web access entries namely SPARQL endpoint, lookup interface and detailed data view, which conform to the principles of publishing data sources to LOD.

A Modularization-Based Approach to Finding All Justifications for OWL DL Entailments

Finding the justifications for an entailment (i.e., minimal sets of axioms responsible for it) is a prominent reasoning service in ontology engineering, as justifications facilitate important tasks like debugging inconsistencies or undesired subsumption. Though several algorithms for finding all justifications exist, issues concerning efficiency and scalability remain a challenge due to the sheer size of real-life ontologies. In this paper, we propose a novel method for finding all justifications in OWL DL ontologies by limiting the search space to smaller modules. To this end, we show that so-called locality-based modules cover all axioms in the justifications. We present empirical results that demonstrate an improvement of several orders of magnitude in efficiency and scalability of finding all justifications in OWL DL ontologies.

Rough operations on Boolean algebras

In this paper, we introduce two pairs of rough operations on Boolean algebras. First we define a pair of rough approximations based on a partition of the unity of a Boolean algebra. We then propose a pair of generalized rough approximations on Boolean algebras after defining a basic assignment function between two different Boolean algebras. Finally, some discussions on the relationship between rough operations and some uncertainty measures are given to provide a better understanding of both rough operations and uncertainty measures on Boolean algebras.

A Kernel Revision Operator for Terminologies -- Algorithms and Evaluation

Revision of a description logic-based ontology deals with the problem of incorporating newly received information consistently. In this paper, we propose a general operator for revising terminologies in description logic-based ontologies. Our revision operator relies on a reformulation of the kernel contraction operator in belief revision. We first define our revision operator for terminologies and show that it satisfies some desirable logical properties. Second, two algorithms are developed to instantiate the revision operator. Since in general, these two algorithms are computationally too hard, we propose a third algorithm as a more efficient alternative. We implemented the algorithms and provide evaluation results on their efficiency, effectiveness and meaningfulness in the context of two application scenarios: Incremental ontology learning and mapping revision.

A Conflict-Based Operator for Mapping Revision

Ontology matching is one of the key research topics in the field of the Semantic Web. There are many matching systems that generate mappings between different ontologies either automatically or semi-automatically. However, the mappings generated by these systems may be inconsistent with the ontologies. Several approaches have been proposed to deal with the inconsistencies between mappings and ontologies. This problem is often called a mapping revision problem, as the ontologies are assumed to be correct, whereas the mappings are repaired when resolving the inconsistencies. In this paper, we first propose a conflict-based mapping revision operator and show that it can be characterized by two logical postulates adapted from some existing postulates for belief base revision. We then provide an algorithm for iterative mapping revision by using an ontology revision operator and show that this algorithm defines a conflict-based mapping revision operator. Three concrete ontology revision operators are given to instantiate the iterative algorithm, which result in three different mapping revision algorithms. We implement these algorithms and provide some preliminary but interesting evaluation results.

Measuring incoherence in description logic-based ontologies

Ontologies play a core role in the success of the Semantic Web as they provide a shared vocabulary for different resources and applications. Developing an error-free ontology is a difficult task. A common kind of error for an ontology is logical contradiction or incoherence. In this paper, we propose some approaches to measuring incoherence in DL-based ontologies. These measures give an ontology engineer important information for maintaining and evaluating ontologies. We implement the proposed approaches using the KAON2 reasoner and provide some preliminary but encouraging empirical results.

Measuring Inconsistency for Description Logics Based on Paraconsistent Semantics

In this paper, we present an approach for measuring inconsistency in a knowledge base. We first define the degree of inconsistency using a four-valued semantics for the description logic

Extending Description Logics with Uncertainty Reasoning in Possibilistic Logic

\mathcal{ALC}