Ontology-Based Methodology for Knowledge Acquisition from Groupware
Abstract
:Future Application
Abstract
1. Introduction
- (1)
- An ontology-based framework with changeable modules to harvest knowledge from groupware discussions. The uniqueness of this framework lies in its five processing phases and components; earlier closet framework [23] focuses on event extraction with four processing phases, which are covered by the first three phases in our proposed framework. This novelty of our framework is the inclusion of an acquisition hub and a knowledge chamber, which are not present in earlier frameworks.
- (2)
- A facts enrichment approach (FEA) for the identification and hooking of new concepts from sentences into an existing ontology, taking into consideration the notions of equality, similarity, and equivalence of concepts. The novelty of the FEA lies within its ability to identify and insert/hook a concept with less information such as those coming from sentences into an existing ontology.
2. Literature Survey
2.1. Ontology in Knowledge Representation
2.2. Framework for Knowledge Extraction
2.3. Ontology Equality of Concepts
- Most of the previous research efforts in this domain are similar in that all are looking for new knowledge to add into a destination ontology from another existing ontology.
- Within such efforts, there is no clear consensus on the notion of equality, similarity, and equivalence of concepts, which is a necessity for the recognition of new concepts from any given source to be compared with an existing ontology.
- The literature is also scant on a technique for the insertion/hooking of a newly recognized concept into an existing ontology.
3. Design
3.1. Groupware Chamber
3.2. Cleansing Chamber
3.3. Harvesting Hub
3.4. Acquisition Hub
- (a)
- New knowledge is recognised.
- (b)
- New knowledge is inserted/hooked into the existing knowledge base.
- an entirely new concept, or
- an existing concept with a new relation, or
- an existing concept with a new attribute.
- Labels need not be the same at the outset but should be made the same once recognised as equal, similar and/or of equivalence.
- The set of attributes and values cannot be expected to be the same, but they must not contradict. Once they are deemed to be the same, then the attributes must be unioned (take the union of both sets).
- The relations in and out of the concept cannot be expected to be the same, but they must not contradict. Once they are deemed to be the same, then the relations also must be unioned.
- ❖
- one attribute in a concept labelled B but not in a concept labelled C.
- ❖
- the same for relations in and out of B and C.
- ❖
- but if the same attribute is in both, the values cannot be different,
- ❖
- if the same relations exist, they must go to or come from the same concepts.
- ❖
- if the same attribute is in both, the values cannot be different,
- ❖ if the same relations exist, they must go to or come from the same concepts
- Equality
- ❖ Exact equality can be when all are the same (label, attributes and values, and relations in/out). This is, however, not very likely to be obtained since the inputs are sentences, hence with lesser information. Other forms of equality may be defined in terms of some level of equivalence and/or similarity.
- Equivalence
- ❖ Equivalence characterises a condition of being equal or equivalent in value, worth, function, etc. (e.g., equivalent equations are algebraic equations that have identical solutions or roots). This may translate to having different labels but with attributes and values and relations in/out that may be similar. Considering the aim of this study, this notion is not very useful at the moment.
- Similarity
- ❖ Similarity describes having resemblance in appearance, character, or quantity without being identical (e.g., similar triangles are the same shape <same angles>, but not necessarily the same size). It may be seen as being equal in certain parts but not in all, with these being defined at the level of label, attributes, and relations in/out. This is the most useful but there must be no contradictions in the parts with some commonality.
- ▪ Exactly equal if all the three aspects are exactly the same. If this is obtained, then it is not a new concept.
- ▪ May or may not be equal if some aspects belong to one but not the other (and vice versa).
- ▪ Not equal if there are any contradictions within the attributes and values and/or relations in and out (excluding labels). If this is obtained, then it may be equal to another concept.
- − May still be equal, as the labels may be synonyms, or in different languages, and so on.
- − May still be equal, as they differ in labels but no contradictions in the attributes.
- − Not equal, as there is a contradiction in the attribute.
- − May still be equal, as they differ in labels but no contradictions in the relations in and out.
- − Not equal, as there is a contradiction in the relations in and out.
- The labels are the same or synonymous.
- No contradictions.
- Have at least one or more exactly same attributes or relations in/out.
3.5. Knowledge Hub
4. Evaluation
4.1. Approaches
- Error rate (ERR) (1) is calculated as the number of all incorrect predictions divided by the total number in the dataset. The acceptance rate should be less than 0.10 indicating a minimal error.
- Accuracy (ACY) (2) is calculated as the number of all of the correct predictions divided by the total number of the dataset. The acceptance rate should be greater than 0.90 showing excellent classification.
- F1-Score (3) is a harmonic mean of precision and recall. The acceptance rate should be greater than 0.90 suggesting excellent precision and recall.
4.2. Processes
- (1)
- Concepts were properly named, and
- (2)
- Their hooking locations in the ontology were accurate.
