Abstract. 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.

Recently, the problem of inconsistency handling in description logics has attracted a lot of attention. Many approaches have been proposed to deal with this problem based on existing techniques for inconsistency management. In this paper, we first define two revision operators in description logics; one is called a weakening-based revision operator and the other is its refinement. Based on the revision operators, we then propose an algorithm to handle inconsistency in a stratified description logic knowledge base. We show that when the weakening-based revision operator is chosen, the resulting knowledge base of our algorithm is semantically equivalent to the knowledge base obtained by applying refined conjunctive maxi-adjustment (RCMA) which refines disjunctive maxi-adjusment (DMA), known to be a good strategy for inconsistency handling in classical logic.

Abstract. Ontology evolution is an important problem in the Semantic Web research. Recently, Alchourrón, Gärdenfors and Markinson's (AGM) theory on belief change has been applied to deal with this problem. However, most of current work only focuses on the feasibility of the application of AGM postulates on contraction to description logics (DLs), a family of ontology languages. So the explicit construction of a revision operator is ignored. In this paper, we first generalize the AGM postulates on revision to DLs. We then define two revision operators in DLs. One is the weakening-based revision operator which is defined by weakening of statements in a DL knowledge base and the other is its refinement. We show that both operators capture some notions of minimal change and satisfy the generalized AGM postulates for revision.

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Abstract. 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.

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 ALC. Then an ordering over knowledge bases is given by considering their inconsistency degrees. Our measure of inconsistency can provide important information for inconsistency handling. We acknowledge support by the China Scholarship Council(http://www.csc.edu.cn/), by the German Federal Ministry of Education and Research (BMBF) under the SmartWeb project (grant 01 IMD01 B), by the EU under the IST project NeOn (IST-2006-027595, http://www.neon-project.org/), and by the Deutsche Forschungsgemeinschaft (DFG) in the ReaSem project.

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Formal query building is an important part of complex question answering over knowledge bases. It aims to build correct executable queries for questions. Recent methods try to rank candidate queries generated by a state-transition strategy. However, this candidate generation strategy ignores the structure of queries, resulting in a considerable number of noisy queries. In this paper, we propose a new formal query building approach that consists of two stages. In the first stage, we predict the query structure of the question and leverage the structure to constrain the generation of the candidate queries. We propose a novel graph generation framework to handle the structure prediction task and design an encoder-decoder model to predict the argument of the predetermined operation in each generative step. In the second stage, we follow the previous methods to rank the candidate queries. The experimental results show that our formal query building approach outperforms existing methods on complex questions while staying competitive on simple questions.