Ontology extension is based on the reuse of the classes existing in other ontologies to properly characterize concepts and reuse existing frameworks and axioms. The reuse of ontology terms creates links between data, making the ontology more valuable. 28
For example, ChEBI contains more than 3 million axioms. Thus, only relevant subsets of ontologies were reused. After searching for classes within the working ontology using the owlready2 Python package, 29 missing chemical compounds and possible reaction types are searched for in other ontologies as listed in Table 1 . To accomplish this, a nested dictionary is used, containing all IRIs of terms alongside their corresponding labels, prefLabels, altLabels, and the names used within the ontology. Applying a class information extraction process that incorporates functions from the owlready2 package, the dictionary was generated for 22 ontologies relevant to the domain of catalysis research. 24 Once the dictionary is loaded into the Python environment, it is searched for classes missing in the working ontology. If one of the labels, prefLabels, altLabels, or names matches the searched entity, the corresponding IRI is added to the dictionary along with the matching value. The IRIs of found terms that are still missing in the working ontology are stored in automatically created text files. The names of the text files include the acronym of the source ontology for further reference.
This ensures that the module retains all the same logical entailments in the full ontology, providing consistency in the ontology subset. The chosen SMLE approach is the BOTTOM module, which contains the terms in the seed, their corresponding superclasses, and the interrelations between them. As the name implies, the class hierarchy is built from the bottom up, gathering the superclasses of the selected class. Thus, for each ontology, a separate subset of relevant classes is created in rdf/xml (owl) format.
The second task for which ROBOT is used in this work is to merge the created subsets of classes and the main ontology into a single ontology with a single .owl-file. Thus, the merging process is used to update the working ontology within existing terms of other ontologies.
Because some of the merged ontologies are aligned with different top-level ontologies, terms that theoretically share the same definition are located at different positions within the class hierarchy. For example, the OBO is the top-level ontology of ChEBI, while the AFO is aligned with the BFO. Both ontologies have, for example, the term “atom”, but at different positions in the class hierarchy. Another factor why the same terms are represented differently is related to the granularity problem of ontologies. This issue arises because ontologies often adopt different levels of details when representing identical knowledge to support different applications. 30
Since all of the utilized ontologies are connected to the domain of catalysis 24 and chemistry, 31 terms with identical designation are assumed equivalent. The equivalence of classes indicates that respective classes share all their instances, and the descriptions of both classes are interlinked. However, the use of the equivalence relation does not imply class equality. Both relations are defined differently in OWL. Equality is denoted by “owl:sameAs”, while the equivalence is represented by owl:equivalentClass. Class equality can only be defined by the description language OWL-Full, and owlready2 supports only equivalence. 29
To identify terms with the same designation that originate from different ontologies and consequently have different IRIs, the mappings created in previous work 24 are used in the processing. These mappings represent all terms shared by two ontologies according to the same IRIs or the same set of labels, prefLabels, names, or altLabels. After merging ontologies to reuse existing terms, the process of creating new classes and subsequently populating the working ontology with new instances is initialized. First, a new instance of a publication is generated as an instance of the “publication” superclass. The DOI and title of the publication retrieved at the beginning of the process are added to the publication instance as datatypes using the “has doi” and “has title” datatype properties, respectively. Extracted chemical compounds that do not exist in the working ontology after merging are then created as new classes within the working ontology, utilizing the context information of the new compounds. Chemical compounds that can be further broken down into compounds and atoms, such as “Al 2 O 3 ” or “titanium dioxide”, or those that are recognized as compounds using pubchempy are created as subclasses of the “molecule” class.
Support material entities, which represent a combination of two or more carrier compounds, like “TiO 2 –SiO 2 ”, or materials such as “MCM-41” are created as subclasses of the “support material” class. Each newly created class and instance are automatically assigned a generated name linked to the number of the processed publications in the working ontology.
Entities from the “Reactant”, “Product” and “Catalyst” categories that represent specific types of chemical entities, such as “light olefin” and “vapour phase propene”, are created as instances of the corresponding chemical compound. Extracted and preprocessed catalyst entities are created as instances of the “chemical substance” class.
