Candidate Concepts Research Articles

A method of core concept extraction based on semantic‐weight ranking

SummaryIn order to improve the efficiency of domain ontology concept extraction, this paper proposes a method of semantic‐weight ranking to extract core concepts. Firstly, we make segmentation processing for corpus texts and obtain the candidate concept set, and then analyze the correlation between the candidate concepts through the method of semantic similarity to form a mesh graph structure. We secondly calculate the semantic‐weight of the concept according to the generated mesh graph. Ultimately, we realize the core concept extraction via ranking different semantic‐weight. At the same time, we give the fuzzy relationship representation of the core concepts through the statistical characteristics of the core concepts' membership. On the foundation of the extraction method, we carried out the experiments with the financial text and had achieved an accuracy rate of 81.8%, which could effectively extract the core concepts and play a positive role in the content construction, knowledge sharing, and knowledge reuse.

Comparison of terminology mapping methods for nursing wound care knowledge representation

BackgroundStandardized clinical terminologies are increasingly used to design and support advanced information systems. In order to examine the representativeness of these terminologies for different professional groups or clinical areas, researchers may perform different methods of terminology mapping. ObjectiveThe purpose of this study was to evaluate the ability of four mapping methods to identify concepts related to wound care in SNOMED CT. MethodsA class diagram of 107 concepts was developed to represent the nursing context of wound assessment, wound diagnosis, and goal of care for wound management. All concepts were mapped to SNOMED CT and identified as a direct match, a one-to-many match, or no match using four mapping methods (manual, automated, comparison, and concordance). The manual, automated and comparison methods produced candidate lists of SNOMED CT concepts, which were then used by two nursing wound care experts. The experts completed concordance mapping, which produced the final list. The SNOMED CT concepts from the manual, automated and comparison mappings were compared to the concordance mapping to generate a proportion of representation by each mapping method. ResultsThe manual, automated and comparison mappings produced partial lists of unique candidate concept matches not found in the other mapping methods. The concordance mapping produced a final list which included: 43 terms (40%) that had direct matches, 2 terms (2%) that had one-to-many matches, and 62 terms (58%) that had no matches to SNOMED CT. All mapping methods were necessary to achieve the representativeness captured in the final list. ConclusionTo increase the representativeness of candidate mapping lists, multiple mapping methods and considerations may be necessary.

The Theoretical Foundations of EU Law

This chapter investigates the contribution of legal theory in understanding the distinctiveness of law’s role in the EU. It does so by evaluating which theoretical approach, or approaches, generate the best conception of law’s place in a wider political vision of supranational community. Key candidate conceptions are positivism, culturalism, idealism and pragmatism. For positivism, the focus is on ‘the authorized law’; for culturalism, ‘the appropriate law’; for idealism, ‘the good law’, and for pragmatism, ‘the law that works’. Whereas EU law lacks the direct democratic resources, the common cultural identity and the plausible claim to universalism to support robust versions of positivism, culturalism, and idealism, its investment in the security and versatility of law in providing positive-sum policy outputs has instead supported a primarily pragmatic vison of law’s legitimacy. The chapter concludes by examining the fragility of excessive dependence on pragmatism in an environment where various crises increasingly undermine even modest levels of secondary reliance on positivist, culturalist and idealist justifications, and by addressing the possibilities inherent in an alternative proceduralist model of EU law.

Commonsense Knowledge Aware Concept Selection For Diverse and Informative Visual Storytelling

Visual storytelling is a task of generating relevant and interesting stories for given image sequences. In this work we aim at increasing the diversity of the generated stories while preserving the informative content from the images. We propose to foster the diversity and informativeness of a generated story by using a concept selection module that suggests a set of concept candidates. Then, we utilize a large scale pre-trained model to convert concepts and images into full stories. To enrich the candidate concepts, a commonsense knowledge graph is created for each image sequence from which the concept candidates are proposed. To obtain appropriate concepts from the graph, we propose two novel modules that consider the correlation among candidate concepts and the image-concept correlation. Extensive automatic and human evaluation results demonstrate that our model can produce reasonable concepts. This enables our model to outperform the previous models by a large margin on the diversity and informativeness of the story, while retaining the relevance of the story to the image sequence.

