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Official websites use. Share sensitive information only on official, secure websites. This article was submitted to Language and Computation, a section of the journal Frontiers in Artificial Intelligence. The use, distribution or reproduction in other forums is permitted, provided the original author s and the copyright owner s are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. The starting point of this paper is the observation that methods based on the direct match of keywords are inadequate because they do not consider the cognitive ability of concept formation and abstraction. We argue that keyword evaluation needs to be based on a semantic model of language capturing the semantic relatedness of words to satisfy the claim of the human-like ability of concept formation and abstraction and achieve better evaluation results. Evaluation of keywords is difficult since semantic informedness is required for this purpose. This model must be capable of identifying semantic relationships such as synonymy, hypernymy, hyponymy, and location-based abstraction. For example, when gathering texts from online sources, one usually finds a few keywords with each text. Still, these keyword sets are neither complete for the text nor are they in themselves closed, i. As a solution, we propose a word graph that captures all these semantic relationships for a given language. Thus the space of keyword sets requires a metric that is non-symmetric, in other words, a quasi-metric. We sketch such a metric that works on our graph. Since it is nearly impossible to obtain such a complete word graph for a language, we propose for the keyword task a simpler graph based on the base text upon which the keyword sets should be evaluated. This reduction is usually sufficient for evaluating keyword sets. Keywords: keyword evaluation, direct matching, concept formation, word graph, non-symmetric metric. The motivation for the present work is the fact that common keyword evaluation methods, as we will point out below, require an exact match of automatically produced keywords with keywords from a reference or gold standard set. We will argue that this is insufficient modeling of keyword evaluation and propose an evaluation method based on a graph representing the words of a language. The starting point of a discussion of the evaluation of keywords should clarify the concept, thus what are keywords? Bharti et al. Due to the descriptive nature of keywords, they are either nouns or noun phrases, i. In this paper we maintain this classification. Keywords can be thus regarded as classification features of texts that can be used, among other by search engines. The point of departure is that keyword evaluation raises the problem of comprehension of natural language, which requires a Common Ground CG of sender and receiver of a message Karttunen, ; Stalnaker, A conditio sine qua non for successful communication by natural language is an intersection of the shared knowledge in CG: sender and receiver of messages have to dispose over similar mental lexicons, i. We claim that lexical knowledge in the mental lexicon can be represented by a graph model, where the nodes represent words and the edges represent semantic relations between words. Our approach follows ideas within cognitive psychology, theory of learning, pedagogy and linguistics. Purely conceptual discussion comes from Aebli , and there is massive empirical evidence for modeling the mental lexicon as a graph. The representation of concepts as cognitive units connected within a graph or network we continue to use the term graph in the following in a mental lexicon Aitchison, goes back to Collins and Quillian for a modular model, see Fodor, , an assumption, that was empirically underpinned by numerous studies, first by Collins and Loftus , who observed a correlation between the distance of words in a semantic network and the times needed to process those words. This observation was confirmed in more recent studies, amongst others by Dorogovtsev and Mendes , Sigman and Cecchi , and De Deyne et al. Furthermore, the graph model proved to be a powerful model of language acquisition Storkel, ; Carlson et al. An evaluation of keywords is then based on distances between the nodes representing the lexical units of a language. In the word graph, there should be only a short distance between Angela Merkel and politician indicating that they are semantically similar. Consequently, politician would not be ruled out a priori because both chains of letters do not match , rather politician would be considered a possible keyword. Furthermore, word pairs like actress and actor which both have the same meaning up to gender, are interchangeable as keywords since they describe the same concept. These graphs are manually generated and make no claim to completeness or generalization. Why is the evaluation of keywords difficult? First, keyword evaluation requires knowledge about the meaning of linguistic units like words, and we postulate that it needs to be based on a semantic model of words capturing how strong they are semantically related. This model must be capable of identifying semantic relationships