Incomplete Knowledge Base Research Articles

Barriers and Facilitators for Usage of Self-Compacting Concrete—An Interview Study

Workers in the construction industry must endure different weather conditions, long working hours, and engage in repetitive and strenuous jobs with unrealistic deadlines. Sick leaves, caused by accidents and by work-related diseases, are common in the construction industry. Hand–arm vibration from hand-held power tools is a cause of significant ill health (disorders of the blood vessels, nerves, and joints). Self-compacting concrete (SCC) is a fluid concrete and does not need to be vibrated. Despite the health advantages of SCC, its market share in Sweden is lower than in comparable countries. The aim of this article is to describe views, opinions, and knowledge concerning the work environment and health in concrete casting and to identify barriers and facilitators of SCC usage. Semi-structured interviews were conducted with 24 interviewees from the construction industry in Sweden. The answers were analysed from a human–technology–organisation (HTO) perspective in order to identify barriers and facilitators for a broader usage of SCC. The results indicate that knowledge about SCC is low within the Swedish construction industry, including educational institutions; when SCC is chosen, it is chosen exclusively due to its technical characteristics, and not because it eliminates vibrations. Barriers to a broader usage of SCC comprise an incomplete knowledge base, clients who never choose it, recipes that are said to be too demanding, and workplace traditions. Facilitators comprise large companies investing in knowledge development about SCC and engaged persons promoting it. This study used an HTO-based model (BTOH) to identify barriers and facilitators for a broader usage of SCC, thus contributing to a deeper understanding of reasons for the low usage of SCC and ways of increasing it.

Completeness, Recall, and Negation in Open-world Knowledge Bases: A Survey

General-purpose knowledge bases (KBs) are a cornerstone of knowledge-centric AI. Many of them are constructed pragmatically from web sources and are thus far from complete. This poses challenges for the consumption as well as the curation of their content. While several surveys target the problem of completing incomplete KBs, the first problem is arguably to know whether and where the KB is incomplete in the first place, and to which degree. In this survey, we discuss how knowledge about completeness, recall, and negation in KBs can be expressed, extracted, and inferred. We cover (i) the logical foundations of knowledge representation and querying under partial closed-world semantics; (ii) the estimation of this information via statistical patterns; (iii) the extraction of information about recall from KBs and text; (iv) the identification of interesting negative statements; and (v) relaxed notions of relative recall. This survey is targeted at two types of audiences: (1) practitioners who are interested in tracking KB quality, focusing extraction efforts, and building quality-aware downstream applications; and (2) data management, knowledge base, and semantic web researchers who wish to understand the state-of-the-art of knowledge bases beyond the open-world assumption. Consequently, our survey presents both fundamental methodologies and the results that they have produced, and gives practice-oriented recommendations on how to choose between different approaches for a problem at hand.

Re-Establishing Naturally Reproducing Sturgeon Populations in the Caspian Basin: A Wicked Problem in the Ural River

Although Eurasia’s Caspian basin once supported the world’s richest and most diverse complex of sturgeon species, recent human activities have decimated populations of these ecologically and economically important fish. All five anadromous Caspian sturgeon species are critically endangered, and the potamodromous sterlet is also threatened. The precipitous decline of these species is due to a combination of factors that includes illegal, unreported, and unregulated (IUU) fishing; destruction of feeding and spawning habitat; water pollution; and the environmental consequences of climate change. International efforts are currently underway to re-establish sustained naturally reproducing sturgeon populations in the basin. Here, we update and review the status of sturgeon in the Caspian Sea with emphasis on the northern basin and the inflowing Volga and Ural rivers. We then focus on efforts to restore sturgeon in the Ural, which originates in Russia and flows through Kazakhstan before entering the Caspian Sea. With nearly ideal hydrological conditions for sturgeon, the Ural is the basin’s sole remaining river that allows migrating sturgeon unimpeded access to potentially productive spawning grounds. The challenge of re-establishing sturgeon in the Ural River exhibits the classical characteristics of wicked problems: ambiguous definitions, changing assumptions and unanticipated consequences, tradeoffs and economic dependencies, an incomplete and contradictory knowledge base, and no straightforward pathway toward a final solution. This challenge is examined here for the first time from the perspective of its wicked dynamics, with consideration given to approaches that have proven effective elsewhere in resolving wicked environmental problems.