Chemical entities, which represent catalysts in the form of
“ <catalytic compound>/<support compound> ” or
“ <catalytic compound>@<support compound> ”
are labelled in the ontology
“ <catalytic compound> supported on <support compound> ”
and linked with their chemical compounds based on their roles in the entity using the “catalytic component of” and “support component of” object properties. A schematic example of interconnections within the ontology is shown in Fig. 3 .
Examples of created entities and assigned relations. Entities within dashed boxes represent instances, while continuous bordered boxes represent classes. |
Table 4 in the appendix lists the object properties and their inverse properties that need to be defined within the working ontology in order to assign the relations between the newly created entities.
The creation of the classes corresponding to the catalyst types is based on the creation of subclasses of the term “catalyst role”, which already exists in the AFO ontology. Roles in ontologies are used to reduce the amount of object properties and thus to speed up reasoning. The corresponding roles of terms are provided as classes in the ontology and terms are linked to them via the “has role” object property. The hierarchical structuring of the catalyst roles is based on the content of the classes extracted from the entities. For instance, within the text corpus, the extracted class of an entity after preprocessing might be a “dispersed catalyst role”, while the catalyst type corresponding to another entity is an “atomically dispersed catalyst role”. Since the second class is identical to the first but with an additional word, it is considered a subclass of the first class. In case no entity from the text corpus has a “dispersed catalyst role” as an extracted class, then an “atomically dispersed catalyst role” is created as a subclass of the “catalyst role”.
Chemical reactions are created as subclasses of the previously extracted reaction “heads”. If there is no corresponding reaction found in other ontologies, a new class is created as a subclass of “chemical reaction (molecular)”, which is also a class within the AFO. For each class created after the merging that corresponds to an extracted entity or a chemical compound, an instance is created with an automatically generated name.
The label of the instance is the same as the label of its corresponding class. The same procedure is applied to the newly created classes. All classes and instances, once created, can be reused for ontology extension with the respective publications. The newly created classes of chemical compounds are linked to their corresponding components via “has part” relations at the instance level.
Created instances are linked to their roles according to the categories and the context using the “has role” relation. The used roles include the “support role”, “reactant role”, “product role”, and “catalyst role”, and all are created as subclasses of the “catalyst role”.
Finally, all created and used instances that are mentioned in the processed publication are linked to the instance of the publication through the “mentioned in” object property. Entities labelled “Characterization” and “Treatment” are added to annotations of publications as comment.
The following competency questions were implemented as SPARQL queries and can thus be easily retrieved from the knowledge graph resulting from the extension of the ontology. The corresponding SPARQL queries are numbered and exemplary input and output of the queries are listed in Table 5 in the appendix and an exemplary SPARQL query is listed in Table 6 in the appendix:
• Give me a list of reactions (1), reactants, support materials, catalysts, and products mentioned in one specific publication, which is a part of the knowledge graph, in one list (2) or separately,
• Retrieve the abstracts from publications in the ontology (3),
• Give me a list of DOIs of publications from the working ontology, which mention the same reactions (4) or the specific reaction (5) or catalyst (6),
• Give me a list of reactions, reactants, support materials, catalysts, and products mentioned in all publications of the knowledge graph (7),
• I need a list of all possible synonyms for the extracted reactants (8), support materials (9), catalysts (10), and products (11) in the form of chemical entities,
• I need possible catalysts where the support material from this paper can be used (12).
The retrieved entities can be used to query Scopus for new publications with similar context. Using the pybliometrics Python package, the search is performed, leading to a query, which has the same structure as a query that works in the Scopus advanced search. With the chosen query type ‘TITLE-ABS-KEY()’ (as depicted exemplarily in Fig. 4 ), the search is performed within the titles, abstracts, and keywords of the publication.
Two types of query formulations for the advanced search for further publications in Scopus, executed by the Python API. |
Since there are multiple ways to name a specific chemical compound, to avoid a large number of possible queries and at the same time allow diversity in the naming of chemical compounds, a trade name or common name and a formula listed in the class annotations of a chemical compound are used for queries' formulation. Moreover to exclude mismatches, the publication will be skipped if during text mining no reaction was found in the text. After a query is executed, its results are downloaded and cached to speed up the subsequent analysis.
After the results are concatenated into one table, duplicates are removed from it. As Scopus contains records of articles published since 1970, an option to filter the results by publication date is integrated into the process, to allow for the inclusion of primarily newer publications into the knowledge graph. Utilization of the pandas Python module 33 allows the resulting DataFrames to be stored as sheets in an Excel file.