Who Owns the Concept of Psychiatric Disorder?

Who Owns the Concept of Psychiatric Disorder? Miriam Solomon (bio) About ten years ago, I participated in a consensus process on migraine nomenclature (Young et al., 2012, p. 12). Participants used a modified Delphi technique to explore their views about what migraine is. Candidate concepts included an illness, disease, syndrome, condition, disorder, or susceptibility. Initially, there was a wide range of views about which concept best fits our concept of migraine. Migraine—in common with many psychiatric disorders—is poorly understood by neuroscience. On scientific grounds, participants thought that "susceptibility" and "syndrome" describes our current knowledge well. However, participants were concerned that terms like "susceptibility" and "syndrome" could lead to migraine not being taken seriously enough by both physicians and the public. They also rejected the term "disorder" on the grounds that it evokes the concept of psychiatric disorder, which they judged to be both stigmatizing and misleading. In the end, participants agreed that the concept "disease" is the most appropriate for migraine, because it gives migraine necessary credibility. This is important for generating funds for research, educating the public, and educating health care providers. I was impressed with the fact that pragmatic concerns to have migraine taken seriously and to avoid stigmatization outweighed epistemic interests in the scientific accuracy of the term chosen. A panel of migraine experts, other healthcare professionals, academics, and patients judged that it was more important for migraine to be classified in a way that would lead to its being taken more seriously. They also judged that it was important to classify migraine in a way that avoids stigmatization. Although the term "disease" is sometimes said to be reserved for conditions with known etiology, this scientific constraint is not applied strictly. I remembered this experience when I began thinking about what falls under the concept of psychiatric disorder. It is important to consider both the scientific meaning of psychiatric disorder (insofar as such a meaning can be discerned) and the pragmatic consequences of including (and excluding) a condition in the category of psychiatric disorders. I am grateful to Claire Pouncey and Jerome Wakefield for their thoughtful comments, which I think help to bring into focus what is at stake in our different positions. Both Pouncey and Wakefield are concerned that I am playing Lewis Carroll's Humpty Dumpty with the concept of psychiatric disorder in seeking to broaden it to include the concept of psychological injury. On [End Page 349] their views, the meaning of "psychiatric disorder" is fixed and to be discovered through conceptual investigation of current practice. In addition, according to Pouncey, the term "psychological injury" is already taken by the legal profession, and not available for other uses, such as the use that I suggest. Pouncey and Wakefield have conservative approaches to the language of psychiatric disorder. This is in accord with "ordinary language" philosophical methodology, which aims to elucidate the current meanings of terms rather than to track them over time or to recommend changes in meaning. Pouncey and Wakefield (as well as many others) think that it is a conceptual error to treat grief as a psychiatric disorder. For Pouncey, the core meaning of disorder is "not normal," and for Wakefield, the core meaning of psychological disorder is "harmful dysfunction." They see my suggestion of including "psychological injury" in the category of psychiatric disorder as a conceptual error because they think that it is contrary to the core meaning of the terms. They also think that my suggestion is pragmatically unnecessary, because Pouncey thinks that grief therapy can be covered under the "adjustment disorder" code and Wakefield recommends that grief therapy should be covered by a "Z-code." I take Pouncey and Wakefield's criticisms seriously. If they were right about "core meanings," then my own suggestions about how to let the meaning of "psychiatric disorder" change would be playing Lewis Carroll's Humpty Dumpty or, as Wittgenstein might put it derisively, "language on a holiday." However, I do not agree with their conservatism about concepts. It is ill-suited for giving an account of concepts that change over time, as well as ill-suited for concepts that would be improved by making changes in them. I do think...

