dataesr/scientific-paragraphs-categorization huggingface dataset:
2. A set of human-generated semantically related word pairs, based on the target lexical units. 3. A subset of semantically related word pairs, rated for the strength of the relation between them. We anticipate that our new collection of semantic relation pairs will not only be of considerable use in computational areas in which semantic relations play a role (such as Distributional Semantics, Natural Language Understanding/Generation, and Opinion Mining), but also in studies in theoretical linguistics and psycholinguistics. In addition, our dataset may be of major interest for research groups working on automatic measures of semantic relatedness, as it allows a principled evaluation of such tools. Finally, since the target lexical units are drawn from the GermaNet database, our results will be directly relevant for assessing, developing, and maintaining this resource.
Related work Over the years a number of datasets have been made available for studying and evaluating semantic relatedness. For English,  Rubenstein and Goodenough (1965)  obtained similarity judgements from 51 subjects on 65 noun pairs, a seminal study which was later replicated by  Miller and Charles (1991), and Resnik (1995). Finkelstein et al. (2002)  created a set of 353 English noun-noun pairs rated by 16 subjects according to their semantic relatedness on a scale from 0 to 10. For German,  Gurevych (2005)  replicated Rubenstein and Goodenough's experiments by translating the original 65 word pairs into German. In later work, she used the same experimental setup to increase the number of word pairs to 350  (Gurevych, 2006). The dataset most similar to ours is BLESS  (Baroni and Lenci, 2011) , a freely available dataset that includes 200 distinct English concrete nouns as target concepts, equally divided between living and nonliving entities, and grouped into 17 broad classes such as bird, fruit.
While syntagmatic and paradigmatic relations can hold between a variety of linguistic units (such as morphemes, phonemes, clauses, or sentences), the focus of this research is on the relations between words. Many studies in computational linguistics work on the assumption that paradigmatic semantic relations hold between words. As will become apparent in the course of this work, it is necessary to move beyond these definitions for an appropriate investigation of paradigmatic semantic relations. According to Cruse (1986), sense is defined as "the meaning aspect of a lexical unit", and he states that "semantic relations" hold between lexical units, not between lexemes. The goal of this work is to create a database of semantic relations for German adjectives, nouns and verbs, focussing on the three types of paradigmatic relations referred to as sense-relations by  Lyons (1968)  and  Lyons (1977) : synonymy, antonymy, and hypernymy.
Target source: GermaNet GermaNet is a lexical-semantic word net that aims to relate German nouns, verbs, and adjectives semantically. GermaNet has been modelled along the lines of the Princeton WordNet for English  (Miller et al. , 1990; Fellbaum, 1998)  and shares its general design principles  (Hamp and Feldweg, 1997; Kunze and Wagner, 1999; Lemnitzer and Kunze, 2007). For example, lexical units denoting the same concept are grouped into synonym sets ('synsets'). These are in turn interlinked via conceptual-semantic relations (such as hypernymy) and lexical relations (such as antonymy). For each of the major word classes, the databases further take a number of semantic categories into consideration, expressed via top-level nodes in the semantic network (such as 'Artefakt/artifact', 'Geschehen/event', 'Gefühl/feeling').
However, in contrast to WordNet, GermaNet also includes so-called 'artificial concepts' to fill lexical gaps and thus enhance network connectivity, and to avoid unsuitable co-hyponymy (e. g. by providing missing hypernyms or hyponyms). GermaNet also differs from WordNet in the way in which it handles part of speech. For example, while WordNet employs a clustering approach to structuring adjectives, GermaNet uses a hierarchical structure similar to the one employed for the noun and verb hierarchies. Finally, the latest releases of WordNet and GermaNet also differ in size: While WordNet 3. 0 contains at total of 117,659 synsets and 155,287 lexical units, the respective numbers for GermaNet 6. 0 are considerably lower, with 69,594 synsets and 93,407 lexical units. Since GermaNet is the largest database of its kind for German, and as it encodes all types of relations that are of interest for us (synonymy, antonymy, and hypernymy), it represents a suitable starting point for our purposes.