Predict, pretrained, select and answer: Interpretable and scalable complex question answering over knowledge bases

Complex question answering (CQA) over knowledge bases(KB) is a challenging task that has attracted increasing attention in recent years. Semantic parsing-based methods face challenges such as poor adaptability for incomplete KB, large search spaces, and high costs to label logic forms. The gap between knowledge graph representations and question token embeddings leads to poor generalizability and uninterpretable reasoning of information retrieval-based methods. We propose an interpretable and scalable system called Predict, Pretrained, Select and Answer (PPSA) to solve CQA tasks over KB. Our system first trains a language model to predict the reasoning paths required to answer questions. We select only the entities that the predicted reasoning paths pass through in the knowledge graph as candidate entities to reduce the amount of distracting information. The paths that connect the topic entity and the selected candidate entity along with the question are then fed into another language model for answer prediction. The answer prediction module loads the parameters of the trained path prediction module before training to improve accuracy. The system reduces the search space by predicting the path and does not need expensive logic forms annotation. The textual path is the input to the language model, which bridges the gap between the graph representations and token embeddings. We analyse the system reasoning ability over knowledge graphs with different degrees of sparseness, and evaluate the system generalizability. The results of experiments performed with the Complex WebQuestions and WebQuestionsSP datasets demonstrate the effectiveness of our approach for CQA task.

An integrated approach to fast model-based process design: Integrating superstructure optimization under uncertainties and optimal design of experiments

During the early-stage design of chemical production processes many decisions have to be made on the basis of incomplete knowledge about the underlying chemical and physical phenomena. Therefore, optimization-based approaches are often applied only in a later stage when more knowledge has been generated. In this work, an integrated approach to fast, model and optimization based process design for the selection of reactors and separation networks is discussed. The approach is based on superstructure optimization under uncertainties about the parameters of the models using a two-stage scenario-based approach. Usually, due to the uncertainties, structural decisions cannot be made in the initial stage as different structures are superior for different scenarios of the uncertain parameters. In order to arrive at such decisions, the models need to be refined based on experimental studies. We combine design of experiments with optimization under uncertainty to optimize the experiments such that the information obtained about those parameters that are critical with respect to taking structural design decisions is maximized. We apply the methodology to the development of a process for the hydroaminomethylation of 1-decene in a thermomorphic solvent system. It is shown that the integrated approach can help to significantly reduce the number of required experiments. Using the integrated approach, only 7 experiments need to be performed while more than 16 experiments following a full-factorial experimental design do not provide the same reduction of the design space.

HRER: A New Bottom-Up Rule Learning for Knowledge Graph Completion

Knowledge graphs (KGs) are collections of structured facts, which have recently attracted growing attention. Although there are billions of triples in KGs, they are still incomplete. These incomplete knowledge bases will bring limitations to practical applications. Predicting new facts from the given knowledge graphs is an increasingly important area. We investigate the models based on logic rules in this paper. This paper proposes HRER, a new bottom-up rule learning for knowledge graph completion. First of all, inspired by the observation that the known information of KGs is incomplete and unbalanced, HRER modifies the indicators for screening based on the existing relation rule mining methods. The new metric HRR is more effective than traditional confidences in filtering Horn rules. Besides, motivated by the differences between the embedding-based methods and the methods based on logic rules, HRER proposes entity rules. The entity rules make up for the limited expression of Horn rules to some extent. HRER needs a few parameters to control the number of rules and can provide the explanation for prediction. Experiments show that HRER achieves the state-of-the-art across the standard link prediction datasets.

Abductive Metareasoning for Truth-Seeking Agents

My research seeks to answer the question of how any agent that is tasked with making sense of its world, by finding explanations for evidence (e.g., sensor reports) using domain-general strategies, may accurately and efficiently handle incomplete evidence, noisy evidence, and an incomplete knowledge base. I propose the following answer to the question. The agent should employ an optimal abductive reasoning algorithm (developed piece-wise and shown to be best in a class of similar algorithms) that allows it to reason from evidence to causes. For the sake of efficiency and operational concerns, the agent should establish beliefs periodically rather than waiting until it has obtained all evidence it will ever be able to obtain. If the agent commits to beliefs on the basis of incomplete or noisy evidence or an incomplete knowledge base, these beliefs may be incorrect. Future evidence obtained by the agent may result in failed predictions or anomalies. The agent is then tasked with determining whether it should retain its beliefs and therefore discount the newly-obtained evidence, revise its prior beliefs, or expand its knowledge base (what can be described as anomaly-driven or explanation-based learning). I have developed an abductive metareasoning procedure that aims to appropriately reason about these situations. Preliminary experiments in two reasoning tasks indicate that the procedure is effective.