such as synonymy, hypernymy, hyponymy, and a location-based abstraction. It is not a bad choice if, for example, instead of the reference keyword meeting , the meaning-similar word encounter is generated as a synonym, or if political system is generated as a superordinate term, i. These semantic relations concern the relation of inclusion in set theory, and philosophic theories about entities and their relations are merologies Link, A mereology has a higher level of abstraction than set theory it abstracts for example from the reduction on rewal numbers; Link, and is concerned with meronyms and with its opposite concept, the holonym: a meronym is a part of something, for instance is a steering wheel a part of a car, while, vice versa , car is a holonym of steering wheel. The space of keyword sets thus requires a metric that is not symmetric, rendering it a quasi-metric space. Second, an evaluation must be able to cope with complex expressions and multiword units, such as Angela Merkel, Angela Dorothea Merkel, Frau Dr. As can easily be seen, the meaning of a multiword expression of that type cannot necessarily be computed following the Fregean principle of compositionality. Rather they touch, quite like synonymy, hypernymy, and hyponymy Leibniz's principle of substitutio salva veritate : a substitution of a term by another term is possible without changing the truth conditions of the embedding proposition if both terms denote the same entity in the world. This principle is essential in generative summarizations, which make use of generated keywords that do not occur in the source text see the Angela Merkel - politician —example from above. The semantics of proper names in modern philosophy goes back to Leibniz and his principle of substitutio salva veritate mentioned above. Frege and later Kripke provided counter evidence for this principle, for example in intensional contexts. That is to say, it is not an ontological necessity that a proper name denotes a specific individual, and there is no meaning by definition, or a priori. It was Cluster Theory Strawson, ; Searle, that was introduced as a remedy: the meaning of a proper name is composed from a cluster of attributes of an individual about which there is conventionalized, i. Cluster Theory that has been criticized by Kripke as possibly none of the attributed characteristics apply to the actual historical individual. Which set of features and which referent are attributed to a proper name is thus essentially dependent on linguistic circumstances, on the conversational context and on individual knowledge of the world. In this paper, we assume that proper names can be keywords. That is to say, for example, that Prince Charles , regardless of whom it refers to, can be a keyword of a text, and will in such a case be treated as if it were a single word. As already briefly stated above, the requirement of an exact match of automatically produced keywords and a reference set neglects the human ability of abstraction and classification see for instance Aebli, , that is to say, concept formation. Consequently, Bruner et al. Thus, to know the name of a concept means to know the hypernym of members of a category, and concepts comprise sets of entities in one category that can be considered linguistically as synonyms, i. In summary, concept formation can be considered an essential cognitive performance, and we postulate that state-of-the-art methods and techniques of keyword evaluation should be able to approach these skills Sidman, However, in previous and recent state-of-the-art studies on keyword evaluation Hulth, , ; Marujo et al. Another common, not uncontroversial, method that avoids direct matching is the evaluation of keywords by human raters, see for instance Turney, : there are objections in Hulth who refers to a report van Dijk, on considerable diversities within human ratings. This evaluation method, however, would require an expensive second line of research which would go beyond the scope of this paper. In the following, we use examples from the German language because it is morphologically more challenging than English, to which TextRank Mihalcea and Tarau, , i. This means that compared to, e. For example, in contemporary German, nouns denoting persons almost universally have both a feminine and a masculine form. The morphological richness and word-formation productivity of the German language is intended to underline the problem described below that it is a hard task to form a complete graph of the words of a language. The structure of the paper is as follows: in Section 2, we sketch previous work on keyword evaluation from different theoretical viewpoints, in 3, the theoretical foundations of keyword sets are given, and in Section 4, we illustrate the structure of the graph. Section 5 defines a quasi-metric for the comparison of a Gold standard keyword set and a set to be evaluated and illustrates the application of this metric by two examples. As discussed in the introduction, the evaluation method widely used for keyword extraction is Precision, which is the ratio of relevant instances among the retrieved instances see Equation 1 , Recall, the ratio of relevant instances that were