Moreover, a set of 28 publications on methanation processes (dataset 2) was used to evaluate how well the created tool works on the different types of catalysed reactions. Hereby the focus was laid on the heterogeneously catalysed conversion of carbon monoxide and carbon dioxide to methane via hydrogenation, which is important for the production of synthetic natural gas. In particular, the valorization of CO 2 together with renewable hydrogen might be considered an integral sustainable path towards the production of renewable gaseous fuels. 36 For that, an extension of an alternate ontology setup similar to the first dataset was performed.
The dataset for training of the model was complemented with 151 sentences manually labelled in label-studio 37 from 18 abstracts of papers to the topic of hydroformylation in the liquid and gas phase. Checkpoints from the model trained by the authors of CatalysisIE and the model trained on the complemented dataset were compared with each other.
To evaluate the difference in the prediction of the checkpoints, ten manually labelled abstracts from papers to the same topic were compared to predictions of both models. Since it is important to gain as many correct distinct predictions from the text as possible to be able to describe the content of the publication using extracted entities, the recall R of the model was evaluated with the number of true positives TP and false negatives FN using eqn (1) . To obtain the true positives and false negatives, the amount of distinct entities was counted and compared with the number of distinct entities from the prediction after qualitative manual labelling of the texts. This comparison for each extracted abstract from dataset 1 is shown in Table 9 in the appendix.
Besides recall, the precision Pr was selected for evaluation of multi-label classification. Because class imbalances are present in the dataset, the precision was calculated using eqn (2) with the number of positives P i instead of true positives TP and the number of used labels N . Furthermore, the standard deviation σ of the precision was selected as a metric and calculated using eqn (3) . The sum of true positives corresponds to the number of correctly predicted instances. Precision and its standard deviation were calculated for the six categories for each of the abstracts.
(1) |
(2) |
(3) |
Extraction of sequences was treated as a binary classification problem, where the sum of TP is correctly extracted from distinct entities and is independent from the assigned label. The sum of true positives and false negatives is the total number of distinct manually labelled entities in the text. The metrics were calculated for the ten manually labelled abstracts from papers to the same topic and compared to predictions of both checkpoints I and II. Here, checkpoint I addresses the complemented model, while checkpoint II addresses the pre-trained checkpoint, provided by the developers of CatalysisIE. The resulting metrics are listed in Table 2 . Deviations in the metrics of the fourth publication may be due to formatting errors in the retrieved abstract, causing extracted tokens to end with citation numbers ( e.g. , “catalysts9”), thus not being counted as found entities.
TP + FN | CP | TP | R | Pr | σ | |
---|---|---|---|---|---|---|
1 | 12 | I | 11 | 91.7 | 83.3 | 40.8 |
II | 10 | 83.3 | 66.7 | 51.6 | ||
2 | 15 | I | 14 | 93.3 | 93.3 | 14.9 |
II | 12 | 80.0 | 80.0 | 44.7 | ||
3 | 15 | I | 13 | 86.7 | 75.6 | 43.3 |
II | 12 | 70.0 | 73.3 | 43.5 | ||
4 | 16 | I | 6 | 37.5 | 35.0 | 23.8 |
II | 6 | 37.5 | 38.7 | 30.7 | ||
5 | 15 | I | 10 | 66.7 | 64.6 | 9.9 |
II | 7 | 66.0 | 61.8 | 26.7 | ||
6 | 15 | I | 13 | 86.7 | 58.3 | 49.2 |
II | 14 | 93.3 | 66.7 | 51.6 | ||
7 | 37 | I | 33 | 89.2 | 88.2 | 21.7 |
II | 24 | 64.9 | 62.7 | 16.3 | ||
8 | 29 | I | 25 | 86.2 | 68.2 | 10.5 |
II | 24 | 82.8 | 64.7 | 11.4 | ||
9 | 25 | I | 12 | 48.0 | 63.6 | 26.0 |
II | 13 | 52.0 | 68.6 | 25.1 | ||
10 | 6 | I | 6 | 100.0 | 100.0 | 0.0 |
II | 6 | 100.0 | 100.0 | 0.0 |
The entities labelled “Characterization” were predicted least accurately. Additionally, there were no “Treatment” labels in the evaluation dataset. Overall, the model trained on the expanded dataset (CP I) was better at predicting entities labelled “Catalyst”. The average recall of the newly trained model for ten abstracts is equal to 86.67% with a standard deviation of 20.85% and shows a high average precision of 71.90%. In comparison, the recall of the old model (CP II) is 80.00% with a standard deviation of 19.37% and an average precision of 66.67%. In both cases only in one text, precision and recall fall under 50%. Furthermore, for the ten publications shown in Table 2 , in the cases where CP I achieved higher precision, σ was lower. This indicates that the dispersion across the different classes in relation to Pr has decreased and therefore the model makes more stable predictions across the classes.