Extending import detection algorithms for concept import from two to three biomedical terminologies

BackgroundWhile enrichment of terminologies can be achieved in different ways, filling gaps in the IS-A hierarchy backbone of a terminology appears especially promising. To avoid difficult manual inspection, we started a research program in 2014, investigating terminology densities, where the comparison of terminologies leads to the algorithmic discovery of potentially missing concepts in a target terminology. While candidate concepts have to be approved for import by an expert, the human effort is greatly reduced by algorithmic generation of candidates. In previous studies, a single source terminology was used with one target terminology.MethodsIn this paper, we are extending the algorithmic detection of “candidate concepts for import” from one source terminology to two source terminologies used in tandem. We show that the combination of two source terminologies relative to one target terminology leads to the discovery of candidate concepts for import that could not be found with the same “reliability” when comparing one source terminology alone to the target terminology. We investigate which triples of UMLS terminologies can be gainfully used for the described purpose and how many candidate concepts can be found for each individual triple of terminologies.ResultsThe analysis revealed a specific configuration of concepts, overlapping two source and one target terminology, for which we coined the name “fire ladder” pattern. The three terminologies in this pattern are tied together by a kind of “transitivity.” We provide a quantitative analysis of the discovered fire ladder patterns and we report on the inter-rater agreement concerning the decision of importing candidate concepts from source terminologies into the target terminology. We algorithmically identified 55 instances of the fire ladder pattern and two domain experts agreed on import for 39 instances. In total, 48 concepts were approved by at least one expert. In addition, 105 import candidate concepts from a single source terminology into the target terminology were also detected, as a “beneficial side-effect” of this method, increasing the cardinality of the result.ConclusionWe showed that pairs of biomedical source terminologies can be transitively chained to suggest possible imports of concepts into a target terminology.

Web-based interactive mapping from data dictionaries to ontologies, with an application to cancer registry

BackgroundThe Kentucky Cancer Registry (KCR) is a central cancer registry for the state of Kentucky that receives data about incident cancer cases from all healthcare facilities in the state within 6 months of diagnosis. Similar to all other U.S. and Canadian cancer registries, KCR uses a data dictionary provided by the North American Association of Central Cancer Registries (NAACCR) for standardized data entry. The NAACCR data dictionary is not an ontological system. Mapping between the NAACCR data dictionary and the National Cancer Institute (NCI) Thesaurus (NCIt) will facilitate the enrichment, dissemination and utilization of cancer registry data. We introduce a web-based system, called Interactive Mapping Interface (IMI), for creating mappings from data dictionaries to ontologies, in particular from NAACCR to NCIt.MethodIMI has been designed as a general approach with three components: (1) ontology library; (2) mapping interface; and (3) recommendation engine. The ontology library provides a list of ontologies as targets for building mappings. The mapping interface consists of six modules: project management, mapping dashboard, access control, logs and comments, hierarchical visualization, and result review and export. The built-in recommendation engine automatically identifies a list of candidate concepts to facilitate the mapping process.ResultsWe report the architecture design and interface features of IMI. To validate our approach, we implemented an IMI prototype and pilot-tested features using the IMI interface to map a sample set of NAACCR data elements to NCIt concepts. 47 out of 301 NAACCR data elements have been mapped to NCIt concepts. Five branches of hierarchical tree have been identified from these mapped concepts for visual inspection.ConclusionsIMI provides an interactive, web-based interface for building mappings from data dictionaries to ontologies. Although our pilot-testing scope is limited, our results demonstrate feasibility using IMI for semantic enrichment of cancer registry data by mapping NAACCR data elements to NCIt concepts.

