The core idea of semantic recommendation is to incorporate semantic knowledge into the recommendation process. The semantic recommendation algorithm, based on knowledge graph, ignores the deep implicit semantics of the evaluation data. The semantic recommendation algorithm based on the deep matrix decomposition model is limited to the implicit semantics of the evaluation data. The semantic recommendation algorithm based on the collaborative filtering algorithm performs only the selection of the nearest neighbors of the user or the item unilaterally and ignores the influence of other aspects, which naturally leads to a decrease in the recommendation accuracy. To solve the above problems, this paper introduces Formal Concept Analysis (FCA) based on collaborative filtering. Using the property that the formal concept in FCA can cluster objects (users) and attributes (items) simultaneously, we propose a semantic recommendation algorithm (SRKGFCA) based on the knowledge graph and formal concept analysis to solve the problem of ignoring user or item factors. Finally, the proposed semantic recommendation algorithm is validated on two public datasets in this work. By using traditional algorithms and current semantic recommendation algorithms as benchmarks, extensive experiments show that our proposed algorithm consistently outperforms state-of-the-art methods.

In most previous research, “semantic computing” refers to computational implementations of semantic reasoning. It lacks support from the formal theory of computation. To provide solid foundations for semantic computing, researchers propose a different understanding of semantic computing based on finite automata. This approach provides a computer theoretical approach to semantic computing. But finite automata are not capable enough to deal with imprecise knowledge. Therefore, in this paper, we provide foundations for semantic computing based on probabilistic automata. Even though traditional probabilistic automata can handle imprecise knowledge, their limitation resides in their being defined on a fixed finite input alphabet. This deeply restricts the abilities of automata. In this paper, we rebuild traditional probabilistic automata for semantic computing. Furthermore, our new probabilistic automata are robust enough to handle any alphabet as input. They have better performances in many applications. We provide an application for weather forecasting, a domain for which traditional probabilistic automata are not effective due to their finite input alphabet. Our new probabilistic automata can overcome these limitations.