Abstract In the research on dynamics of argumentation frameworks (AFs), the enforcement problem deals with changing an AF for the purpose of ensuring that a certain set of desired arguments becomes (part of) an extension. In this paper we focus on expansions of an AF where solely the addition of new arguments and attacks is allowed and the original framework remains unchanged. Existing results about the enforcement problem are all sufficient conditions, and we argue that necessary and sufficient conditions are essential concerning the solvability of the enforcement problem. A suite of jointly necessary and sufficient conditions are identified for the enforcement problem under normal expansion. These conditions are presented from multiple perspectives including the sub-strict versus non-strict problem, constructive versus eliminative conditions and arbitrary AFs versus even(odd)-length cycle free AFs, determined by the nature of various argumentation semantics. Such necessary and sufficient conditions are of the essence to the enforcement problem by defining requirements that are both minimum and absolute, which can be used to determine when the enforcement problem is (un)solvable. We further infer solely sufficient and solely necessary conditions based on the partial order relation (w.r.t. set-inclusion) among various types of AF extensions.

MOTIVATION: Regulatory elements (REs), such as enhancers and promoters, are known as regulatory sequences functional in a heterogeneous regulatory network to control gene expression by recruiting transcription regulators and carrying genetic variants in a context specific way. Annotating those REs relies on costly and labor-intensive next-generation sequencing and RNA-guided editing technologies in many cellular contexts. RESULTS: We propose a systematic Gene Ontology Annotation method for Regulatory Elements (RE-GOA) by leveraging the powerful word embedding in natural language processing. We first assemble a heterogeneous network by integrating context specific regulations, protein-protein interactions and gene ontology (GO) terms. Then we perform network embedding and associate regulatory elements with GO terms by assessing their similarity in a low dimensional vector space. With three applications, we show that RE-GOA outperforms existing methods in annotating TFs' binding sites from ChIP-seq data, in functional enrichment analysis of differentially accessible peaks from ATAC-seq data, and in revealing genetic correlation among phenotypes from their GWAS summary statistics data. AVAILABILITY AND IMPLEMENTATION: The source code and the systematic RE annotation for human and mouse are available at https://github.com/AMSSwanglab/RE-GOA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

After entering the big data era, a new term of `big knowledge' has been coined to deal with challenges in mining a mass of knowledge from big data. While researchers used to explore the basic characteristics of big data, we have not seen any studies on the general and essential properties of big knowledge. To fill this gap, this paper studies the concepts of big knowledge, big-knowledge system, and big-knowledge engineering. Ten massiveness characteristics for big knowledge and big-knowledge systems, including massive concepts, connectedness, clean data resources, cases, confidence, capabilities, cumulativeness, concerns, consistency, and completeness, are defined and explored. Based on these characteristics, a comprehensive investigation is conducted on some large-scale knowledge engineering projects, including the Fifth Comprehensive Traffic Survey in Shanghai, the China's Xia-Shang-Zhou Chronology Project, the Troy and Trojan War Project, and the International Human Genome Project, as well as the online free encyclopedia Wikipedia. We also investigate the recent research efforts on knowledge graphs, where they are analyzed to determine which ones can be considered as big knowledge and big-knowledge systems. Further, a definition of big-knowledge engineering and its life cycle paradigm is presented. All of these projects are accordingly checked to determine whether they belong to big-knowledge engineering projects. Finally, the perspectives of big knowledge research are discussed.

Ontology mappings are regarded as the semantic bridges that link entities from different yet overlapping ontologies in order to support knowledge sharing and reuse on the Semantic Web. However, mappings can be wrong and result in logical conflicts among ontologies. Such kind of mappings are called incoherent mappings. As an important part of ontology matching, mapping validation aims at detecting the conflicts and restoring the coherence of mappings. In this paper, we propose a graph-based approach which is complete for detecting incoherent mappings among DL-Lite ontologies. The lightweight DL-Lite family of description logics stand out for tractable reasoning and efficient query answering capabilities. Our approach consists of a set of graph construction rules, a graph-based incoherence detection algorithm, and a graph-based incoherence repair algorithm. We propose and formalize three repair principles in an attempt to measure the wrong mappings, where the notion of common closures w.r.t. a mapping arc in the constructed graph is introduced. These principles feature a global removal strategy that is independent of individual ontology matchers. In order to relieve the loss of information among ontologies in the repair process, we further define a mapping revision operator so that common closures related to the removed mappings can be preserved in the graph. We implement the graph-based algorithms and evaluate their performance in a comparison with state-of-the-art systems on real-world ontologies. Experimental results show that our approach can remove more wrong mappings and achieve better repairing results in most of the cases.