Detecting missing IS-A relations in the NCI Thesaurus using an enhanced hybrid approach

BackgroundThe National Cancer Institute (NCI) Thesaurus provides reference terminology for NCI and other systems. Previously, we proposed a hybrid prototype utilizing lexical features and role definitions of concepts in non-lattice subgraphs to identify missing IS-A relations in the NCI Thesaurus. However, no domain expert evaluation was provided in our previous work. In this paper, we further enhance the hybrid approach by leveraging a novel lexical feature—roots of noun chunks within concept names. Formal evaluation of our enhanced approach is also performed.MethodWe first compute all the non-lattice subgraphs in the NCI Thesaurus. We model each concept using its role definitions, words and roots of noun chunks within its concept name and its ancestor’s names. Then we perform subsumption testing for candidate concept pairs in the non-lattice subgraphs to automatically detect potentially missing IS-A relations. Domain experts evaluated the validity of these relations.ResultsWe applied our approach to 19.08d version of the NCI Thesaurus. A total of 55 potentially missing IS-A relations were identified by our approach and reviewed by domain experts. 29 out of 55 were confirmed as valid by domain experts and have been incorporated in the newer versions of the NCI Thesaurus. 7 out of 55 further revealed incorrect existing IS-A relations in the NCI Thesaurus.ConclusionsThe results showed that our hybrid approach by leveraging lexical features and role definitions is effective in identifying potentially missing IS-A relations in the NCI Thesaurus.

Few-Shot Induction of Generalized Logical Concepts via Human Guidance.

We consider the problem of learning generalized first-order representations of concepts from a small number of examples. We augment an inductive logic programming learner with 2 novel contributions. First, we define a distance measure between candidate concept representations that improves the efficiency of search for target concept and generalization. Second, we leverage richer human inputs in the form of advice to improve the sample efficiency of learning. We prove that the proposed distance measure is semantically valid and use that to derive a PAC bound. Our experiments on diverse learning tasks demonstrate both the effectiveness and efficiency of our approach.

Stability liabilities of biotherapeutic proteins: Early assessment as mitigation strategy

Identification of molecular liabilities and implementation of mitigation strategies are key aspects that need to be considered by pharmaceutical companies developing therapeutic proteins.In the field of monoclonal antibodies, an efficient and streamlined process known as developability assessment is used for the selection of the “fittest” candidate. Other protein modalities, have in most cases only a limited number of possible candidates, requiring a paradigm change to a concept of candidate enabling. The assessment of liabilities at early project phases with the possibility to re-engineer candidates becomes essential for the success of these projects.Each protein possesses a unique stability profile resulting from the interplay of conformational, colloidal, chemical and physical stability attributes. All of these attributes strongly depend on external factors. Conformational and colloidal stability profiles of three non-immunoglobulin domain based proteins, namely Carbonic anhydrase, Ovalbumin and Thyroglobulin, and of two monoclonal antibodies were assessed in dependence of solution pH, ionic strength and varying buffering agents. The impact of screened external factors on proteins’ stability attributes varied significantly, indicating presence of molecule specific liabilities. Screening of such a broad space of conditions at early project phases is only feasible using low-material consuming, high-throughput analytical methods as exemplified in this study.

Development of the impact of weight on daily activities questionnaire: A patient-reported outcome measure.

SummaryWhile patient‐reported outcome measures are available to evaluate health‐related quality of life and functioning in obesity, existing measures do not evaluate the impact of excess weight and weight loss on the ability to perform regularly occurring daily activities. Three iterative sets of qualitative interviews were conducted in two countries (United States, n = 23; United Kingdom, n = 23) with individuals with body mass index ≥30 kg/m2 to inform development of the Impact of Weight on Daily Activities Questionnaire (IWDAQ) for use in clinical trials to evaluate daily activity limitations associated with excess weight. Candidate concepts were selected based on the literature, expert opinion, and previously conducted qualitative research, after which the draft IWDAQ was developed and tested. Interviews included a brief concept elicitation phase, followed by cognitive debriefing during which the IWDAQ was refined based upon participants' feedback. The IWDAQ uses a novel, adaptive questionnaire design, such that clinical trial participants choose the three IWDAQ activities they would most like to improve with weight loss and rate the degree of limitation in each of these activities at baseline. By allowing individuals participating in trials to identify and monitor changes in the activities they most want to see improve with weight loss, the 19‐item IWDAQ has the potential to detect the benefits of weight‐loss treatment that individuals with obesity value most.