retrieved see Equation 2 , and F1, the weighted average of the two see Equation 3. All three measures are based on direct matching, i. There are some unique evaluation measures inspired by them or combined with them. Saga et al. The Topic Coverage is defined in Equation 4 , where E denotes the number of elements of set E , and T is the set of topics in the document sets, which are extracted employing clustering methods such as k-means, etc. Further E i denotes the set of the top j keywords in topic i , and M i is the set of keywords in topic i extracted by a certain method to be evaluated. Since this measurement is similar to Recall, the performance of Topic Coverage is examined by the comparison with Recall and is confirmed with their high correlation. In the end, this study concludes that Topic Coverage may be used instead of Recall. Unlike Topic Coverage, our method requires a gold standard keyword set for each text. However, this gives the benefit of being able to judge the quality of a keyword set with a stronger focus on the actual text it was assigned to, instead of having to rely on a topic based average. Zesch and Gurevych use the R-precision R-p measure for evaluation of keyphrases. They define R-p as the Precision when the number of retrieved keyphrase matchings equals the number of gold standard keyphrases assigned to the document. That is, only extracted keyphrases that are regarded to match the gold standard keyphrases are counted. As for the matching strategy, instead of exact matching, they propose a new approximate matching that accounts for morphological variants MORPH and the two cases of overlapping phrases: either the extracted key phrase includes the gold standard keyphrase INCLUDES or the extracted key phrase is a part of the gold standard keyphrase PARTOF. For overlapping phrases, they do not allow character level variations, but only token level variations and morphological variations MORPH are limited only to detecting plurals. The main difference to our approach is the fact that this method does not take more abstract semantic relationships into account. Liu et al. In the Pyramid metric, a score is assigned to each keyword candidate based on how many human annotators selected it. Keywords with a high score are placed at a high level of the pyramid, and the score of hypothesized keywords is computed by adding the scores of keywords that exist in the pyramid. However, since unmatched keywords cannot be measured by these two metrics, they resort to a human evaluation. In this human evaluation, evaluators are asked to exclude non-keywords from the sets of human and machine-generated candidates. Unlike traditional evaluations based on string matching, the PMI estimates semantic similarity. Thanks to relative scores generated by the PMI, it can be used to compare various keyphrase extraction algorithms. Graph theory, which has been contributing to various fields of natural language processing, is also indispensable when it comes to evaluation measures. Since the method of the present paper is based on semantic distances in word graphs, it makes sense to consider techniques for automatic construction of semantic classes and identification of semantic distance. For automatic construction of semantic classes, the following method is presented by Widdows and Dorow : The method starts by constructing a large graph consisting of all nouns in a large corpus. Each node represents a noun, and two nodes get connected if they co-occur, separated by the conjunctions and and or. Rare words are filtered out by a cut-off value, that is, the top n neighbors of each word, which could be determined by the user. A candidate node is not added just because of the connection with one single node of the seed set, but rather it is added only when it has a link to some other neighboring node in the seed set. In doing so, the inclusion of an out-of-category word, which happens to co-occur with one of the category words, is avoided. This process is repeated until no new elements can be added to the seed set. In addition to the automatic construction of semantic classes, the semantic distance between words can be measured given existing semantic networks such as WordNet Miller, ; Oram, , in which nouns are organized as nodes into hierarchical structures. Wu and Palmer 's similarity metric measures what they call conceptual similarity between two nodes c 1 and c 2 in a hierarchy see Equation 5 , where depth c i is the length of the path to c i from the global root , that is, the top node of the taxonomy. Further lso c i , c j denotes the lowest super-ordinate, namely the closest common parent node between c i and c j. Resnik , using the lso c i , c j in combination with information theory, proposes a similarity measure. Let p c be the probability of encountering an instance of a concept c in the taxonomy such as WordNet. The key idea of this measure is the extent to which two concepts share information in common. If the position of the lowest super-ordinate between c 1 and c 2 is lower, that is, if the closest common parent node of c 1 and c 2 is a less abstract concept, the possibility of encountering an instance of the lowest super-ordinate is lower. That