4.3. Results
5. Discussion
6. Conclusions & Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
List of Abbreviations | |
AH | Acquisition Hub |
AI | Artificial Intelligence |
CC | Cleansing Chamber |
FEA | Facts Enrichment Approach. |
FCA | Formal Concept Analysis |
FN | False negative |
FP | False Positive |
GC | Groupware Chamber |
GO 1 | Grand Ontology 1 |
GO2 | Grand Ontology 2 |
HH | Harvesting Hub |
II | Identification Instrument |
JEOPS | Java Embedded Object Production System |
KG | Knowledge Graph |
KH | Knowledge Hub |
LCS | Longest Common Substring |
OAEI | Ontology Alignment Evaluation Initiative |
OWL | Web Ontology Language |
RDF | Resource Descriptive Framework |
SWRL | Semantic Web Rule Language |
TN | True Negative |
TO | Target Ontology |
TP | True positive |
List of Mathematical Symbols | |
C | the concept in question. |
Sj | a super-concept of C. |
IS_A, RI, Rm | relations. |
atri atrk | attributes with values. |
vi vk | attributes values. |
Al Bm | related concepts in and out of C. |
Appendix A
Grand Ontology 1 | Grand Ontology 2 | ||
---|---|---|---|
Sentences | Concepts | Sentences | Concepts |
1 | api | 1 | gitflow |
2 | code(new) | 2 | conversation |
3 | vertical scale | 3 | milestone |
4 | cookie | 4 | - |
5 | feature(new) | 5 | bug |
6 | testlog | 6 | impact |
7 | signoff | 7 | journey |
8 | rollback | 8 | misuse |
9 | script | 9 | toggle |
10 | production | 10 | debt |
11 | go-no-go | - | - |
12 | attend | - | - |
13 | codebase | - | - |
14 | build | - | - |
15 | codefreeze | - | - |
16 | timelines | - | - |
17 | sprint | - | - |
18 | backlog | - | - |
19 | tag | - | - |
20 | log | - | - |
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Authors/Year | Source of New Knowledge | Research Focus | Sampled Ontology | Techniques Used | Evaluation |
---|---|---|---|---|---|
Ngom et al. [42] | Existing ontology | Adding a concept from one ontology to another ontology | WordNet | Similarity measure | Correlation among ontologies |
Yin et al. [43] | Existing ontology | Merging two or more existing ontologies | WordNet | Classification with Word and CONtext Similarity | LCS |
Xue et al. [44] | Existing ontology | Matching two or more existing ontologies | WordNet | Similarity measure | Recall, Precision, F-measure |
Oliveira and Pesquita [45] | Existing ontology | Matching two or more existing ontologies | Biomedical ontologies | Similarity measure | Recall, Precision, F-measure |
Priya and Kumar [46] | Existing ontology | Mapping two or more existing ontologies | Transportation and vehicles ontologies | Granular computing | Use case |
Liu et al. [47] | Existing ontology | Mapping two or more existing ontologies | OAEI dataset | Mapping similarity | Accuracy |
Ernadote [48] | Existing ontology | Aligning two or more existing ontologies | Metamodel-based ontologies | NA | NA |
Maree and Belkhatir [49] | Existing ontology | Combining two or more existing ontologies | EMET, AGROVOC, and NAL | OAEI | Recall, Precision |
Zhen-Xing and Xing-Yan [50] | Existing ontology | Matching two or more existing ontologies | WordNet | Similarity measure | FCA |
Huang and Bian [51] | Existing ontology | Matching two or more existing ontologies | Tourism info and tourists ontologies | FCA-based approaches | FCA and Bayesian analysis |
This research | Sentences in groupware | Recognizing new concepts from sentences and inserting/hooking into an existing ontology | Software knowledge ontology | FEA | Error rate, Accuracy, F1 score |
Discipline Concept | Waterfall Role | Agile Role | WAGILE Role |
---|---|---|---|
Project-management | PM | PO | PM |
Team-focus | PM | SM | SM |
Product-ownership | PM | PO | PO |
Requirement-analysis | BSA | DT | BSA |
Design | PM & UX | DT | UX |
Implementation | Dev | DT | Dev |
Test/QA | QA | DT | QA |
Deployment | RM | DT | RM |
Maintenance | ASA | DT | ASA |
Matrix Variables | Grand Ontology 1 | Grand Ontology 2 | Total | ||
---|---|---|---|---|---|
Concept Naming | Hooking Location | Concept Naming | Hooking Location | ||
True-positive | 457 | 456 | 213 | 214 | 1340 |
False-positive | 32 | 34 | 15 | 14 | 95 |
True-negative | 232 | 231 | 109 | 106 | 678 |
False-negative | 3 | 4 | 2 | 4 | 13 |
Total | 724 | 725 | 339 | 338 | 2126 |
Parameters | Grand Ontology 1 | Grand Ontology 2 | ||||
---|---|---|---|---|---|---|
ER | ACY | F1 | ER | ACY | F1 | |
Concept Naming | 0.048 | 0.952 | 0.963 | 0.050 | 0.949 | 0.984 |
Hooking locations | 0.052 | 0.948 | 0.960 | 0.053 | 0.947 | 0.957 |
Mean | 0.05 | 0.95 | 0.96 | 0.05 | 0.95 | 0.97 |
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Uwasomba, C.F.; Lee, Y.; Yusoff, Z.; Chin, T.M. Ontology-Based Methodology for Knowledge Acquisition from Groupware. Appl. Sci. 2022, 12, 1448. https://doi.org/10.3390/app12031448
Uwasomba CF, Lee Y, Yusoff Z, Chin TM. Ontology-Based Methodology for Knowledge Acquisition from Groupware. Applied Sciences. 2022; 12(3):1448. https://doi.org/10.3390/app12031448
Chicago/Turabian StyleUwasomba, Chukwudi Festus, Yunli Lee, Zaharin Yusoff, and Teck Min Chin. 2022. "Ontology-Based Methodology for Knowledge Acquisition from Groupware" Applied Sciences 12, no. 3: 1448. https://doi.org/10.3390/app12031448
APA StyleUwasomba, C. F., Lee, Y., Yusoff, Z., & Chin, T. M. (2022). Ontology-Based Methodology for Knowledge Acquisition from Groupware. Applied Sciences, 12(3), 1448. https://doi.org/10.3390/app12031448