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Formal Concept Analysis (FCA) is a field of applied mathematics with its roots in order theory. Over the past 20 years, FCA has been widely studied. Knowledge Graphs (KGs) model factual information in the form of entities and relations between them to semantically represent the world's truth. Since existing semantic FCA (or ontology-based FCA) only relies on ontological knowledge, ontology-based FCA is limited in scope and scalability. This paper theoretically and empirically investigates a new semantic FCA by exploiting KGs to solve the scalability of ontology-based FCA. Concretely, we propose a kind of novel semantic FCA (i.e., KGs-based Formal Concept Analysis, KGs-based FCA) by semantifing FCA based on KGs. We further expand KGs-based FCA and propose a KGs-based FFCA (KGs-based Fuzzy Formal Concept Analysis) by extending KGs-based FCA. We also investigate the properties of KGs-based FCA and KGs-based FFCA in theory. The experimental results show that our proposals (KGs-based FCA and KGs-based FFCA) significantly outperform other traditional FCA and semantic FCA in information retrieval, and is 15.96–16.30 higher in Retrieval Results Ratio (R3) than other traditional FCA and semantic FCA. [ABSTRACT FROM AUTHOR]

In recent years, researchers have proposed several feature-based methods to measure semantic similarity using knowledge resources like Wikipedia and WordNet. While Wikipedia covers millions of concepts with multiple features, it has some limitations such as articles with limited content and concept ambiguity. Disambiguating these concepts remains a challenge. Conversely, WordNet offers unambiguous terms by covering all possible senses, making it a useful resource for disambiguating Wikipedia concepts. Additionally, WordNet can enrich the limited content of Wikipedia articles. Thus, we present a new approach that combines both resources to enhance previous feature-based methods of semantic similarity. We begin by analyzing the limitations of previous research, followed by introducing a novel method to disambiguate Wikipedia concepts using WordNet's synonym structure, resulting in more effective disambiguation. Furthermore, we use WordNet to supplement the features in Wikipedia articles and redefine the feature similarity functions. Finally, we train non-linear fitting-based models to measure semantic similarity. Our approach outperforms other previous methods on various benchmarks. To further showcase our approach, we apply our models to develop a movie recommender system using the MovieLens dataset, which consistently outperforms other systems. [ABSTRACT FROM AUTHOR]

User preference learning is an important process in automated negotiation, because only when the negotiating agents are able to fully grasp the user preference information can the negotiation strategy play its due role. However, in most automated negotiation systems, user preference is assumed to be complete and correct, which is quite different from the reality. In real life, user preference is often complex and incomplete, which hinders the application of automated negotiation research in practice. To this end, this paper focuses on the learning method of user preference in negotiation. Since CP-nets can intuitively express the interdependence among negotiation issues, which have good interpretability and expansibility, they have become one of most important representations of user preference in automated negotiation. Therefore, we propose a CP-nets-based user preference learning module in negotiation framework, which consists of both passive learning and active learning methods. In passive learning, we propose an algorithm to construct complete CP-nets with incomplete user preference information. In active learning, we innovatively propose the structural query method, which improves the accuracy of preference learning represented by CP-nets with less query cost. The experimental results show that the module is effective for negotiation framework and can help users reach better agreements in negotiation. [ABSTRACT FROM AUTHOR]

Semantic similarity assessment between concepts is an important task in many language related applications. In the past, many approaches to assess similarity of concepts have been proposed by using one knowledge source. In this paper, some limitations of the existing similarity measures are identified. To tackle these problems, we propose an extensive study for semantic similarity of concepts from which a unified framework for semantic similarity computation is presented. Based on our framework, we give some generic and flexible approaches to semantic similarity measures resulting from instantiations of the framework. In particular, we obtain some new approaches to similarity measures that existing methods cannot deal with by introducing multiple knowledge sources. The evaluation based on eight benchmarks, three widely used benchmarks (i.e., M&C, R&G, and WordSim-353 benchmarks) and five benchmarks developed in ourselves (i.e, Jiang-1, Jiang-2, Jiang-3, Jiang-4, and Jiang-5 benchmarks), sustains the intuitions with respect to human judgements. Overall, some methods proposed in this paper have a good human correlation (Pearson correlation with human judgments and Spearman correlation with human judgments) and constitute some effective ways of determining semantic similarity between concepts. [ABSTRACT FROM AUTHOR]

Researches about semantic computing are usually evaluated by experiments, lacking support from the theory of computation. But traditional finite automata are defined on a fixed finite set of states. They will be invalid when a different application needs to change the states. However, semantic computing always asks for more robust automata that can modify itself according to the changes of states. Therefore, in this paper, we redefine traditional automata in more robust forms by semantic computing. The intuitive idea is when the state which an automaton is supposed to enter has changed, we drive automata into a state that is similar to the original one. We can find another similar state in the original state set to replace the changed state by semantic similarity. Based on the theories of semantic similarity, there always exists a similar state. Therefore, once a state has changed, we can always find a similar state to replace it, which means that we can empower automata to adapt to the changes of states. Our new automata bridge the gap between semantic computing and the theory of computation. Furthermore, we also redefine fuzzy finite automata and pushdown automata in more robust forms. Finally, we provide an application about the weather forecast, which indicates how to overcome the limitations of traditional automata. [ABSTRACT FROM AUTHOR]