implies a higher IC, which indicates that the two concepts are similar. While it is possible to build our method on top of any of these similarity measures, the constructions we propose are asymmetric. That is because the comparison of a keyword set with a gold standard set is an asymmetric process: if the adequacy of one keyword set implies the adequacy of another, it does not necessarily follow that the same is true the other way around. Hence, we prefer the usage of quasi-metrics rather than metrics to measure semantic similarity. Nowadays a state-of-the-art method for keyword extraction is the graph-based model, TextRank Mihalcea and Tarau, In TextRank, text units such as words and sentences are represented as vertices in a graph, and the graph is constructed based on their co-occurrences. In the graph, edges connecting the vertices are defined according to the relation between the text units, e. As a graph-based ranking algorithm Mihalcea and Tarau, modify Google's PageRank developed by Brin and Page and offer a new formula for graph-based ranking see Equation 7 , where In V i denotes the set of vertices pointing to the vertex V i , while Out V i denotes the set of vertices that the vertex V i points to. Further d is a damping factor that integrates into the model the probability of jumping from a given vertex to another random vertex in the graph. The damping factor d is usually set to 0. Next w ij is defined as a weight of the edge between two vertices V i and V j. In this regard, it is worth noting that the graph-based ranking in the original PageRank definition is not weighted. In the end, this TextRank algorithm computes scores of the text units by the iteration until convergence and based on the final scores; the relevant text units are extracted. Since some lexical ontologies are relevant to our study, brief remarks about them must be made. WordNet is the most popular ontology, and nouns, verbs, adjectives, and adverbs are connected with each other based on their semantic relations. The main relation among words in WordNet is synonymy. In addition, the super-subordinate relation such as hypernymy and hyponymy is also integrated. GermaNet Hamp and Feldweg, ; Henrich and Hinrichs, is designed for the German language and shares such common structural features with WordNet. The most distinctive feature of this ontology is that concepts are semantically related to each other across various languages. FrameNet is also one of the lexical ontologies, but it is not constructed based on words per se , but on semantic frames Baker and Fellbaum, For a text T we assume that there exists a complete keyword set K T that contains all possible keywords for T. This is mostly due to the fact that keywords most often are used in information retrieval systems. Many texts that can be found, e. For a visualization of all above mentioned keyword sets see Figure 1. When collecting texts one usually finds the keyword set K T , observed , also known as the ground truth. Depending on the praxis of the source of the text, K T , observed can look very different. For example one online news publication has the mandate to always give four keywords with a text, all of them a topic. Another publication with the mandate to give between three and ten keywords with about half of them occurring in the text. None of them is close to be K T. Let us further assume that A returns always all keywords, i. This algorithm will still yield bad Precision, Recall, and F1 values when evaluated against K T , observed. In contrast, when A is a perfect named entity recognizer, it will return a superset of K T , names , as not all names need to be keywords, which can be very close to K T , observed and thus return very good Precision, Recall and F1 values depending. This is the basis for our assumption of why current keyword evaluation methods fail. Approaches based on the direct match between two keyword sets, where one is considered the ground truth, completely rely on the quality of this ground truth set and are unable to account for any abstraction or small differences in the writing, e. The third path would ensure that the ground truth always is K T and not some subset K T , observed , which could be done for some small datasets for a competition or so, but is not feasible for large text corpora. Based on this, we propose a solution along the second lines of the first path, while the approach can easily extended using the word graph to follow the second path. In the following we consider two examples. First a short toy text that we created to show that through a synonym very unrelated fields are connected. The second text is from an online news site, which is also considerably longer. They have talked about weed, among other things. Obama announced the legalization during dinner. As can be seen, some of the keywords, the names especially, would rarely appear in a news text but often refer to the same thing, i. Secondly, consider the news article 2 Atomkraft: Iranisches AKW Buschehr wieder am Netz 1 Nuclear power: Iranian nuclear power plant Bushehr back on the net again from Heise online a German tech news site , about the Iranian nuclear power plant in Buschehr and its return to power