To investigate the performance of the extended model further, ten abstracts from dataset 2 are labelled manually and classified with CP I and CP II to evaluate the metrics as in Table 2 . The resulting metrics are presented in more detail in Table 7 . An average recall of 82.81% with a standard deviation of 22.39% and an average precision of 71.46% was achieved for CP I. Furthermore, an average recall of 79.47% with a standard deviation of 19.92% and an average precision of 73.20% was achieved for CP II. Thus, the extended model can also be applied on dataset 2.
Title recognition by 19 out of 23 processed PDFs from dataset 1 was successful and 26 from 28 publications from dataset 2 could be recognized correctly. Publications of “Royal Society of Chemistry” could not be correctly recognized because the layout of the publications is not integrated in the workflow of the used pdfdataextractor package.
The AFO was chosen as the initial ontology, because of its linkage to the chemical domain and well-defined structure in the class hierarchy. Table 8 lists the terms and textual definitions assigned as equivalent in ontologies for both datasets, which exist in the AFO and are merged into the working ontology from ChEBI.
Chemicals which could not be found in PubChem or in ChEBI are created as instances of the class “chemical substance”. For dataset 1, the ontology is extended with 53 instances of “chemical substance”. Dataset 2 results in 55 instances of “chemical substance” that were also created automatically. Each of the generated instances representing extracted entities and their chemical components is provided with a connection to the publication in which it is mentioned and linked to the corresponding roles as shown in an excerpt of the resulting ontology in Fig. 5 . The reactions that are mentioned within the publication are listed, including the respective participants of the reactions within the knowledge base (upper area of the figure). The individual “cobalt atom”, for example, is connected with the individual “Co-containing catalyst” via the object property “catalytic component of” (right area of the figure), thus indicating the suitable catalytic component of the concept extracted from text. Furthermore, the role of a “bimetallic catalyst role” is asserted to the three individuals on the bottom right of Fig. 5 . The class “bimetallic catalyst role” is created as a subclass of the “catalyst role”, which in turn also has an individual that is connected to other substances via the “has role” object property (bottom left of the figure).
Excerpt from the created ontology for dataset 1 created with Protégé. Boxes marked with yellow circles represent classes and those with purple rhombi are instances. Arrows denote the relationships between them, color-coded as listed in the legend on the right. Small boxes with a plus (+) inside indicate that not all relations of the entity are shown in the figure. |
The knowledge graph with publications from dataset 1 was extended by 48 classes from the other ontologies, including their superclasses and interrelations. In total, 331 new classes, 9 new object properties, 2 new data properties, and 155 new individuals were added to the working ontology. From the new classes, 288 were merged from other ontologies, while none of the new individuals were merged from other ontologies, as expected. The new object and data properties were merged from other ontologies.
In the knowledge graph with dataset 2, 39 classes from other ontologies were imported from other ontologies together with their respective superclasses and interrelations. With this, 222 new classes, 4 new object properties, 2 new data properties, and 130 new individuals were added to the working ontology. Here, 198 from the 222 new classes were merged from other ontologies, while also none of the new individuals were merged from other ontologies. The new object and data properties were merged from the other ontologies listed in Table 1 and counted without the ones already presented in Table 4 in the appendix. The explained ontology metrics are listed in Table 3 .