What Happens to Bad Actors in Organizations? A Review of Actor-Centric Outcomes of Negative Behavior

Negative workplace behavior has received substantial research attention over the past several decades. Although we have learned a lot about the consequences of negative behavior for its victims and third-party observers, a less understood but equally important research question pertains to the consequences for bad actors: How does engaging in negative behavior impact one’s thoughts, feelings, and subsequent behaviors? Moreover, do organizational members experience costs or benefits from engaging in negative acts? We address these questions with an integrative review of empirical findings on various actor-centric consequences of a wide range of negative behaviors. We organize these findings into five dominant theoretical perspectives: affective, psychological-needs, relational, psychological-resources, and cognitive-dissonance perspectives. For each perspective, we provide an overview of the theoretical arguments, summarize findings of relevant studies underlying it, and discuss observed patterns and contradictory findings. By doing so, we provide a very tentative answer to our initial questions, contending that engaging in negative acts is a two-edged sword for actors and its costs seem to slightly prevail over its benefits. Nevertheless, we make this preliminary conclusion based upon an incomplete knowledge base. In order to further our understanding of actor-centric outcomes of negative behavior, we also identify several important research gaps and needed future research directions.

KBPearl

Nowadays, most openly available knowledge bases (KBs) are incomplete, since they are not synchronized with the emerging facts happening in the real world. Therefore, knowledge base population (KBP) from external data sources, which extracts knowledge from unstructured text to populate KBs, becomes a vital task. Recent research proposes two types of solutions that partially address this problem, but the performance of these solutions is limited. The first solution, dynamic KB construction from unstructured text, requires specifications of which predicates are of interest to the KB, which needs preliminary setups and is not suitable for an in-time population scenario. The second solution, Open Information Extraction (Open IE) from unstructured text, has limitations in producing facts that can be directly linked to the target KB without redundancy and ambiguity. In this paper, we present an end-to-end system, KBPearl, for KBP, which takes an incomplete KB and a large corpus of text as input, to (1) organize the noisy extraction from Open IE into canonicalized facts; and (2) populate the KB by joint entity and relation linking, utilizing the context knowledge of the facts and the side information inferred from the source text. We demonstrate the effectiveness and efficiency of KBPearl against the state-of-the-art techniques, through extensive experiments on real-world datasets.

Researching experiences of childhood brain injury: co-constructing knowledge with children through arts-based research methods

Objective: In the knowledge base examining experiences of childhood brain injury, the perspectives of children with brain injury are notably lacking. This failure to represent the voices of pediatric clients has resulted in an incomplete knowledge base from which to inform evidence-based rehabilitation practice. In this paper, we examine why the perspectives of children with brain injury are rarely sought and propose a new way forward.Methods: We draw upon current evidence and practices in related fields and present an exemplar from an in-progress qualitative arts-based research project with children with brain injury.Results: Assumptions ingrained in research practices, particularly those surrounding the capacity of ‘doubly vulnerable’ children with brain injury to produce knowledge, have resulted in the relative exclusion of this group from research that concerns them. For the field to evolve, research practices must value children’s first-hand accounts, engage them in co-constructing knowledge about their lives, and invite methods that meet their interests and abilities.Conclusion: By reframing how we think about the capacities of children with disabilities and shifting our research practices to include children with brain injury as knowledgeable participants, it becomes possible to expand the knowledge base upon which clinical rehabilitation practices are built.Implications for RehabilitationCurrent research practices preclude ‘doubly vulnerable’ pediatric populations, including children with brain injury, from actively contributing to research that concerns them.As a result, the perspectives of children with brain injury are virtually absent from research, rendering the evidence base upon which rehabilitative practices are built incomplete.Reframing how we think about the capacities of children with brain injury can shift how we engage with them, both in research and clinic, and may subsequently impact the knowledge available to us.Implications for client-centred rehabilitation are discussed, including the need to understand children as capable of insight into their own experience, as able to contribute to a more complete understanding of the health phenomena that affect them, and as invaluable and active participants in research and clinical care.