production. Our graph is completely manually constructed, that is to say, it is a sectional representation of our mental lexicons, and we created the connections between the nodes according to our intuition. V contains a node, for every noun and every proper name, as we want to use the graph for the evaluation of keyword extraction methods, i. For organizations the node has additionally the abbreviation attached, i. This might also be useful for some nouns, e. Furthermore, since keywords are usually the base form, i. The usage of the lemma becomes more important the more tokens a language has. For example, in German, the word Haus house has the additional forms in the genitive and dative case, respectively, i. Some Slavic languages still have the grammatical number dual, for example in Upper Sorbian the word dom house has the additional form doma, domej, domom , and domje in singular, in dual domaj, domow , and domomaj and in plural domy, domam, domami , and domach. The usage of the lemma reduces the nodes in the graph significantly, and the usage of some grammatical form has no use as a keyword. In these cases, it is not a problem to have distinct nodes, but in general the reduction of the number of nodes is more desirable. For a language such as German with a lot of word forms, this has a huge impact on keeping the graph small. The graph G needs to be connected, i. The edges E represent different types of relations of the words. There are edges E representing synonyms, hypernyms, hyponyms, meronyms, holonyms, location-based abstraction and co-occurrences could either be sentence co-occurrences or neighborhood. Since many of the relation types are directed, the graph G is usually directed. But, if for example only sentence co-occurrences were used to create the edges E , the graph would be undirected. In case of a directed graph, every node in V needs to have at least an incoming and an outgoing edges, so that in all cases a distance can properly be calculated. For a word that is a homonym, the corresponding node in V has a lot of different edges in E representing the different groups of meaning. When considering polysemy, i. Both approaches have their advantages and disadvantages. The first approach requires no knowledge about all the different meanings a word can have. It will implicitly appear in the connection a node has. Whereas for the second approach this knowledge is required when creating the graph and, therefore, is creating the graph more complex. What this means for the metric see the next Section 5. Through the location-based abstraction edges, the graph contains information that, for example, the White House is in Washington, D. We assume that this relation is directed. We also thought about translating the different types of edges as different weights in the graph. This has the advantage that when traversing the graph, some words are closer to another, and would lead to a much more fine-grained distance between the nodes. This would however also mean that when creating the graph, one must decide what weights all these types should have, i. How does this relate to a synonym relation? Since we came to no clear decision here, we decided to use an unweighted graph G. Our proposed metric calculates the distance between nodes and thus does not require weighting to be able to calculate the distance. But it might be a good extension to get more fine-grained distances. It would be hard to construct such a graph, but it can be approximated. In Figures 2 , 3 , we show two sections of an approximated graph for our two example texts. We divided the graph into two figures in order to increase clarity and readability. We styled the words in Figure 3 in an italic font that occur in the Heise text. Approximated word graph for our example text. Translations are listed in Table 1. Approximated word graph for our second example text from Heise. All italic words occur in the text. Translations are listed in Table 2. In the context of our example text, this is sufficient, but it is not true in general, and different connection types need to be used here. But the impact on the metric and subsequently the keyword sets is irrelevant. In both graphs, we have included some nodes with multiple connections between each other. One of those double connections is always a co-occurrence relation. This distinction may be irrelevant when both connections are bidirectional but are otherwise quite relevant. The creation of such a graph is not trivial. While it is possible to create a graph by hand, it becomes quite inefficient the larger the graph becomes. The two sections in this paper were created by hand, and took quite some time and discussion with the relation to some of the relations. For a larger graph it is therefore desirable to automate this process as much as possible. The easiest method is to just create it from co-occurrences. Here one could use left- and right neighborhood co-occurrences to get directions and use the number of occurrences of a co-occurrence inversely proportional as a weight. While it is a strictly hierarchical graph, it is nevertheless a handcrafted graph of word relations. Some resource as that can be used with some modifications as a