Metric | Initial ontology | Extended ontology dataset 1 | Extended ontology dataset 2 |
---|---|---|---|
Classes | 3116 | 3447 | 3338 |
Instances | 47 | 203 | 178 |
Logical axioms | 5755 | 6936 | 6596 |
SubClassOf | 4823 | 5372 | 5174 |
Equivalent classes | 178 | 188 | 185 |
Fig. 6 shows the individual “0.5% Co–0.5% Rh supported on Al 2 O 3 ” in an excerpt of Protégé after reasoning with HermiT. 38 The implicit knowledge is highlighted in yellow, showing an increased semantic expressiveness for the individual describing the catalyst complex. Thus, the individual now also can be found when searching the knowledge graph, e.g. , for catalysts that contain cobalt.
Excerpt from Protégé with inferred relations after reasoning for the individual “0.5% Co–0.5% Rh supported on Al O ”. Knowledge inferred by the reasoner is highlighted in yellow, showing increased semantic expressiveness of the individual. |
Most of the terms in both knowledge graphs originate from the ChEBI ontology and identify chemical compounds and atoms. But also, the classes for such terms as “hydrogenation”, “hydroformylation”, and “acylation” are reused from the RXNO ontology. In the current process, entities representing some chemical groups, such as “phenolic substances” or “phenolic species”, can be recognized with the text mining module, but the extension of the ontology with them is not implemented. This includes, for example, entities such as “phenolic substances”, “phenolic species”, and “alkyl species” which are usually classified as products or reactants in the text. Such entities cannot be queried in PubChem, and in ChEBI, the presumed superclasses are placed in different positions. All queries are formulated within the functions in the module “queries” provided in the GitHub repository of this work 39 and can be executed by the Jupiter notebook “user_queries.ipynb” contained in the repository. It contains descriptions of code cells, which execute specific queries, which can answer competency questions formulated in Methods. In addition, some examples of executed functions are also provided in the notebook.
Listings of the resulting publications are found in the provided GitHub repository of this work 39 in the “output” directory.
To further rate the quality of the query result, a random sample in size of 50 publications from the resulting filtered list of 731 publications similar to those of dataset 1 was selected for the evaluation of the queried content. The list of chosen publications for evaluation is provided in the appendix in Table 9 .
These publications are rated as similar to the publications in the knowledge graph (of dataset 1) if the following requirements are fulfilled, based on the evaluation of their abstracts and titles:
• Heterogeneous or homogeneous catalysis or catalysts are mentioned.
• Hydroformylation or hydrogenation is mentioned.
• Rh-, Co-, and Ni-based catalysts with silica, zeolite or aluminium oxide as a support material are mentioned.
According to these restrictions, 34 out of 50 publications of the sample provided are rated as similar to the content of the publications within the knowledge graphs, which is equal to 68% accuracy.
The quality of matched articles to the content of the knowledge graphs was evaluated with dataset 1, dealing with hydroformylation reactions. A random sample of 50 publications was investigated in more detail and abstracts, keywords, and titles were screened for the mention of catalysts, hydrogenation or hydroformylation, and whether Rh-, Co-, and Ni-based catalysts with silica, zeolite or aluminium oxide as a support material were mentioned. With these quite strict criteria and with the lack of case sensitivity in the Scopus API, 68% accuracy was achieved for the random sample. This allows for more structured searches of relevant scientific literature in the domain of catalysis research, which is highly important, especially in this domain, as research is quite heterogeneous and the number of relevant publications in the field is quite high. However, a more thorough post-processing of these found publications needs to be conducted, e.g. by a post-processing that conducts a case-sensitive automated search of the respective keywords in the extracted abstracts from Scopus, to improve the accuracy of the output related publications.