The set of word pairs used as input is a carefully selected subset of responses acquired in the generation experiment. 4 For each of the 99 targets and each of the semantic relations antonymy, synonymy, and hypernymy two responses were included (if available): the response with the highest frequency (random choice if several available), and a response with a lower frequency (2, if available, otherwise 1; random choice if several available). Multi-word responses and blanks were excluded from consideration. A manual post-processing step aimed to address the issue of duplicate pairs in the randomly generated dataset, where the same responses had been generated for two of the relations. In theory, each target should have 6 associated pairs (2xANT, 2xHYP, 2xSYN). In practice, there are sometimes fewer than 6 pairs per target in the dataset, because (i) for some targets, only one response is available for a given relation (e.
g. , if all 10 participants provided the same response), or (ii) no valid response of the required frequency type is available. The resulting dataset includes 1,684 target-response pairs altogether, 546 of which are adjective pairs, 574 noun pairs, and 564 verb pairs. To avoid confusion, the ratings were collected in separate experimental settings, i. e. , for each word class and each relation type, all generated pairs were first judged for their strength of one relation, and then for their strength of another relation.
Conclusion This article presented a new collection of semantically related word pairs in German which was compiled via human judgement experiments. The database consists of three parts: 1. A representative selection of target lexical units drawn from GermaNet, using a principled sampling technique and taking into account the three major word classes adjectives, nouns, and verbs, which are balanced according to semantic category, polysemy, and type frequency. 2. A set of 8,910 human-generated semantically related word pairs, based on the target lexical units. 3. A subset of 1,684 semantically related word pairs, rated for the strengths of relations. To our knowledge, our dataset is the first that (i) focuses on multiple paradigmatic relations, (ii) systematically works across word classes, (iii) explicitly balances the targets according to semantic category, polysemy and type frequency, and (iv) explicitly provides positive and negative rating evidence.
Introduction The connections between WordNets, large semantic databases grouping lexical units into synonym sets or synsets, are an important resource in knowledge-based multilingual language processing. EuroWordNet  (Vossen, 1997)  aims to build language-specific WordNets among the same lines as the original WordNet 1  (Miller et al. , 1990) , using Inter-Lingual-Indexes to weave a web of equivalence relations between the synsets contained within the databases. Cornetto 2  (Vossen et al. , 2007) , a Dutch lexical-semantic collection of data associated with the Dutch EuroWordNet 3 , consists of more than 118 000 synsets. The equivalence relations establish connections between Dutch and English synsets in Princeton WordNet version 1. 5 and 2. 0. We update these links to Princeton WordNet version 3.
0 by the mappings among WordNet versions made available by TALP-UPC 4. The equivalence relations between Cornetto and Princeton have been established semi-automatically by  Vossen et al. (1999). Manual coding was carried out for the 14 749 most important concepts in the database. These include the most frequent concepts, the concepts having a large amount of semantic relations and the concepts occupying a high position in the lexical hierarchy. Automatic linkage was done by mapping the bilingual Van Dale database 5 to WordNet 1. 5. For every WordNet synset containing a dictionary's translation for a particular Dutch word, all its members were proposed as alternative translations. In the case of only one translation, the synset relation was instantly assumed correct, while multiple translations were weighted using several heuristics, such as measuring the conceptual distance in the WordNet hierarchy.
Covered Total   3  shows that the error rate of Cornetto's equivalence relations on the Sclera and Beta subsets is much lower than the error rate on the whole set (section 2). We attribute this difference to the fact that  Vossen et al. (1999)  carried out manual coding for the most important concepts in the database (see section 1), as the Sclera and Beta pictographs tend to belong to this category. In these cases, every synset has between one and two automatically derived English equivalents on the average, allowing us to roughly compare with the initial quality check of the equivalence relations performed by  Vossen et al. (1999)  showing that, in the event of a Dutch synset having only one English equivalent, 86% of the nouns and 78% of the verbs were correctly linked, while the ones having two equivalents were appropriate in 68% and 71% of the cases respectively.