Shared representations of actions for alternative suggestion with incomplete information

Common coding theory posits there are cycles between perceptions and actions at the fundamental logic of the nervous system. An action performed by an agent modifies its external environment and/or internal states. The agent is able to complete a given goal by performing a sequence of actions. In this work, shared representations for both perception and action and their processing algorithm are presented that suggest goal-oriented alternative actions even with incomplete information. The alternative actions provide more opportunities for a service robot to achieve goals. Knowledge plays significant roles to successfully complete service tasks. Most knowledge inference mechanisms assume complete and correct knowledge about the environment. Real world environments are often uncertain and only partially observable. Thus, intelligent service robots may have an incomplete knowledge base which includes false negatives and false positives as well as true positives. False negatives and false positives can prevent service robots from completing their service tasks. A case study reveals that the proposed method has proved valuable to suggest alternatives for false information as well as to build reactive plans with customary efficiency.

Assigning matrix metalloproteinase roles in ischaemic cardiac remodelling

Matrix metalloproteinases (MMPs) and their endogenous inhibitors have been studied in the myocardium for the past 2 decades. An incomplete knowledge base and experimental design issues with inhibitors have hampered attempts at translation, but clinical interest remains high because of strong associations between MMPs and outcomes after myocardial infarction (MI) as well as mechanistic studies showing MMP involvement at multiple stages of the MI wound-healing process. This Review focuses on how our understanding of MMPs has evolved from a one-dimensional early focus on measuring MMP activity, monitoring MMP:inhibitor ratios, and evaluating one MMP-substrate pair to the current use of systems biology approaches to integrate the whole MMP repertoire of roles in the left ventricular response to MI. MMP9 is used as an example MMP to explain these concepts and to provide a template for examining MMPs as mechanistic mediators of cardiac remodelling.

Collective Entity Linking Method in Chinese Text Based on Topic Consistency

Entity Linking refers to the task of linking entity mentions in the given text with their referent entities in a knowledge base, which is a key technology of knowledge base expansion. However, the performance of traditional Chinese entity linking methods are affected by the incomplete Chinese knowledge base. Also they rarely use the semantic relevance between entities. Therefore, we propose a Chinese collective entity linking method based on the consistency of the topic, which considers both the content similarity and topic relevance of the co-occurrence entities, and propose a method for calculating the topic consistency of entities. This method implements batch links for multiple ambiguous entities that appear in the same text, and reduces the reliance on the local knowledge base by using the combination of the local knowledge base and the external knowledge base. Experimental results show that our method performs well over the traditional methods. And it is potentially effective.

Method of Consequences Inference from New Facts in Case of an Incomplete Knowledge Base

Objectives: Incomplete information contained in a knowledge base is one of the serious problems hampering the achievement of goals in the field of applied artificial intelligence. This paper provides a content-related statement and a formal description of the problem of consequences inference in view of an incomplete knowledge base. Methods/Statistical Analysis: The author proposed the original method that enables not only to detect consequences from new facts, but also determine additional facts that are needed for consequences inference. The special features of implementation of all stages of the methods are illustrated by an example. To confirm the correctness of the proposed method, a program of consequence inference in the propositional calculus has been developed. Findings: The major advantage of the suggested method is the parallel execution of disjuncts division operations in logical inference procedures. This will significantly (several times) improve the performance of inference engine implemented on the basis of this method. Application/Improvements: The method of consequences inference from new facts in case of an incomplete knowledge base can be applied to reduce the time period required for solving problems in the following areas: logical forecasting of situations development; fault detection; acquisition (accumulation and assimilation) of knowledge, etc.

Nutrition and physical activity for the prevention and treatment of age-related sarcopenia.

Sarcopenia, defined as loss of skeletal muscle mass and function, is associated with adverse outcomes such as physical disability, impaired quality of life and increased mortality. Several mechanisms are involved in the development of sarcopenia. Potentially modifiable factors include nutrition and physical activity. Protein metabolism is central to the nutritional issues, along with other potentially modifying nutritional factors as energy balance and vitamin D status. An increasing but still incomplete knowledge base has generated recent recommendations on an increased protein intake in the elderly. Several factors beyond the total amount of protein consumed emerge as potentially important in this context. A recent summit examined three hypotheses: (1) A meal threshold; habitually consuming 25-30 g protein at breakfast, lunch and dinner provides sufficient protein to effectively stimulate muscle protein anabolism; (2) Protein quality; including high-quality protein at each meal improves postprandial muscle protein synthesis; and (3) performing physical activity in close temporal proximity to a high-quality protein meal enhances muscle anabolism. Optimising the potential for muscle protein anabolism by consuming an adequate amount of high-quality protein at each meal, in combination with physical activity, appears as a promising strategy to prevent or delay the onset of sarcopenia. However, results of interventions are inconsistent, and well-designed, standardised studies evaluating exercise or nutrition interventions are needed before guidelines can be developed for the prevention and treatment of age-related sarcopenia.