Object property | Reverse object property | ||
---|---|---|---|
Name | Rdfs:Label | Name | Rdfs:Label |
supported_on | Supported on | support_material_of | Support material of |
support_component_of | Support component of | has_support_component | Has support component |
catalytic_component_of | Catalytic component of | has_catalytic_component | Has catalytic component |
mentioned_in | Mentioned in | Mentions | Mentions |
Query no. | Input parameters | Output |
---|---|---|
1 | Doi = r‘10.1021/acsami.0c21749.s001’ | [‘hydroformylation’] |
2 | list_type = ‘all’ | [‘hydroformylation’, ‘olefin’, ‘rhodium atom’, ‘Rh-based atomically dispersed catalyst’, ‘Rh supported on ZnO modified with Pi’, ‘zinc oxide’, ‘phosphate ion’, ‘aldehyde’, ‘linear aldehyde’] |
Doi = r‘10.1021/acsami.0c21749.s001’ | ||
3 | Doi = r‘10.1021/acsami.0c21749.s001’ | Abstract: in the study of heterogeneity of homogeneous processes, effective control of the microenvironment of active sites… |
4 | Doi = r‘10.1021/acsami.0c21749.s001’ | [[‘10.1021/acscatal.1c02014.s001’], [‘10.1021/acscatal.0c04684.s001’], [‘10.1021/acscatal.1c00705.s002’], [‘10.1021/acscatal.1c04359’],…] |
5 | Reac = “hydrogenation”, Doi = none | [[‘10.1021/acscatal.0c04684.s001’], [‘10.1021/acscatal.1c00705.s002’],…] |
6 | Cat = “RhCo”, Doi = none | [[‘10.1021/acscatal.0c04684.s001’], [‘10.1021/acscatal.1c00705.s002’], |
7 | list_type = ‘all’ | [‘styrene’, ‘cobalt atom’, ‘0.5% Co–0.5% Rh supported on Al O ’, ‘Co-containing catalyst’, ‘aluminum oxide’, ‘hydroformylation’,…] |
Doi = none | ||
8 | list_type = ‘reactant’ | [‘olefin’] |
Doi = r‘10.1021/acsami.0c21749.s001’ | ||
9 | list_type = ‘product’ | [‘Aldehyd’, ‘RCHO’, ‘aldehidos’, ‘aldehydes’, ‘Aldehyde’, ‘aldehydum’, ‘an aldehyde’, ‘RC( |
Doi = r‘10.1021/acsami.0c21749.s001’ | ||
10 | Doi = r‘10.1021/acsami.0c21749.s001’ | [[‘45Rh’, ‘rhodium’, ‘Rh’, ‘rodio’, ‘Rh(111)’, ‘rhodium atom’]], [[‘Rh on ZnO modified with Pi’, ‘Rh supported on ZnO modified with Pi’]] |
11 | Doi = r‘10.1021/acsami.0c21749.s001’ | [[‘zinc oxide’, ‘oxyde de zinc’, ‘Zinkoxid’, ‘oxido de cinc’, ‘ZnO’], [‘phosphate ions’, ‘Pi’, ‘phosphate’, ‘phosphate ion’]] |
12 | Doi = r‘10.1021/acscatal.1c02014.s001’ | [[‘silicon dioxide’, ‘Rh supported on SiO catalyst’], [‘silicon dioxide’, ‘Rh P nanoparticle supported on SiO support material’], [‘silicon dioxide’, ‘Rh7Co1P4 supported on SiO ’],…] |
only_doi = false |
TP + FN | CP | TP | R | Pr | σ | |
---|---|---|---|---|---|---|
1 | 15 | I | 13 | 86.7 | 68.3 | 32.5 |
II | 12 | 80.0 | 61.7 | 36.1 | ||
2 | 19 | I | 15 | 78.9 | 82.7 | 28.9 |
II | 15 | 78.9 | 82.7 | 28.9 | ||
3 | 9 | I | 9 | 100.0 | 93.3 | 14.9 |
II | 9 | 100.0 | 93.3 | 14.9 | ||
4 | 11 | I | 6 | 54.5 | 66.7 | 28.9 |
II | 7 | 63.6 | 70.8 | 26.0 | ||
5 | 6 | I | 6 | 100.0 | 100.0 | 0.0 |
II | 6 | 100.0 | 100.0 | 0.0 | ||
6 | 14 | I | 8 | 57.1 | 43.8 | 51.5 |
II | 8 | 57.1 | 43.8 | 51.5 | ||
7 | 12 | I | 12 | 100.0 | 73.6 | 29.7 |
II | 11 | 91.7 | 72.6 | 42.6 | ||
8 | 12 | I | 7 | 58.3 | 69.3 | 41.3 |
II | 6 | 50.0 | 49.3 | 46.6 | ||
9 | 9 | I | 4 | 44.4 | 69.0 | 27.0 |
II | 5 | 55.6 | 73.8 | 25.1 | ||
10 | 15 | I | 15 | 100.0 | 92.6 | 13.5 |
II | 15 | 100.0 | 92.6 | 13.5 |
Term label | IRIs + Definitions | |
---|---|---|
In the AFO | In ChEBI | |
‘Chemical substance’ | http://purl.allotrope.org/ontologies/material#AFM_0001097 | http://purl.obolibrary.org/obo/CHEBI_33250 |
A chemical substance is a portion of material that is matter of constant composition best characterized by the entities (molecules, formula units, atoms) it is composed of [IUPAC] | A chemical entity constituting the smallest component of an element having the chemical properties of the element | |
‘Anion’ | http://purl.allotrope.org/ontologies/material#AFM_0000161 | http://purl.obolibrary.org/obo/CHEBI_22563 |
An anion (−) is an ion with more electrons than protons, giving it a net negative charge (since electrons are negatively charged and protons are positively charged) | A monoatomic or polyatomic species having one or more elementary charges of the electron | |
