DSSim
DSSim[1][2] is an ontology mapping system, that has been conceived to achieve a certain level of the envisioned machine intelligence on the Semantic Web. The main driving factors behind its development was to provide an alternative to the existing heuristics or machine learning based approaches with a multi-agent approach that makes use of uncertain reasoning. The system provides a possible approach to establish machine understanding over Semantic Web data through multi-agent beliefs and conflict resolution.
Theoretical background
[edit]The DSSim framework for ontology mapping was introduced in 2005[3] by Miklos Nagy and Maria Vargas-Vera at the Open University (OU). DSSim addresses three challenges of the Semantic Web:
- Uncertainty: Ontology mapping agents adopt the Dempster–Shafer theory for creating beliefs over mapping hypothesis. Based on evidences of similarity the mapping agents combine their beliefs in order to provide a coherent view on the mappings. The system is based on a theoretical mental model for software agents to represent beliefs over similarities of different terms in different ontologies. Through these beliefs that are derived using different similarity measure and background knowledge, each agent can establish certain understanding of the terms and their context.
- Inconsistency: Conflicts in belief are resolved using fuzzy voting mechanism. Processing data on the Semantic Web produces scenarios where the different agents has conflicting beliefs over a particular solution. In these situations the agents need to resolve their conflicts in order to choose the best possible solution e.g. in our case mapping. Mapping agents use fuzzy voting to determine the best decision for agent society but in case voters make mistakes in their judgments, then the majority alternative (if it exists) is statistically most likely to be the best choice. The application of voting for mapping agents is a possible way to make systems more intelligent i.e. mimic the decision making how humans reach the decision on a problematic issue.
- Vastness: Genetic algorithms based optimisations techniques are used in order to provide a reasonable time frame for belief combination using large ontologies. One of the main disadvantages of using Dempster-Shafer theory for uncertain reasoning is the computational complexity of the belief combination. DSSim resolves the problem by using genetic algorithm for creating the graphical structure that is used to compute the belief combination efficiently in the ontology mapping context.
DSSim uses novel 3D visualisation techniques of both mapping and reasoning results. The main purpose of the reasoning storage and visualisation is to retain the reasoning states, in order to visualise it later to the end users. The main objective is to show to the end users why the system has selected a mapping candidate from two different ontologies.
Evaluation of the system
[edit]The evaluation of the system was carried out in the Ontology Alignment Evaluation Initiative (OAEI). DSSim has participated in 2006,[4] 2007,[5] 2008[6] and 2009[7] achieving gradually improved results. The following sections present the result of two tracks out of 8 from the OAEI 2008.
| Year | Available tracks | Participated tracks | Number of systems participated | DSSim track coverage |
|---|---|---|---|---|
| 2006 | 5 | 1 | 10 | 20% |
| 2007 | 7 | 6 | 17 | 85% |
| 2008 | 8 | 8 | 13 | 100% |
| 2009 | 8 | 7 | 16 | 87% |
Library track at OAEI 2008
[edit]According to the original task definition[8] provided by the organizers of the OAEI 2008, the library track involved the alignment of two Dutch thesauri. These Dutch thesauri are used to index books from two collections held by the National Library of the Netherlands (KB). KB maintains two big collections: the Deposit Collection, containing all the Dutch printed publications (one million items), and the Scientific Collection, with about 1.4 million books mainly about the history, language and culture of the Netherlands. Each collection is described according to its own indexing system and conceptual vocabulary. On the one hand, the Scientific Collection was described using the GTT, a huge vocabulary containing 35,000 general concepts ranging from Wolkenkrabbers (Sky-scrapers) to Verzorging (Care). On the other hand, the books contained in the Deposit Collection are mainly indexed against the Brinkman thesaurus, containing a large set of headings (more than 5,000) that were expected to serve as global subjects of books. For each concept, the thesauri provided the usual lexical and semantic information: preferred labels, synonyms and notes, broader and related concepts, etc. The language of both thesauri was Dutch, but a quite substantial part of Brinkman concepts (around 60%) come with English labels. The library track was difficult partly because of its relative large size and because of its multilingual representation. Nevertheless in the library track DSSim has performed the best out of the 3 participating systems. However these ontologies contain related and broader terms therefore the mapping can be carried out without consulting multi-lingual background knowledge.
Directory track at OAEI 2008
[edit]As stated by the original task definition[9] provided by the organizers of the OAEI 2008, this track is designed to evaluate mapping quality in a real world taxonomy integration scenario. The main objective is to measure whether ontology alignment tools can effectively be applied to integration of "shallow ontologies". The evaluation dataset was extracted from Google, Yahoo! and Looksmart web directories. The way these ontology pairs were created was to rely on a reference interpretation for nodes, constructed by looking at their use. The assumption was that the semantics of nodes could have been derived from their pragmatics, namely from analysing, which documents were classified under which nodes. The basic idea was therefore to compute the relationship hypotheses based on the co-occurrence of documents. The specific characteristics of the dataset were:
- More than 4500 of node matching tasks, where each node matching task is composed from the paths to root of the nodes in the web directories.