Studying both sexes: a guiding principle for biomedicine.

In May 2014, the U.S. National Institutes of Health (NIH) announced that it will ensure that investigators account for sex as a biological variable (SABV) in NIH-funded preclinical research as part of the agency's rigor and transparency initiative. Herein, I describe in more detail the rationale behind the SABV policy component and provide additional detail about policy goals. In short, studying both sexes is a guiding principle in biomedical research that will expand knowledge toward turning discovery into health. NIH expects that considering SABV in preclinical research will help to build a knowledge base that better informs the design of clinical research and trials in humans. Integrating the practice of studying both sexes in preclinical research will, over time, expand our currently incomplete knowledge base that plays a critical role in informing the development of sex- and gender-appropriate medical care for women and men.

Five-Year Retrospective

We have just completed 5 years as editors of Journal of Teacher Education (JTE), having published four full volumes (63-66) and part of one (62), and will hand over the privilege and responsibility to a new team from Michigan State University in the next issue. In one of our first editorials (Knight et al., 2012), we reflected on how views of teacher education research from both within and outside the profession influenced our vision for the journal. At that time, we saw our challenge as building on the emerging traditions of diversity and excellence established by previous teams of capable editors with the ultimate goal of further advancing research to establish teacher education as a distinct field with knowledge, histories, research methodologies, and practices that are recognized and recognizable. Furthering the goal would require us to bring together the three dimensions of teacher education--practice, policy, and research--in challenging and productive ways so that considerations of issues or challenges in teacher education would be enriched by careful attention from these multiple frames of reference. We recognized a number of obstacles: the reputation of research in teacher education as lacking rigor and relevance and, relatedly, an incomplete knowledge base that prevents us from connecting findings in meaningful ways to inform practice and policy (e.g., Cochran-Smith & Zeichner, 2005; Feuer, Towne, & Shavelson, 2002; Kaestle, 1993; Moss et al., 2006; National Research Council, 2002; Wilson, Floden, & Ferrini-Mundy, 2001); a lack of a sense of professional identity among teacher educators (Labaree, 2008); and publication of teacher education research in specialized content journals with limited audiences rather than in broader teacher education research journals. In reflecting on our tenure as JTE editors, we see that putting our rhetoric into reality was challenging. The sheer number of manuscripts--more than 700 per year--was overwhelming even for a relatively large editorial team with diverse expertise and interests. We made concerted efforts to address our goal of improving quality; we devoted editorials (e.g., Knight et al., 2012) to the topic of quality and led interactive sessions at the annual meetings of our sponsoring organization, the American Association of Colleges of Teacher Education (AACTE), to discuss what constitutes rigor in teacher education research. To address our goal of improving relevance, we sponsored major forums at AACTE meetings on current topics and solicited recommendations from teacher educators for theme issues focusing on emerging areas of interest. Teacher Education Research Quality Based on our review of manuscripts from the first year of our editorship (Volume 63), we identified four areas that authors could target to improve the quality of their research (Knight et al., 2013). The first area that we identified, appropriateness for JTE, involves an explicit connection to an important topic or issue related to research and scholarship in teacher education. We initially rejected a large number of articles prior to external review for two primary reasons: They focused on teachers, teaching, or K-12 students without a clear connection to teacher education or they used teacher education students or faculty as their sample but did not connect to relevant theory and previous methodological and empirical work in teacher education. The second and third areas involve intertwined issues related to the nature of the research design and the samples used in the studies. We received a large number of manuscripts describing studies where the researchers were also the teacher educators or program developers and implementers and the samples were their own students. Whereas this relationship is not problematic in and of itself, the genre of many of the manuscripts often appeared to be program evaluation with program improvement or validation as the primary purpose. …