Do We Know Who the Person With the Borderline Score is, in Standard-Setting and Decision-Making

PurposeWhen assessing clinical competence, health professional educators use assessments of knowledge attainment, skills acquisition, and professional development, which impact on decision-making for student’s training progression. Given the impact of progression-failure, it is critical that the expected standard of performance is derived accurately, fairly, and transparently, and that the rating of student performance is performed within the highest standards achievable. There is ongoing disagreement as to the most appropriate methods to address both standard setting and decision-making. The borderline candidate has been debated extensively in the academic and educational setting, with ongoing disagreement surrounding the concept. MethodsIn this paper, we discuss further perspectives on the use of the borderline candidate, as part of the process for standard-setting, to give insights into how we can reframe the concept more accurately and apply it more appropriately. DiscussionDrawing parallels to Kane’s validity framework, we consider the concept of the borderline candidate from four different perspectives: ‘what is’-what are the linguistics and implications behind the phrase ‘borderline candidate’; ‘who is’-who is the borderline candidate; decided ‘by whom’-who is the person making the judgement; and ‘under what circumstances’-the context of the assessment. ConclusionFinally, we translate the theoretical discussion into pragmatic and practical solutions in standard-setting practice

Unified Medical Language System resources improve sieve-based generation and Bidirectional Encoder Representations from Transformers (BERT)-based ranking for concept normalization.

ObjectiveConcept normalization, the task of linking phrases in text to concepts in an ontology, is useful for many downstream tasks including relation extraction, information retrieval, etc. We present a generate-and-rank concept normalization system based on our participation in the 2019 National NLP Clinical Challenges Shared Task Track 3 Concept Normalization.Materials and MethodsThe shared task provided 13 609 concept mentions drawn from 100 discharge summaries. We first design a sieve-based system that uses Lucene indices over the training data, Unified Medical Language System (UMLS) preferred terms, and UMLS synonyms to generate a list of possible concepts for each mention. We then design a listwise classifier based on the BERT (Bidirectional Encoder Representations from Transformers) neural network to rank the candidate concepts, integrating UMLS semantic types through a regularizer.ResultsOur generate-and-rank system was third of 33 in the competition, outperforming the candidate generator alone (81.66% vs 79.44%) and the previous state of the art (76.35%). During postevaluation, the model’s accuracy was increased to 83.56% via improvements to how training data are generated from UMLS and incorporation of our UMLS semantic type regularizer.DiscussionAnalysis of the model shows that prioritizing UMLS preferred terms yields better performance, that the UMLS semantic type regularizer results in qualitatively better concept predictions, and that the model performs well even on concepts not seen during training.ConclusionsOur generate-and-rank framework for UMLS concept normalization integrates key UMLS features like preferred terms and semantic types with a neural network–based ranking model to accurately link phrases in text to UMLS concepts.

An Empirical Method of Automatic Pattern Extraction for Clinical Text Classification.

Clinical text classification is an indispensable and extensively studied problem in medical text processing. Existing research primarily employs machine learning and pattern based approaches to address the stated problem. In general, pattern based approaches perform better than other methods. However, these approaches commonly require human intervention for pattern identification, which diminish their benefits and restrain their applications. In this study, we present a novel pattern extraction algorithm, which identifies and extracts patterns from clinical textual resources, automatically. The algorithm identifies the candidate concepts in the clinical text, finds the context of the concepts by discovering their context windows, and finally transforms each context window to a pattern. We evaluate our proposed algorithm on Hypertension, Rhinosinusitis, and Asthma guidelines. 70% of the hypertension guideline was used for pattern extraction while the remaining 30% and the other two guidelines were used for evaluations. The algorithm extracts 21 patterns that classify Hypertension, Rhinosinusitis, and Asthma guidelines sentences to the recommendation and non-recommendation sentences with 84.53%, 80.03%, and 84.62% accuracy, respectively. The initial results reveal the benefits and applicability of the algorithm for clinical text classification.

