Facts extraction is pivotal for constructing knowledge graphs.Recently, the increasing demand for temporal facts in downstream tasks has led to the emergence of the task of temporal fact extraction.In this paper, we specifically address the extraction of temporal facts from natural language text.Previous studies fail to handle the challenge of establishing time-tofact correspondences in complex sentences.To overcome this hurdle, we propose a timelinebased sentence decomposition strategy using large language models (LLMs) with in-context learning, ensuring a fine-grained understanding of the timeline associated with various facts.In addition, we evaluate the performance of LLMs for direct temporal fact extraction and get unsatisfactory results.To this end, we introduce TSDRE 1 , a method that incorporates the decomposition capabilities of LLMs into the traditional fine-tuning of smaller pre-trained language models (PLMs).To support the evaluation, we construct ComplexTRED, a complex temporal fact extraction dataset.Our experiments show that TSDRE achieves state-of-theart results on both HyperRED-Temporal and ComplexTRED datasets.Shaquille O'Neal is one of only three players to win NBA MVP, All-Star game MVP and Finals MVP awards i n the same year (2000); the other players are Willis Reed i n 1970 and Michael Jordan in 1996 and 1998 .

The traditional particle swarm optimization (PSO) algorithm suffers from shortcomings like easily falling into local optimum and inadequate sharing of information among particles, To ad-dress these limitations and enhance the search capacity of the particle swarm algorithm, we present a novel particle swarm optimization algorithm known as Exponential Dynamic Inertia Weight for Particle Swarm Optimization (ExDyPSO) in this paper. ExDyPSO is composed of two parts: firstly, by introducing dynamic inertia weight based on exponential distributions and acceleration factors that vary with the number of iterations, harmonizes the global and local search capabilities. Secondly, a stochastic particle-based jump-out strategy is proposed to surmount the case of particles falling into stagnation during the search process, thus effectively addressing the issue that PSO is prone to falling into local optimum. To assess the performance of ExDyPSO, we carried out experiments on eight benchmark functions and compared its performance against four alternative PSO variations. The experimental findings demonstrate that ExDyPSO achieves quicker and more accurate convergence towards the global optimal solution, all the while sustaining population diversity.

Information retrieval (IR) methods for KGQA consist of two stages: subgraph extraction and answer reasoning. We argue that current subgraph extraction methods underestimate the importance of structural dependencies among evidence facts. We propose Evidence Pattern Retrieval (EPR) to explicitly model the structural dependencies during subgraph extraction. We implement EPR by indexing the atomic adjacency pattern formed by resource pairs. Given a question, we perform dense retrieval to obtain atomic patterns. We then enumerate their combinations to construct candidate evidence patterns. These evidence patterns are scored using a neural model, and the best one is selected to extract a subgraph for downstream answer reasoning. Experimental results demonstrate that the EPR-based approach has significantly improved the F1 scores of IR-KGQA methods by over 10 points on ComplexWebQuestions and achieves competitive performance on WebQuestionsSP.

Temporal facts, the facts for characterizing events that hold in specific time periods, are attracting rising attention in the knowledge graph (KG) research communities. In terms of quality management, the introduction of time restrictions brings new challenges to maintaining the temporal consistency of KGs and detecting potential temporal conflicts. Previous studies rely on manually enumerated temporal constraints to detect conflicts, which are labor-intensive and may have granularity issues. We start from the common pattern of temporal facts and constraints and propose a pattern-based temporal constraint mining method, PaTeCon. PaTeCon uses automatically determined graph patterns and their relevant statistical information over the given KG instead of human experts to generate time constraints. Specifically, PaTeCon dynamically attaches type restriction to candidate constraints according to their measuring scores. We evaluate PaTeCon on two large-scale datasets based on Wikidata and Freebase respectively, the experimental results show that pattern-based automatic constraint mining is powerful in generating valuable temporal constraints.