basis for a graph. For a text T , we now want to find a way to compare the set of given keywords K T , observed with the set K T, A , which is a set of keywords given by some algorithm A. Intuitively, it is supposed to measure how much sense it makes to substitute a given non-empty set of keywords K 1 by the non-empty set K 2. The subscript sd stands for the semantic distance function sd w 1 , w 2 between a word w 1 and a word w 2. The higher the number, the larger is the semantic difference between the sets. The basic assumption for this function is that K 1 is an already perfect set of keywords, and K 2 needs to be as semantically close as possible. Hence, if we want to add new keywords, we are concerned with how well they will fit in. A keyword set will typically consist of words throughout a greater range of topics. This motivates the first condition. If we want to take keywords away, we want to avoid losing as many semantically distant words as possible because they likely represent different topics in the text. Hence, we assume that taking keywords away is a means to get rid of redundancies. This justifies the second condition. With these conditions, substituting a keyword set K 1 by another set K 2 first and then substituting K 2 by a third set K 3 cannot yield a better result than substituting K 1 by K 3 directly. Thus, Equations M1 to M3 almost fit the definition of a metric. Only symmetry is missing, but in general, we do not want that. For instance, consider the example text from the second section. For further information on quasi-metrics see Wilson, The following example provides some evidence as to why this is a choice. This is not bad for keyword sets of these sizes and very good considering that they do not intersect the lowest possible distance there is 1. In keyword sets that intersect, the distance will yield lower values. Since the semantic aspect of drug legalization gets lost entirely, the value is still relatively high. Except for Atomkraftwerk all other keywords in K 1 are not in the text and both keyword sets have no keyword in common, resulting in a Precision, Recall and F1 of 0. Once again, this illustrates the advantage of our approach: K T , observed and K 1 do not intersect and consequently do not match directly, but, intuitively, K 1 is not a bad set of keywords for the text, and our approach manages to express this intuition. In the case of polysemy, as mentioned in the previous Section 4, there are two cases to consider. This obliviously falls short when there is only one keyword with multiple meanings in one of the keyword sets that should be compared. If there are multiple words in a keyword set with multiple meanings this gets significantly more complex, but should result in a minimum. In the second case there is a single node for each word w , regardless of how many meanings there are. The graph has therefore nodes that connect some node clusters with very different meanings. In the cases of the example graph in Figure 2 this is the word Gras , that connects the drug related nodes to gardening related node. Even with a cross comparison between all words in the two keyword sets it might not be possible to identify all wrong keywords. This result might be leading to the wrong conclusion, that these two sets are very good keyword sets for our text. While our proposed method works only if there is some kind of ground truth keyword set, which is a somewhat limiting factor, an argument can be made that if there is no ground truth keyword set available the text itself could be used. The distance in the graph and the resulting value of the metric should best be especially low to be considered a good keyword set. Popular keyword evaluation methods rely on direct matching without any regard to semantic nuance, making them fast to assign a low level of accuracy to a perfectly adequate keyword set. Hence, we propose using a word graph to provide a richer semantic structure that an evaluation method can use to cast more refined judgment. The advantage becomes clear when comparing our approach with a Precion-Recall-F1-based evaluation: the latter evaluated intuitively good keyword sets, when compared to gold standard sets, as completely deviating and non-fitting. In contrast, our approach, albeit illustrated only by two small examplary and intuitively generated graphs, showed the semantic closeness of the sets to be evaluated and gold standard sets. Since the construction of a complete word graph is an extremely hard task for a language, however, finding manageable, text-specific approximations without sacrificing too much of their quality would presumably fulfil the task with satisfactory results. This may prove difficult enough already: recall that even the graph of our simple three-sentence-example-text is quite extensive and complex despite only being a sample. Since keywords still have to be topical, it makes sense to only approximate the graph locally, i. Given a text T , the most radical local approximation is the graph G T , which only uses the words in T. The task now is to extend G T by a reasonable amount to include words related to the words in G T whatever that means. Finding a good way to do so is not a trivial