Understanding a bag of words by conceptual labeling with prior weights

In many natural language processing tasks, e.g., text classification or information extraction, the weighted bag-of-words model is widely used to represent the semantics of text, where the importance of each word is quantified by its weight. However, it is still difficult for machines to understand a weighted bag of words (WBoW) without explicit explanations, which seriously limits its application in downstream tasks. To make a machine better understand a WBoW, we introduce the task of conceptual labeling, which aims at generating the minimum number of concepts as labels to explicitly represent and explain the semantics of a WBoW. Specifically, we first propose three principles for label generation and then model each principle as an objective function. To satisfy the three principles simultaneously, a multi-objective optimization problem is solved. In our framework, a taxonomy (i.e., Microsoft Concept Graph) is used to provide high-quality candidate concepts, and a corresponding search algorithm is proposed to derive the optimal solution (i.e., a small set of proper concepts as labels). Furthermore, two pruning strategies are also proposed to reduce the search space and improve the performance. Our experiments and results prove that the proposed method is capable of generating proper labels for WBoWs. Besides, we also apply the generated labels to the task of text classification and observe an increase in performance, which further justifies the effectiveness of our conceptual labeling framework.

A study of entity-linking methods for normalizing Chinese diagnosis and procedure terms to ICD codes.

This study aims to develop and evaluate effective methods that can normalize diagnosis and procedure terms written by physicians to standard concepts in International Classification of Diseases(ICD) in Chinese, with the goal to facilitate automated medical coding in China. We applied the entity-linking framework to normalize Chinese diagnosis and procedure terms, which consists of two steps - candidate concept generation and candidate concept ranking. For candidate concept generation, we implemented both the traditional BM25 algorithm and an extended version that integrates a synonym knowledgebase. For candidate concept ranking, we investigated a number of different algorithms: (1) the BM25 algorithm, (2) ranking support vector machines (RankSVM), (3) a previously reported Convolutional Neural Network (CNN) approach, (4) 11 deep ranking-based methods from the MatchZoo toolkit, and (5) a new BERT (Bidirectional Encoder Representations from Transformers) based ranking method. Using two manually annotated datasets (8,547 diagnoses and 8,282 procedures) collected from a Tier 3A hospital in China, we evaluated above methods and reported their performance (i.e., accuracy) at different cutoffs. The coverage of candidate concept generation was greatly improved after integrating the synonym knowledgebase, achieving 97.9% for diagnoses and 93.4% for procedures respectively. Overall the new BERT-based ranking method achieved the best performance on both diagnosis and procedure normalization, with the best accuracy of 92.1% for diagnosis and 80.1% for procedure, when the top one concept and exact match criteria were used. This study developed and compared diverse entity-linking methods to normalize clinical terms in Chinese and our evaluation shows good performance on mapping disease terms to ICD codes, demonstrating the feasibility of automated encoding of clinical terms in Chinese.

Least General Generalizations in Description Logic: Verification and Existence

We study two forms of least general generalizations in description logic, the least common subsumer (LCS) and most specific concept (MSC). While the LCS generalizes from examples that take the form of concepts, the MSC generalizes from individuals in data. Our focus is on the complexity of existence and verification, the latter meaning to decide whether a candidate concept is the LCS or MSC. We consider cases with and without a background TBox and a target signature. Our results range from coNP-complete for LCS and MSC verification in the description logic εℒ without TBoxes to undecidability of LCS and MSC verification and existence in εℒI with TBoxes. To obtain results in the presence of a TBox, we establish a close link between the problems studied in this paper and concept learning from positive and negative examples. We also give a way to regain decidability in εℒI with TBoxes and study single example MSC as a special case.