This paper reports a social media analysis study proposed by the Beijing Municipal Institute of Urban Planning and Design. The purpose is to explore techniques that can help the urban planning administrations to improve the social sensing and social perception abilities under the evolving data and technology environments. A framework integrating a comprehensive set of text mining algorithms is presented to conduct topic modeling, text clustering, event evolution detection, sentiment analysis, opinion mining, and information extraction on user-generated contents in Chinese social media. A domain ontology of Beijing urban planning is constructed to facilitate the text mining processes. Evaluations on two large, real-world datasets composed of microblogs and WeChat articles about the residential community and school education in Beijing demonstrate the effectiveness of our framework. The study illustrates the power of combining machine learning with knowledge-based, semantic approaches in analyzing social media for the domain of interest.

We propose to use Semantic Web technologies to support the automatic generation of 3D computer animations. One of our projects is in the domain of Chinese traditional architecture. More precisely, the user describes in natural language the house he/she wants to build, and our system eventually generates an animation to demonstrate how the timber structure is constructed by every single piece of wood. The processing in between includes information retrieval, reasoning of various types of components needed, computation of the size and position of every wood in the 3D scene, reasoning of construction sequence, and generation of animation descriptions. We have built a knowledge base consisting of one ontology and four rule bases to support the whole automation. Different Semantic Web formalisms are used including OWL DL, SWRL and Jess language. The inference engine Pellet is used to ensure the logical consistency of the ontology, and the rule engine Jess to implement the size and position computation and sequence reasoning. Our experience in using the Semantic Web technologies in real-world systems validates their prominent advantages including the reasoning facilities and modeling tools. At the meantime, limitations are revealed including low efficiency in processing large knowledge bases.

In this study we constructed rules to systematically identify the differences as well as similarity in partonomy between two large biomedical ontologies. For a group of one-to-one mapped concepts, we distinguish among different types of parts defined for these mapped concepts, and the structural imbalance between two systems. As a result, 1.4% of the concept mappings have exactly the same part-whole relations, 68.4% do not have any parts, and 30.2% mappings are modeled differently, including additional parts, more detailed part paths, and different intermediate concepts in the part paths. This is a parallel study to our previous work where granularity mismatches in is-a classification among ontologies were identified. Using rules and the rule inference engine enables an automatic and scalable investigation of the structural incompatibility among biomedical ontologies.

Color knowledge representation is an essential aspect for designing intelligent graphic systems. Since the mapping between low-level color numerical values and high-level semantics is crucial, the theory of fuzzy numbers is greatly needed for representing color concepts. The limited linguistic color words bring about the requirement for representing intermediate color concepts. However, there is not corresponding definitions in the fuzzy realm. To overcome this representational inadequacy, this work addresses the issue of formally defining the intermediate fuzzy concept. Since definitions of the intermediate fuzzy numbers need a well-defined order between two basic fuzzy numbers, this paper first presents a cognition based linguistic ranking method. Then two novel definitions, namely, intermediate fuzzy numbers and intermediate operations on fuzzy numbers, are presented. The definitions are of abstract ones in order for general use. Besides, the paper also presents and studies some specific intermediate fuzzy numbers and intermediate operations, which are important for modelling gradual changes between two color terms in Color Naming System.

A significant set of challenges to the use of large, source ontologies in the medical domain include: automated translation, customization of source ontologies, and performance issues associated with the use of logical reasoning systems to interpret the meaning of a domain captured in a formal knowledge representation. SNOMED-CT and FMA are two reference ontologies that cover much of the domain of clinical informatics and motivate a better means for re-use. In this paper, we present a method for segmenting and merging modules from these ontologies for a specific domain that preserve the meaning of the anatomy terms they have in common.

An ontology is a formal representation of a domain modeling the entities in the domain and their relations. When a domain is represented by multiple ontologies, there is need for creating mappings among these ontologies in order to facilitate the integration of data annotated with these ontologies and reasoning across ontologies. The objective of this paper is to recapitulate our experience in aligning large anatomical ontologies and to reflect on some of the issues and challenges encountered along the way. The four anatomical ontologies under investigation are the Foundational Model of Anatomy, GALEN, the Adult Mouse Anatomical Dictionary and the NCI Thesaurus. Their underlying representation formalisms are all different. Our approach to aligning concepts (directly) is automatic, rule-based, and operates at the schema level, generating mostly point-to-point mappings. It uses a combination of domain-specific lexical techniques and structural and semantic techniques (to validate the mappings suggested lexically). It also takes advantage of domain-specific knowledge (lexical knowledge from external resources such as the Unified Medical Language System, as well as knowledge augmentation and inference techniques). In addition to point-to-point mapping of concepts, we present the alignment of relationships and the mapping of concepts group-to-group. We have also successfully tested an indirect alignment through a domain-specific reference ontology. We present an evaluation of our techniques, both against a gold standard established manually and against a generic schema matching system. The advantages and limitations of our approach are analyzed and discussed throughout the paper.