‘Ion’ | http://purl.allotrope.org/ontologies/material#AFM_0000077 | http://purl.obolibrary.org/obo/CHEBI_24870 |
An ion is an atom or molecule in which the total number of electrons is not equal to the total number of protons, giving the atom or molecule a net positive or negative electrical charge | A molecular entity having a net electric charge | |
‘Role’ | http://purl.obolibrary.org/obo/BFO_0000023 | http://purl.obolibrary.org/obo/CHEBI_50906 |
B is a role means: b is a realizable entity and b exists because there is some single bearer that is in some special physical, social, or institutional set of circumstances in which this bearer does not have to be and b is not such that, if it ceases to exist, then the physical make-up of the bearer is thereby changed [BFO] | A role is particular behavior which a material entity may exhibit | |
‘Cation’ | http://purl.allotrope.org/ontologies/material#AFM_0000189 | http://purl.obolibrary.org/obo/CHEBI_36916 |
A cation (+) is an ion with fewer electrons than protons, giving it a positive charge | A monoatomic or polyatomic species having one or more elementary charges of the proton | |
‘Group’ | http://purl.obolibrary.org/obo/BFO_0000023 | http://purl.obolibrary.org/obo/CHEBI_24433 |
A group is an aggregate of people | A defined linked collection of atoms or a single atom within a molecular entity | |
‘Atom’ | http://purl.allotrope.org/ontologies/material#AFM_0001028 | http://purl.obolibrary.org/obo/CHEBI_33250 |
An atom is a smallest particle still characterizing a chemical element. It consists of a nucleus of a positive charge carrying almost all its mass (more than 99.9%) and Z electrons determining its size | A chemical entity constituting the smallest component of an element having the chemical properties of the element | |
‘Chemical substance’ | http://purl.allotrope.org/ontologies/material#AFM_0001097 | http://purl.obolibrary.org/obo/CHEBI_59999 |
A chemical substance is a portion of material that is matter of constant composition best characterized by the entities (molecules, formula units, atoms) it is composed of | A chemical substance is a portion of matter of constant composition, composed of molecular entities of the same type or of different types |
Author contributions, conflicts of interest, acknowledgements.
† Electronic supplementary information (ESI) available: https://github.com/AleSteB/CatalysisIE_Knowledge_Graph_Generator. See DOI: |
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Research Paper Appendix | Example & Templates. Published on 15 August 2022 by Kirsten Dingemanse and Tegan George. Revised on 25 October 2022. An appendix is a supplementary document that facilitates your reader's understanding of your research but is not essential to your core argument. Appendices are a useful tool for providing additional information or clarification in a research paper ...
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Label the appendices: Label each appendix with a capital letter (e.g., "Appendix A," "Appendix B," etc.) and provide a brief descriptive title that summarizes the content. F ormat the appendices: Follow the same formatting style as the rest of your paper or report. Use the same font, margins, and spacing to maintain consistency.
Learn the functions, tips, format and examples of writing an appendix for a research paper. An appendix is a supplementary section that includes raw data, tables, figures, maps and other supporting materials.
Information in this section is as outlined in the APA Publication Manual (2020), sections 2.14, 2.17, 2.24, and 7.6. Appendices are used to include information that supplement the paper's content but are considered distracting or inappropriate for the overall topic. It is recommended to only include an appendix if it helps the reader ...
An appendix is a supplemental section of a research paper that provides additional information, data, or materials to support the main content. The appendix is usually placed at the end of the document and is numbered with letters or numbers, such as "Appendix A," "Appendix B," etc.