On the projection problem in active knowledge bases with incomplete information

The problem of projection has been identified as a fundamental reasoning concern in dynamical domains, where we are to determine whether or not some conditions will hold after a sequence of actions has been performed starting in some initial state. Solving the problem requires, at the very least, effectively reasoning about how actions transform the world, and inferring the logical consequences of the initial knowledge base (KB). For various reasons, tractability one of them, applications often make the closed-world assumption, thereby limiting the scope of these systems for the real world. In this thesis, using the language of the situation calculus, we investigate the computational properties of a number of unsolved reasoning tasks in the context of projection with incomplete information. We first look at inherently incomplete KBs, where the information provided to the agent may not determine every fact about the world. Projection, then, may involve reasoning about what is believed and also, about what is not believed. We then look at physical agents with unreliable hardware, as a result of which actions lead to certain kinds of incomplete knowledge. Intuitively, beliefs should be (periodically) synchronized with this noise. Finally, we consider the presence of other agents in the environment, whose beliefs may differ arbitrarily, and the formalism should incorporate what others sense and learn during actions. To enable a precise mathematical treatment of incomplete KBs, we appeal to a seminal proposal by Levesque, called only knowing. Building on existing work, we investigate projection wrt extensions to the situation calculus for only knowing, noisy hardware and multiple agents. Our central contribution will be to show that, in spite of the additional expressivity, reasoning about knowledge and action reduces to nonepistemic non-dynamic reasoning about the initial KB. More precisely, we show that when the initial KB is an arbitrary first-order theory, we are able to identify conditions under which projection can be solved by progressing the KB to a sentence reflecting the changes due to actions that have already occurred. Moreover, when effectors are unreliable, we allow the system to maintain probabilistic beliefs and then show how projection can be addressed by means of updating these beliefs. Finally, when there are many agents in the picture, we show that queries about the future can be resolved by regressing the query backwards to a formula about the initial KB. Only knowing comes with a significant result that allows us to reduce queries about knowledge to first-order theorem-proving tasks, which is then made use of when solving projection.

A Multi-Modal Incompleteness Ontology model (MMIO) to enhance information fusion for image retrieval

A significant effort by researchers has advanced the ability of computers to understand, index and annotate images. This entails automatic domain specific knowledge-base (KB) construction and metadata extraction from visual information and any associated textual information. However, it is challenging to fuse visual and textual information and build a complete domain-specific KB for image annotation due to several factors such as: the ambiguity of natural language to describe image features; the semantic gap when using image features to represent visual content and the incompleteness of the metadata in the KB. Typically the KB is based upon a domain specific Ontology. However, it is not an easy task to extract the data needed from annotations and images, and then to automatically process these and transform them into an integrated Ontology model, because of the ambiguity of terms and because of image processing algorithm errors. As such, it is difficult to construct a complete KB covering a specific domain of knowledge. This paper presents a Multi-Modal Incompleteness Ontology-based (MMIO) system for image retrieval based upon fusing two derived indices. The first index exploits low-level features extracted from images. A novel technique is proposed to represent the semantics of visual content, by restructuring visual word vectors into an Ontology model by computing the distance between the visual word features and concept features, the so called concept range. The second index relies on a textual description which is processed to extract and recognise the concepts, properties, or instances that are defined in an Ontology. The two indexes are fused into a single indexing model, which is used to enhance the image retrieval efficiency. Nonetheless, this rich index may not be sufficient to find the desired images. Therefore, a Latent Semantic Indexing (LSI) algorithm is exploited to search for similar words to those used in a query. As a result, it is possible to retrieve images with a query using (similar) words that do not appear in the caption. The efficiency of the KB is validated experimentally with respect to three criteria, correctness, multimodality, and robustness. The results show that the multi-modal metadata in the proposed KB could be utilised efficiently. An additional experiment demonstrates that LSI can handle an incomplete KB effectively. Using LSI, the system can still retrieve relevant images when information in the Ontology is missing, leading to an enhanced retrieval performance.

Facilitating implementation of the translational research pipeline in neurological rehabilitation

Knowledge translation is a growing area of specialisation. This review summarises the field perspectives and highlights recent work that has particular relevance to neurological rehabilitation. Research in knowledge translation can usefully be organised into three overlapping perspectives, namely a linear transfer of codified knowledge, a social interaction perspective, or a multilevel implementation perspective that incorporates contextual factors. Although systematic reviews remain foundational in supporting knowledge translation, they often lack structured updating and can be problematic to implement in complex cases. Knowledge brokers play an important role in evidence use; these may be managers or administrators of rehabilitation services. Organisational support that sustains and structures knowledge brokering roles has been found lacking. Numerous contextual factors influence knowledge translation, including leadership, fidelity monitoring, and divergent stakeholder perspectives. Integrative frameworks have been developed that consolidate the multiple contingencies. Knowledge translation is a complex process with an incomplete knowledge base; its uniprofessional focus is particularly limiting for neurological rehabilitation. Developing accessible systematic reviews remains central, as well as supporting knowledge brokers, being aware of stakeholder absorptive capacity in developing translational strategies and using integrative frameworks to guide knowledge translation for complex interventions.