task either. Hence, our focus for further research is to try and test different extension paradigms. In that case, the latter is still a valid keyword and would be included in the graph G T hyper. The reverse is not generally true. Basing G T hyper on WordNet also opens up the possibility to use, for example, the information theory based metric defined in Resnik to measure the semantic distance between concepts and words. A small distance in information would thus represent a small distance in meaning, and an ideal set to be evaluated would have a distance of 0 to the gold standard set. But that as well is likely to prove infeasible for a keyword evaluation algorithm. Hence, once a decent approximation has been found, another aim is to construct a fast heuristic, for example, to train a neural net or another statistical model with graph data. Finally, we would like to stress the following point: the determination of hierarchy relations within the graph are theory and model dependent and based on techniques of epistemology. A graph can be—based on corpus data—automatically generated or, alternatively, based on judgements of raters. Automatic generation of graphs is often based on statistical regularities of co-occurrences of words. Semantic similarity of two words can be represented through a similar context, which is the view in a distributional theoretic framework. But this depends on the size and quality of the data basis of a study, so that an automatically generated graph will sometimes show semantically implausible relations between words, and semantic relations such as hyperonymy, hyponymy, meronymy, etc. Our graph is a sectional representation of individual mental lexicons because the strength of the semantic relations between nodes in the graph, i. In cognitive psychology and theory of learning, it depends on the individual experience of the language learner how relations in the world are structured in cognition, however, it seems to be indisputable that knowledge is organized by abstraction into concepts, i. All authors contributed equally, making the concepts, and writing the article. All authors contributed to the article and approved the submitted version. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. This section collects any data citations, data availability statements, or supplementary materials included in this article. As a library, NLM provides access to scientific literature. Front Artif Intell. Find articles by Yuki Kyogoku. Find articles by J Nathanael Philipp. Find articles by Michael Richter. Find articles by Clements Rietdorf. Find articles by Tariq Yousef. Edited by: Petra B. Schumacher, University of Cologne, Germany. Received Oct 25; Accepted Feb 23; Collection date Open in a new tab. Translation for the terms in the Figure 2. Translation for the terms in the Figure 3. Similar articles. Add to Collections. Create a new collection. Add to an existing collection. Choose a collection Unable to load your collection due to an error Please try again. Add Cancel. Nachrichtenagentur der Islamischen Republik.

Emerging Europe’s top 10 ski resorts

Buy marijuana Borovets

Should you wish to join them, here are our suggestions of the best places to do so. High in the Caucasus mountains, kilometres from the Georgian capital Tbilisi the drive takes around two and a half hours , Gudauri has been a favourite of skiers-in-the-know for a number of years. Our only complaint is that for what is a purpose-built resort the layout can be awkward a lot of the accommodation is a long walk from the lifts , but most hotels and apartment complexes offer shuttle buses to and from the slopes. With the exception of Georgian public holidays, crowds are unheard of and queues for the lifts non-existent. The slopes at Borovets are split over two separate ski areas, and both offer long-ish runs good for beginners and intermediates in a gorgeous setting below Mt Musala, the highest peak in the Balkans. There are around 50 kilometres of slopes in all, usually immaculately groomed and served by a decent lift system although note that access to the Markudjik ski area can sometimes be hampered by high winds closing the gondola lift. Off-piste is limited however and there is very little to keep experts happy. The resort offers some great accommodation close to the slopes and myriad dining options. One of the best-kept secrets on the emerging Europe ski-circuit is the almost immaculate resort of Bukovel. There is a good range of accommodation, and prices are very cheap. Now the bad news. One of the reasons Bukovel has remained something of a secret is its inaccessibility. It is more than four and a half hours drive from the nearest international airport, Lviv, and the roads in this part of world are not the best. The largest ski area in Central Europe offers 50 kilometres of slopes on both sides of Mt Chopok, including some very steep couloirs on the north face and wide-open, easier tracks on the south side of the mountain. There is a large freeride area and expert skiers will find plenty to keep them happy. Snow-making machines cover most of the slopes and ensure good cover until the end of April. Accommodation is spread over a number of small, satellite