Multiple-instance learning via multiple-point concept based instance selection

Multiple-instance learning (MIL) is a kind of weakly supervised learning where a single label is assigned to a bag of instances. To solve MIL problems, researchers have presented an effective embedding based framework that projects bags into a new feature space, which is constructed from some selected instances that can represent target concepts to some extent. Most previous studies use single-point concepts for the instance selection, where every possible concept is represented by only a single point (i.e., instance). However, multiple points may be more powerful for the same concept than a single. In this paper, we propose the notion of multiple-point concept, jointly represented by a group of similar points, and then build an iterative instance-selection method for MIL upon Multiple-Point Concepts. The proposed algorithm is thus named MILMPC, and its main difference from other MIL algorithms is selecting instances via multiple-point concept rather than single-point concept. The experimental results on five data sets have validated the convergence of the iterative instance-selection method, and the generality of the resulting MIL model in that it performs consistently well under three different kinds of relevance evaluation criteria (used to measure the relevance of a candidate concept to the target). Furthermore, compared to other MIL algorithms, the proposed model has been demonstrated not only suitable for common MIL problems, but more suitable for hybrid problems.

Class expression induction as concept space exploration: From DL-Foil to DL-Focl

The Web of Data is one of the perspectives of the Semantic Web. In this context, concept learning services, supported by multirelational machine learning, have been integrated in various tools for knowledge engineers to carry out several tasks related to the construction, completion and maintenance of the knowledge bases: essentially they are used to elicit new candidate concept definitions (i.e. axioms regarding classes) to be incorporated in the knowledge bases possibly also as replacements for previous ones. Sundry reference approaches rely on a covering strategy to generalize input examples that can be regarded as a form of hill-climbing search that explores a huge discrete conceptual space. Methods adopting this strategy are known to be affected by an inherent problem of myopia. In particular, our DL-Foil has been shown to suffer from this problem as its algorithm is based on a stochastic yet informed exploration of the concept space, by means of a refinement operator, to generate partial descriptions iteratively. To tackle this problem and enhance the performance of our system we have introduced a series of extensions of the original DL-Foil algorithm, that have led to various releases of its spin-off DL-Focl. Essentially they aim at reducing the aforementioned problem through specific strategies grounded on either the integration of meta-heuristics, such as repeated hill-climbing and tabu search, or the employment of some form of lookahead. In this work, we present consolidated and extended releases of both DL-Foil and DL-Focl along various dimensions:better heuristics and stop conditions, more complex refinement operators with the possibility to perform the specialization adopting iterative deepening or lookahead strategies, improved versions of the algorithm based on the repeated hill-climbing strategy with new quality criteria and of the tabu search with a different policy for managing the local memory. All the implementations of these approaches have been extensively evaluated in three experimental sessions, involving various publicly available knowledge bases and fragments extracted from the Linked Data cloud, showing interesting results and indicating some lessons to be learned: our approaches outperformed a popular reference system from the DL-Learner framework on learning problems when the open-world semantics is explicitly considered. They also exhibited an analogous performance on a benchmark of datasets from contexts with an intended underlying closed-world semantics.

Towards Safer and More Sustainable Ways for Exploiting Nuclear Power

Future electricity systems are challenged by deep decarbonization and concurrently increasing demand and there are growing concerns that renewables cannot shoulder this alone. Starting from the proven principle of diversity, we argue for keeping the nuclear option open or even for expanding its use. However, the perspectives are dim for the current technology as safety concerns and social aversion remain as fundamental problems. While looking for future revolutionary safe and more sustainable nuclear concepts we first review the main characteristics of civil nuclear energy, as well as its safety records and technical progress. We then list the key requirements for innovative nuclear systems designs which are less dependent on active safety systems and human performance as well as social stability. This allows us to provide a concept by concept comparison and assessment of existing and novel technologies and designs including different coolants and neutron spectra. The results indicate a high potential for far-reaching improvements compared to most advanced LWRs, although none of the candidate concepts meets all requirements convincingly, yet, helium cooled, small modular reactors (HTR-PM) come closest. We end by stressing the need for future research and development, and keeping human capital and know-how in nuclear energy; we call for an urgent increase in government and international RD&D funding by the order of a few hundreds of billions of USD per year, which will likely lead to breakthroughs that will restart productivity growth in severely affected stagnating modern economies.