The semantic transparency of a constituent of a compound, i. e. , the constituent semantic transparency, is the extent to which the constituent retains its meaning in the actual meaning of the compound. Semantic similarity between the literal meaning and the actual meaning of a compound can be used to estimate the overall semantic transparency of a compound, for the more the literal meaning is retained in the actual meaning, the more similar they are. The same technique can be used to estimate constituent semantic transparency. Semantic transparency can be quantified; if we assign 0 to "fully opaque" and assign 1 to "fully transparent", then semantic transparency can be quantified as a closed interval [0, 1]. The quantitative analysis of semantic transparency must be supported by semantic transparency datasets.
In previous semantic transparency related studies on Chinese compounds, some researchers created some datasets to support their own studies. But this kind of datasets are usually relatively small and restrictive, so cannot be used widely, for example,  (徐彩华 and 李镗, 2001; Myers et al. , 2004; 干 红梅, 2008; Mok, 2009) , etc. Some datasets, although large enough and can be used in other studies, are not publicly accessible, for example,  (王春茂 and 彭聃龄, 1999; 高兵 and 高峰强, 2005) , etc. A large and publicly accessible semantic transparency dataset of Chinese compounds is still a gap in Chinese language resources. Crowdsourcing, as an emerging method of data collection and resource construction  (Snow et al. , 2008; Callison-Burch and Dredze, 2010; Munro et al. , 2010; Schnoebelen and Kuperman, 2010; Gurevych and Zesch, 2013; Wang et al.
Intra-group Consistency of OST Results There are 21 groups and in each group there are two intra-group repeated words, so there are a total of 42 such words. Each intra-group repeated word appears twice, so we can obtain two OST results r 1 , r 2. The difference value between the two results, d = |r 1 -r 2 |, of each intra-group repeated word is calculated, so there are 42 difference values. Among them, the maximum value is 0. 29; the minimum value is 0; the median is 0. 1; their mean is 0. 11; and their standard deviation is 0. 08; all of these values are low and indicate that these OST datasets have good intra-group consistency (see Table  2 ).
Intra-group Consistency of CST Results Each intra-group repeated word has two constituents, c 1 , c 2 , so each constituent gets two CST results, i. e. , r c1,1 , r c1,2 and r c2,1 , r c2,2. We calculate the difference values for the two constituents, d 1 = |r c1,1 -r c1,2 | and d 2 = |r c2,1 -r c2,2 |, and get 42 difference values of the first constituents and 42 difference values of the second constituents. Among the difference values of the first constituents, the maximum value is 0. 27; the minimum value is 0; the median is 0. 09; their mean is 0. 1, and their standard deviation is 0. 07; all of these values are low, this indicates that the CST results of the first constituents in the CST datasets of the 21 groups have good intra-group consistency. Among the difference values of the second constituents, the maximum value is 0.
Inter-group Consistency We inserted two inter-group repeated words, w 1 "地步", w 2 "高山", into all of these 21 groups G i (i = 1, 2, 3,. , 21); we can evaluate the inter-group consistency by comparing their semantic transparency rating results in different groups. Since w 1 , w 2 appear in all OST and CST questionnaires of 21 groups, we can obtain (1) 21 OST results of w 1 , (2) 21 OST results of w 2 , (3) 21 CST results of each of the two constituents w 1,c1 , w 1,c2 of w 1 , and (4) 21 CST results of each of the two constituents w 2,c1 , w 2,c2 of w 2. Standard deviation can be used to measure difference, for example, the standard deviation of the 21 OST results of w 1 is 0. 2; this value is small and indicates high consistency; because these 21 results are from the OST datasets of 21 groups respectively, so we can say that these 21 OST datasets have good intergroup consistency.
Correlation between OST and CST Results Each compound in the datasets has two constituents; both constituents affect the OST of the compound, but neither of them can solely determine the OST of the compound. So the mean of the two CST values of a compound is a fairly good estimation of its OST value. Therefore, if the datasets are reliable, in each group, we should observe strong correlation between the OST results and their corresponding means of the CST results. For each group, we calculate three Pearson product-moment correlation coefficients (r); r 1 is the r between the OST results and their corresponding CST results of the first constituents; r 2 is the r between the OST results and their corresponding CST results of the second constituents; and r 3 is the r between the OST results and their corresponding means of the CST results.