- Expert mappings for all the matching tasks.
- Simple relationships. Basically web directories contain only one type of relationship the so called "classification relation".
- Vague terminology and modeling principles: The matching tasks incorporate the typical "real world" modeling and terminological errors.
| System | Prec. | Rec. | F-measure. |
|---|---|---|---|
| DSSim | 0.60 | 0.41 | 0.49 |
| CIDER | 0.60 | 0.38 | 0.47 |
| Lily | 0.59 | 0.37 | 0.46 |
| TaxoMap[10] | 0.59 | 0.39 | 0.43 |
| MapPSO[11] | 0.57 | 0.31 | 0.40 |
| RiMOM[12] | 0.55 | 0.17 | 0.26 |
| ASMOV | 0.64 | 0.12 | 0.20 |
In the directory track only 6 systems have participated in 2008. In terms of F-value DSSim has performed the best however the difference was marginal compared to the CIDER or Lily systems.
References
[edit]- ^ Miklos Nagy and Maria Vargas-Vera. Towards an Automatic Semantic Data Integration: Multi-Agent Framework Approach. Semantic Web. Gang Wu (ed), Chapter 7, pp. 107-134; In-Tech Education and Publishing KG; 2010, ISBN 978-953-7619-54-1.
- ^ Nagy, Miklos; Vargas-Vera, Maria (2011). "Multiagent Ontology Mapping Framework for the Semantic Web". IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans. 41 (4). Institute of Electrical and Electronics Engineers (IEEE): 693–704. doi:10.1109/tsmca.2011.2132704. ISSN 1083-4427. S2CID 206792175.
- ^ Miklos Nagy, Maria Vargas-Vera and Enrico Motta. Multi-agent Ontology Mapping Framework in the AQUA Question Answering System. the Fourth International Mexican Conference on Artificial Intelligence (MICAI-2005), Lecture Notes in Artificial Intelligence LNAI 3789, Gelbukh, A de Albornoz and H. Terashima (Eds), pp. 70-79, Monterrey Mexico, November 14–18, 2005.
- ^ Jérôme Euzenat, Malgorzata Mochol, Pavel Shvaiko, Heiner Stuckenschmidt, Ondrej Sváb, Vojtech Svátek, Willem Robert van Hage, Mikalai Yatskevich: Results of the Ontology Alignment Evaluation Initiative 2006. In Proceedings of the 1st International Workshop on Ontology Matching, ISWC 2006
- ^ Jérôme Euzenat, Antoine Isaac, Christian Meilicke, Pavel Shvaiko, Heiner Stuckenschmidt, Ondřej Šváb, Vojtěch Svátek, Willem Robert van Hage, Mikalai Yatskevich (2007). "Results of the Ontology Alignment Evaluation Initiative 2007" (PDF). Proceedings of the 2nd International Workshop on Ontology Matching, ISWC 2007.
{{cite journal}}: CS1 maint: multiple names: authors list (link) - ^ Caterina Caracciolo, Jérôme Euzenat, Laura Hollink, Ryutaro Ichise, Antoine Isaac, Véronique Malaisé, Christian Meilicke, Juan Pane, Pavel Shvaiko, Heiner Stuckenschmidt, Ondřej Šváb-Zamazal, and Vojtěch Svátek: Results of the Ontology Alignment Evaluation Initiative 2008. In Proceedings of the 3rd International Workshop on Ontology Matching, ISWC 2008
- ^ Jérôme Euzenat, Alfio Ferrara, Laura Hollink, Antoine Isaac, Cliff Joslyn, Véronique Malaisé, Christian Meilicke, Andriy Nikolov, Juan Pane, Marta Sabou, François Scharffe, Pavel Shvaiko, Vassilis Spiliopoulos, Heiner Stuckenschmidt, Ondřej Šváb-Zamazal, Vojtěch Svátek, Cássia Trojahn dos Santos, George Vouros and Shenghui Wang: Results of the Ontology Alignment Evaluation Initiative 2007. In Proceedings of the 4th International Workshop on Ontology Matching, ISWC 2009
- ^ Ontology Alignment Evaluation Initiative::Library Track
- ^ Ontology Alignment Evaluation Initiative::Track name
- ^ http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-431/oaei08_paper12.pdf [bare URL PDF]
- ^ "MapPSO". 30 April 2013.
- ^ Juanzi Li; Jie Tang; Yi Li; Qiong Luo (2019-04-16). "RiMOM: A Dynamic Multistrategy Ontology Alignment Framework". IEEE Transactions on Knowledge and Data Engineering. 21 (8): 1218–1232. CiteSeerX 10.1.1.649.4619. doi:10.1109/TKDE.2008.202. S2CID 4332951.