Classical or traditional Information Retrieval (IR) approaches rely on the word-based representations of query and documents in the collection. The specification of the user information need is completely based on words figuring in the original query in order to retrieve documents containing those words. Such approaches have been limited due to the absence of relevant keywords as well as the term variation in documents and user's query. The purpose of this paper is to present a new method to Semantic Information Retrieval (SIR) to solve the limitations of existing approaches. Concretely, we propose a novel method SIRWWO (Semantic Information Retrieval using Wikipedia, WordNet, and domain Ontologies) for SIR by combining multiple knowledge sources Wikipedia, WordNet, and Description Logic (DL) ontologies. In order to illustrate the approach SIRWWO, we first present the notion of Labeled Dynamic Semantic Network (LDSN) by extending the notions of dynamic semantic network and extended semantic net based on WordNet (and DAML ontology library). According to the notion of LDSN, we obtain the notion of Weighted Dynamic Semantic Network (WDSN, intuitively, each edge in WDSN is assigned to a number in the [0, 1] interval) and give the WDSN construction method using Wikipedia, WordNet, and DL ontology. We then propose a novel metric to measure the semantic relatedness between concepts based on WDSN. Lastly, we investigate the approach SIRWWO by using semantic relatedness between users' query keywords and digital documents. The experimental results show that our proposals obtain comparable and better performance results than other traditional IR system Lucene. [ABSTRACT FROM AUTHOR]

Formal Concept Analysis (FCA) is a field of applied mathematics with its roots in order theory, in particular the theory of complete lattices. It is not only a method for data analysis and knowledge representation, but also a formal formulation for concept formation and learning. Over the past 20 years, FCA has been widely studied. In this paper, the current research progresses and the existing problems of similarity measures in FCA are analyzed. To address the drawbacks of the existing methods, we propose a kind of novel semantic similarity measure for FCA by using Linked Data and WordNet. We aim to develop a method that is fully automatic without requiring predefined domain ontologies and can be used independently of the domain in applications requiring semantic similarity measures in FCA. To realize the semantic similarity estimation for FCA, we firstly extend the similarity assessment methods for resources (or entities) in Linked Data into semantic cases by using WordNet. Furthermore, we propose two kinds of semantic similarity measures (i.e., context-free method and context-aware method) for FCA concepts and concept lattices, respectively. Compared with the existing similarity measure methods in FCA, the proposed approach uses concept of possibility theory to determine lower and upper bounds of similarity intervals. Finally, we evaluate the proposed similarity assessment approaches by applying them to real-worlds datasets. [ABSTRACT FROM AUTHOR]

In the existing works of semantic computing (SC), the word "computing" in the phrase "semantic computing" means computational implementations of semantics reasoning (e.g., ontology reasoning, rule reasoning, semantic query, and semantic search) but is irrelevant to the formal theory of computation (e.g., computational models such as finite automaton, pushdown automaton, and Turing machine). In this paper, we propose a different understanding of "semantic computing" from a computation theory perspective. Concretely, we present a formal model of SC in terms of automata and discuss SC for the two most important and simplest types of automata, namely finite automata and pushdown automata. For each automaton, we first consider a simple case (equivalent concepts) and then we further investigate a general situation (semantically related concepts). That is, some new automata for SC are provided: finite (or pushdown) automaton for SC under equivalent concepts, finite (or pushdown) automaton for SC w.r.t. external words, nondeterministic finite automaton for SC under equivalent concepts (or w.r.t. external words), fuzzy finite (or pushdown) automaton for SC under semantically related concepts, and fuzzy finite (or pushdown) automaton for SC w.r.t. external words. Furthermore, we give some properties of these new automata for SC and prove that these new automata are extensions (or enlargements) of traditional (fuzzy) automata. [ABSTRACT FROM AUTHOR]

The current research works and the existing problems of terminological cycles in description logics are analyzed in this paper. Referring to the works of Baader F and Nebel B, we aim in a new direction. Firstly, description logic $$ v\mathcal{L} $$ is defined, and the description graphs GT and GJ are redefined. A syntax condition for the satisfiability of membership relation is given. By using this syntax condition, we prove the following: The subsumption reasoning in $$ v\mathcal{L} $$ with respect to gfp-model, lfp-model and descriptive model is polynomial. [ABSTRACT FROM AUTHOR]

The current research progresses and problems of the semantic Web are analyzed in this paper, and the insufficiency of using description logic to act as logical foundation for the semantic Web is analyzed too. According to the characteristics and requirement of the semantic Web, a kind of new dynamic description logic (DDL) framework is presented. The representation and reasoning of static knowledge and dynamic knowledge are integrated in this framework. Especially, a kind of action description method is proposed, and according to description logic theory, the action semantics is described, so DDL is a kind of formal logical framework which can process static knowledge and dynamic knowledge. The DDL has clear and formally defined semantics. It provides decidable reasoning services, and it can support effective representation and reasoning of the static knowledge, dynamic process and running mechanism (realization and subsumption relation of action). Therefore, the DDL provides reasonable logic foundation for the semantic Web, and overcomes the insufficiency of using description logic to act as logical foundation for the semantic Web. [ABSTRACT FROM AUTHOR]

The objective of this work is to detect underwater sound with light diffracted by liquid surface wave (LSW). An experimental setup for underwater detection with a converter is established. The diffraction patterns by LSW are obtained. By analyzing the fringes, the characteristics of those relating to both amplitude and wavelength of LSW and underwater sound are achieved. Meanwhile, the transformation model of waves from underwater into LSW is proposed. The dependence of the amplitudes on the diameter of the converter is obtained by utilizing droplet-floating model. Finally, a comparison between the model and the experiment supports these observations. [ABSTRACT FROM AUTHOR]