The r 3 values of the 21 groups are all greater than 0. 9 which indicates very strong correlation; among them, the maximum value is 0. 96; the minimum value is 0. 91; and their mean is 0. 94 (SD = 0. 02); the r 1 and r 2 values are also  reasonably high (see Table  5 ) 4. The results support the reliability of these datasets.
Merging and Normalization The evaluation results show that the collected data are generally reliable and have relatively high intragroup and inter-group consistency which further indicate that these datasets share similar scale and are basically comparable, so we can merge the 21 OST datasets into one big OST dataset D ost and merge the 21 CST datasets into one big CST dataset D cst. When we merge these datasets, we delete all the extra words which are used to evaluate the inter-group consistency; for the repeated words which are  used to evaluate the intra-group consistency, the final result of each of them is the mean of its two results. According to our definition, the range of semantic transparency value is [0, 1], but the experimental results are obtained using 7-point scales, so we need to normalize these results in order to map them to the range [0, 1].
The normalized OST and CST results will be merged into D ost and D cst respectively. Assume that, in the dataset D ost , the OST result of the ith (i = 1, 2, 3,. , 1176) word is S w i , and the normalized result is S ′w i , then,  And assume that, in the dataset D cst , the CST result of the jth (j = 1, 2) constituent of the ith word is S c i,j , and the normalized result is S ′c i,j , then, S ′c i,j = S c i,j -1 6 S ′w i = S w i -.
79; the minimum is 0. 22; the median is 0. 6; and their mean is 0. 58 (SD = 0. 1). The distributions of OST, C1. CST, and C2. CST results are similar; all of them are negatively skewed (see Figure  1 ), and their estimated skewnesses are -0. 66, -0. 77, and -0. 63 respectively. These distributions exhibit that more compounds and their constituents in our datasets have relatively high semantic transparency values.
9 datasets to integrate our LRs with LLOD. DBPedia Italian is an open project developed and maintained by the Web of Data 10 research unit of Fondazione Bruno Kessler 11. 6All inflectional codes are built by means of local grammars in the form of Finite State Automata/Transducers. 7The taxonomy we use is structured on the basis of the indications given by the ICCD guidelines. Therefore, the tags RA1SUORC stands for Archaeological Remains/Tools/Receptacles and Containers. 8http://www. w3. org/standards/semanticweb/data. 9http://it. dbpedia. org/?lang=en. According to Linked Data prescriptions, URI schema is structured as http://it. dbpedia. org/resource/ordine_dorico Resource URI http://it. dbpedia. org/page/ordine_dorico HTML representation http://it. dbpedia. org/data/ordine_dorico. { rdf | n3 | json | ntriples } Machine-readable resource representation.
Acknowledgements The authors would like to thank the  Department of Swedish at the University of Gothenburg  and the three anonymous reviewers. The research presented here was supported by the  Swedish Research Council  (grant agreement  2010-6013 ), the  Bank of Sweden Tercentenary Foundation  (grant agreement  P120076:1 ), and by the  University of Gothenburg  through its support of the  Centre for Language Technology and of Språkbanken.
Acknowledgements This work was supported by the grant  GP13-03351P  of the  Grant Agency of the Czech Republic , projects  LH12093  and  LM2010013  of the  Ministry of Education, Youth and Sports of the Czech Republic , and the  EU   FP7  project  610516  " QTLeap ". It has been using language resources distributed by the  LIN-DAT/CLARIN 7  project of the  Ministry of Education, Youth and Sports of the Czech Republic  (project  LM2010013 ).
Acknowledgements The authors would like to acknowledge the financial supports from  Capes/PDSE  under Processes  BEX 12751/13-8  and  BEX 6561/13-6  and also national funds through FCT -Fundacão para a Ciência e a Tecnologia, under project  PEst-OE/EEI/LA0021/2013. We would like to thank the comments of the anonymous reviewers, which helped to improve this paper.
Acknowledgements We would like to thank  Tom Vanallemeersch  for providing us with the EuroParl and Opus alignment tables. We are also grateful to  Martijn van der Kooij  for sending us the mappings between the Dutch and English Sclera pictograph file names made in the Picto-selector project. The research in this paper was partly funded by the support fund  M. M. Delacroix  under the  Picto  project, by the  EU-CIP  project  Able-To-Include , and by the  Program Advisory Committee of the Advanced Master of Artificial Intelligence of the University of Leuven.