4. Add page numbers. You should make sure the appendix has page numbers at the bottom right corner or the center of the page. Use the same page number formatting for the appendix that you used for the rest of the paper. Continue the numbering from the text into the appendix so it feels like part of the whole.
A research paper appendix can include a wide range of supporting materials, such as: Raw data sets or statistical tables that are too extensive for the main text. Detailed descriptions of research methodologies, instruments, or protocols. Interview transcripts or survey questionnaires. Correspondence with research participants or collaborators.
An appendix is an academic work section that contains additional information (statistics, references, tables, figures, etc.) that cannot be included in the main text. This component is usually placed after the reference list at the end of a research paper or dissertation.
Each appendix begins on a new page. The order they are presented is dictated by the order they are mentioned in the text of your research paper. The heading should be "Appendix," followed by a letter or number [e.g., "Appendix A" or "Appendix 1"], centered and written in bold. Appendices must be listed in the table of contents [if used].
The appendix of a paper consists of supporting information for the research that is not necessary to include in the text. This section provides further insight into the topic of research but happens to be too complex or too broad to add to the body of the paper. A paper can have more than one appendix, as it is recommended to divide them ...
An appendix in writing is a supplementary section that is included at the end of a document, such as a research paper, report, or book. It contains additional information that is relevant to the main text but not essential for understanding the core content.
An appendix is a section added to the end of a research paper to give readers extra information. Appendices are labeled with numbers or letters and are often a good place to include data that might be distracting in the main text. The word appendix comes from the root word append, a verb meaning "to attach or add.".
An appendix or appendices should always be inserted after your Reference List; however, the appropriateness of appendix content really depends on the nature and scope of your research paper. For a more in-depth review of what supplemental materials might be included in a social science appendix, be sure to review Section 2.14 "Appendices ...
Title of the appendix can be in the same format as the title of the other sections of your research paper or presentation. You can write it in the same font style and size. It can also be written in all capital letters, i.e. APPENDIX or in title or sentence case, i.e. Appendix. Use Appendix A, Appendix B, Appendix C and so on to give them a ...
Each appendix begins on a new page. The order they are presented is dictated by the order they are mentioned in the text of your research paper. The heading should be "Appendix," followed by a letter or number [e.g., "Appendix A" or "Appendix 1"], centered and written in bold type. If there is a table of contents, the appendices must be listed.
Then, gather all the information you need to place in your appendix and assess their relevance to the research paper. Remember, your purpose should not be to attach each and every little detail you found in your research. Next, organize your appendix; the order it should appear should be properly prearranged. Use sections, headings, subheading ...
The appendix is a section at the end of the paper with additional information that doesn't belong in the main text. It shouldn't have any arguments, quotes, or conclusions. You must use it to support the claims you already made in your essay — appendices help illustrate everything better.
A research paper explores and evaluates previously and newly gathered information on a topic, then offers evidence for an argument. It follows academic writing standards, and virtually every college student will write at least one. Research papers are also integral to scientific fields, among others, as the most reliable way to share knowledge.
We have explored index additions and deletions and associated issues in an array of research papers over the years, and our findings have inspired a new approach to cap-weighted indexing. 1 In each paper, we keep a narrow focus on one aspect of this work: the reconstitution of a cap-weighted index. The reconstitution puzzle is a rich and nuanced topic, and our analysis has revealed several ...
Based on the work of thousands of volunteers, TRB delivers an extensive research program; convenes leaders, practitioners, and academics from around the world; and provides timely policy advice on issues facing the transportation community.
If you've used ChatGPT or other AI tools in your research, describe how you used the tool in your Method section or in a comparable section of your paper. ... You may also put the full text of long responses from ChatGPT in an appendix of your paper or in online supplemental materials, so readers have access to the exact text that was ...
The search for definitive biosignatures—unambiguous markers of past or present life—is a central goal of paleobiology and astrobiology. We used pyrolysis-gas chromatography coupled to mass spectrometry to analyze chemically disparate samples, including living cells, geologically processed fossil organic material, carbon-rich meteorites, and laboratory-synthesized organic compounds and ...
Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.
This allows for automated classification of research papers with regard to those classes, increasing FAIRness of the classified texts. ... Table 4 in the appendix lists the object properties and their inverse properties that need to be defined within the working ontology in order to assign the relations between the newly created entities.