villages which means that the vast majority have quick access to and from the slopes, but makes lively apres ski difficult to find if you are in one of the more quiet locations. Great ski school, but costs are relatively high and the lift pass is one of the most expensive in the region. Bansko was a lively market town centuries before it became a ski resort and its historic centre retains a charm unmatched by most ski resorts in the region. There is plenty to do off the slopes — from visiting museums to shopping for handmade local artefacts — which makes it a perfect destination for larger groups which include non-skiers. The pistes themselves and there are nearly 70 kilometres of them, most suited for intermediates are all high altitude and made snow-sure by a sophisticated snow-making system. The downside is that access to the ski area from the town is via a gondola lift for which the queues are the stuff of legend. Waiting an hour is not unheard of at the wrong time of day between 10am and midday. Get there early. Kopaonik, on the border of Serbia and Kosovo, offers 55 kilometres of tree-lined pistes. Runs are quite short but good fun, and there are few crowds: the resort is very well designed and the lift system, which has seen much investment in recent years, including a new six-seat chair-lift, keeps queues to a minimum. Accommodation is good value, and there is plenty to choose from. Prishtina is in theory far closer, but as you are not allowed to cross the border directly from Kosovo to Serbia, you need to go via Montenegro. After all, if there is a more picture postcard perfect place on earth than Lake Bled when covered in ice and snow, then I have yet to see it. There are currently seven places to ski in Montenegro, and the country is keen to develop winter tourism further. With a modest 20 kilometres of mainly gentle, tree-lined slopes served by seven lifts including a brand new chair-lift the resort is currently off the radar of most European skiers, but that could soon change. Competitions are held all winter, the highest calibre being the World Cup event which takes place at the end of January. Alas, when it comes to more conventional skiing, you will almost certainly leave Poland with the impression that it could be fantastic, if only they could get their act together. The two areas are not connected however, and despite the recent installation of new chair-lifts, the crowds and lift queues remain a turn off. In a parallel universe Sinaia is the best place to ski in emerging Europe, not the 10th best. In that universe one company operates the ski lifts instead of two in the real world and only one far-from-cheap lift pass is needed. In that universe high winds do not close the lifts and there is reliable snow Romania, contrary to popular belief, has relatively dry winters. If you get lucky and catch Sinaia on a sunny day, with good snow and no crowds, then it can be easy to think that you are in that parallel universe. Alas, such days are few and far between. Enjoy them — if you can. Unlike many news and information platforms, Emerging Europe is free to read, and always will be. There is no paywall here. We are independent, not affiliated with nor representing any political party or business organisation. We want the very best for emerging Europe, nothing more, nothing less. Your support will help us continue to spread the word about this amazing region. You can contribute here. Thank you. You must be logged in to post a comment. Thank you for the interesting piece, Craig! Before the pandemic, there were international flights coming there, at least from Vienna as far as I know. Cheers from Kyiv, Anastasiia. Oh, and the roads leading to Bukovel from Ivano-Frankivsk have been reconstructed recently and are really nice now we drove there from Kyiv. Craig Turp-Balazs. Gudauri, Georgia pictured above gudauri. Good Incredibly cheap lift pass, high-altitude skiing for all levels Bad Very little apres ski, resort layout means a fair bit of walking — 2. Borovets, Bulgaria borovets-bg. Good Easy access from Sofia, lively apres ski Bad Very little on offer for experts, apres ski too rowdy for some — 3. Bukovel, Ukraine bukovel. Jasna, Slovakia jasna. Good Largest ski area in Central Europe. Bansko, Bulgaria banskoski. Good Loads for non-skiers to do, perfect for families and mixed groups Bad The queue for the gondola lift in the morning can be very long — 6. Kopaonik, Serbia kopaonik. Good Varied skiing with plenty for experts Bad Access is far from easy — 7. Bove c-Kanin, Slovenia kanin. Zakopane, Poland discoverzakopane. Good Great place to watch ski jumping Bad Long queues, main ski areas not connected — Sinaia, Romania sinaiago. Good Lots to do off the slopes, especially the tour of gorgeous Peles Castle Bad Two lift passes needed, unreliable snow, crowded at weekends — Unlike many news and information platforms, Emerging Europe is free to read, and always will be. You may also like. Click here to post a comment. Cancel reply You must be logged in to post a comment. Log in to Reply. Polish comic book heroes set for Netflix adaptation. Where was Young Wallender filmed? Comment Share This!

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