Acknowledgements The first author was supported by  DAAD (Deutscher Akademischer Austausch Dienst)  for a research stay at the  University of Konstanz  in 2013-14s. We are also thankful to  Prof Miriam Butt  for hosting the first author at the  University of Konstanz. We would like to thank  Bhuvana Narasimhan ,  Miriam Butt  and the anonymous reviewers for their useful remarks on this paper. Any errors that remain are our own.
This paper proposes a notion of fuzzy graph database and describes a fuzzy query algebra that makes it possible to handle such database, which may be fuzzy or not, in a flexible way. The algebra, based on fuzzy set theory and the concept of a fuzzy graph, is composed of a set of operators that can be used to express preference queries on fuzzy graph databases. The preferences concern i) the content of the vertices of the graph and ii) the structure of the graph. In a similar way as relational algebra constitutes the basis of SQL, the fuzzy algebra proposed here underlies a user-oriented query language and an associated tool implementing this language that are also presented in the paper.
I. INTRODUCTION Much work has been done about fuzzy querying of relational databases, cf. for instance  [1]  or  [2] , which led in particular to a fuzzy extension of the SQL language, called SQLf  [3]. However, even though relational databases are still widely used, the need to handle complex data has led to the emergence of other types of data models. In the last few years, a new concept has started to attract a lot of attention in the database world, namely that of graph databases (see eg. [4] ,  [5] ,  [6] ,  [7] ,  [8] ,  [9]  and  [10] ), whose basic purpose is to efficiently manage networks of entities where each node is described by a set of characteristics (for instance a set of attributes), and each edge represents a link between entities. Such a database model has many potential applications, e.
g. for modeling social networks, RDF data, cartographic databases, bibliographic databases, etc. Graph databases raise new challenges in terms of flexible querying since two aspects may be involved in the preferences that a user may express: i) the content of the nodes and ii) the structure of the graph itself. Furthermore, graph database management systems still lack query languages with a clear syntax and semantics  [11]. Our work tackles these two problems as we aim to outline a fuzzy algebra suited to querying graph databases in a flexible way, for graph databases that may be fuzzy or not. Our contributions are: (i) a formal definition of fuzzy graph databases and a fuzzy algebra for querying fuzzy or crisp graph databases and (ii) a language based on this algebra with an associated prototype software.
The remainder of the paper is structured as follows. Section II presents some background notions about graph databases, fuzzy set theory and fuzzy graphs. Section III describes the main elements that may be involved in a fuzzy query addressed to a graph database. Section IV is the heart of the paper: it proposes a definition of a fuzzy graph database and presents the fuzzy query algebra. Then Section V discusses of a concrete language based on the algebra, and presents an tool implementing this language. Related work is discussed in Section VI. Section VII recalls the contributions and outlines perspectives for future work.
II. BACKGROUND NOTIONS A. Graph databases A graph database management system enables managing data for which the structure of the schema is modeled as a graph (nodes are entities and edges are relations between entities), and data is handled through graph-oriented operations and type constructors  [12]. Among the existing systems, let us mention AllegroGraph  [13] , InfiniteGraph  [14] , Neo4j  [15]  and Sparksee  [16]. There are different models for graph databases (see  [12]  for an overview), including the attributed graph (aka. property graph) aimed to model a network of entities with embedded data. In this model, nodes and edges may contain data in attributes (aka. properties).
III. FUZZY PREFERENCES ON GRAPH DBS In this section, we describe the main elements that may appear in a fuzzy query addressed to a graph database. Two types of preferences have to be considered: those on content and those on structure. 1) Preferences on the node contents: The idea is to express flexible conditions about the attributes associated with nodes and/or vertices of the graph. An example is: "find the people who are young, highly educated, and live in Eastern Europe" (assuming that each node contains information about the age, education level, address, etc. , of the person it corresponds to). Compound conditions may also be expressed using a large range of fuzzy connectives. We do not get into more detail as this aspect has been thoroughly studied in a relational context  [2]. 2) Preferences on the graph structure : Hereafter, we describe the concepts of fuzzy graph theory that appear the most useful in a perspective of graph database querying.