Answering factual questions with temporal intent over knowledge graphs (temporal KGQA) attracts rising attention in recent years.In the generation of temporal queries, existing KGQA methods ignore the fact that some intrinsic connections between events can make them temporally related, which may limit their capability.We systematically analyze the possible interpretation of temporal constraints and conclude the interpretation structures as the Semantic Framework of Temporal Constraints, SF-TCons.Based on the semantic framework, we propose a temporal question answering method, SF-TQA, which generates query graphs by exploring the relevant facts of mentioned entities, where the exploring process is restricted by SF-TCons.Our evaluations show that SF-TQA significantly outperforms existing methods on two benchmarks over different knowledge graphs.

In this article, we consider a class of decentralized nonsmooth composite optimization problems over undirected graphs. The global optimization problem is to minimize the sum of local objective functions consisting of a Lipschitz-differentiable convex function and two possibly nonsmooth convex functions, one of which contains a bounded linear operator. The goal is to solve the global optimization problem through decentralized computation and communication over a network of agents without a central coordinator. Through using triple proximal splitting operators to deal with the nonsmooth terms, we come up with a novel decentralized algorithm with uncoordinated stepsizes, where the stepsizes with independent upper bounds are also distributed for agents or edges over the communication network. Furthermore, we establish the sublinear convergence rate for the proposed algorithm in terms of the first-order optimality residual in a nonergodic sense. Simulation experiments on a constrained quadratic programming problem and an optimal load-sharing problem are carried out to verify the correctness of the theoretical results.

Recognizing time expressions is a fundamental and important task in many applications of natural language understanding, such as reading comprehension and question answering. Several newest state-of-the-art approaches have achieved good performance on recognizing time expressions. These approaches are black-boxed or based on heuristic rules, which leads to the difficulty in understanding the temporal information. On the contrary, classic rule-based or semantic parsing approaches can capture rich structural information, but their performances on recognition are not so good. In this paper, we propose a pattern-based approach, called PTime, which automatically generates and selects patterns for recognizing time expressions. In this approach, time expressions in training text are abstracted into type sequences by using fine-grained token types, thus the problem is transformed to select an appropriate subset of the sequential patterns. We use the Extended Budgeted Maximum Coverage (EBMC) model to optimize the pattern selection. The main idea is to maximize the correct token sequences matched by the selected patterns while the number of the mistakes should be limited by an adjustable budget. The interpretability of patterns and the adjustability of permitted number of mistakes make PTime a very promising approach for many applications. Experimental results show that PTime achieves a very competitive performance as compared with existing state-of-the-art approaches.

To embrace the open data movement, increasingly many datasets have been published on the Web to be reused. Users, when assessing the usefulness of an unfamiliar dataset, need means to quickly inspect its contents. To satisfy the needs, we propose to automatically extract an optimal small portion from a dataset, called a snippet, to concisely illustrate the contents of the dataset. We consider the quality of a snippet from three aspects: coverage, familiarity, and cohesion, which are jointly formulated in a new combinatorial optimization problem called the maximum-weight-and-coverage connected graph problem (MwcCG). We give a constant-factor approximation algorithm for this NP-hard problem, and experiment with our solution on real-world datasets. Our quantitative analysis and user study show that our approach outperforms a baseline approach.

The quantity of entities in the Linked Data is increasing rapidly. For entity search and browsing systems, filtering is very useful for users to find entities that they are interested in. Type is a kind of widely-used facet and can be easily obtained from knowledge bases, which enables to create filters by selecting at most K types of an entity collection. However, existing approaches often fail to select high-quality type filters due to complex overlap between types. In this paper, we propose a novel type selection approach based upon Budgeted Maximum Coverage (BMC), which can achieve integral optimization for the coverage quality of type filters. Furthermore, we define a new optimization problem called Extended Budgeted Maximum Coverage (EBMC) and propose an EBMC-based approach, which enhances the BMC-based approach by incorporating the relevance between entities and types, so as to create sensible type filters. Our experimental results show that the EBMC-based approach performs best comparing with several representative approaches.