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1 INFO310 0 Advanced Topics in Model-Based Information Systems Kandidat 113 Oppgaver Oppgavetype Vurdering Status Introduction Dokument Automatisk poengsum Levert Plagiarism and Declaration Dokument Automatisk poengsum Levert 1 Essay Filopplasting Manuell poengsum Levert INFO310 0 Advanced Topics in Model-Based Information Systems Emnekode INFO310 Vurderingsform INFO310 Starttidspunkt: :00 Sluttidspunkt: :00 Sensurfrist Ikke satt PDF opprettet :37 Opprettet av Andreas Lothe Opdahl Antall sider 12 Oppgaver inkludert Nei Skriv ut automatisk rettede Nei 1

2 Seksjon 1 1 OPPGAVE Essay Upload your file here. Maximum one file. BESVARELSE Filopplasting Filnavn Filtype Filstørrelse _cand _ pdf KB Opplastingstid :29:16 Neste side Besvarelse vedlagt INFO310 0 Advanced Topics in Model-Based Information Systems Page 2 av 12

3 Candidate number: 113 Page: 1/10 Ontology Matching in the Semantic Web, Progress and a Futuristic Approach I. Introduction II. Questions III. Discussion IV. Conclusion I. Introduction Ontologies are a central part of the semantic web. They are used for structuring data on the web, which can then be retrieved using unique IRI s (Internationalized Resource Identifier) within one ontology. One powerful tool for creating such ontologies are Apache Jena, where an ontology is created using vocabularies such as OWL, RDF and RDFS. Apache Jena is a library for Java, making it easy to create an ontology with a language that many are familiar with. Ontologies are a way of structuring knowledge, while also applying some inference rules that are meant to deduce information that is not explicitly stated. In semantic web it is used as a way to give a semantic meaning to the data published on the web. Ontology matching is a field where researchers try to match, or align, ontologies together to form an alignment between them. It finds correspondences between related entities of ontologies. What this means is that it takes two ontologies, O1 and O2, and tries to create an alignment A between O1 and O2. This can be used for ontology merging, which is the process of merging the data from O1 and O2 into an ontology containing all the data from both O1 and O2. [1]. Figure 1 shows a simple alignment done between two simple ontologies, o and o. Here, certain correspondences have been identified. Human has been set to be equivalent to Person. The same goes for the and has relations. Regular author from o has been set as a subsumption of Author, meaning that every regular author is also an author. Creating this alignment, showing the correspondences between relations and classes from two ontologies provides the possibility of merging the data. As mentioned earlier, IRI s are unique within one ontology. Merging two ontologies to one then gives an entity that was previously mentioned in two ontologies with two different IRI s, now only one IRI in one ontology. This can be useful when querying the dataset, as you remove ambiguity. It can also prove useful if one of the ontologies is queried or used in combination with other datasets, like for example DBPedia. Say that you have an ontology o1 that contains a lot of data on fish created by a university somewhere, and they have linked their entities to the equivalent DBPedia entity. If you then match and merge a different ontology on fish, o2, that does not have any links to DBPedia to o1 then you can merge o2 into o1 to both enrich o1 with more data, but also gain the possibility of querying data from o2 with DBPedia. INFO310 0 Advanced Topics in Model-Based Information Systems Page 3 av 12

4 Candidate number: 113 Page: 2/10 Fig. 1: Two simple ontologies. Correspondences between them are shown with blue arrows that are marked with the relation. Classes are shown as rectangles with rounded corners. [13] Ontology matching is a broad field with a lot of different techniques, tools and research. Entering the field for the first time gave me some initial questions. Merging ontologies in such a way that all data related to one domain is contained within one ontology is a goal that would reduce ambiguity and would result in what is called a best practice within the semantic web [2]. If every biology institute across the world could represent their data in one ontology, then that would result in a comprehensive and unambiguous dataset that is easy to query. Whether this goal ever will be achievable or not is something I will try to explore later in the essay, and it is certainly interesting to keep in mind when looking at ontology matching. I had three initial questions when I started reading about ontology matching, and I will try to answer them in this essay. The questions are as follows: II. Questions Q1: What are the main approaches to ontology matching, and how successful are they? Q2: What does ontology matching contribute to the semantic web? Q3: Is it possible that the field will advance to the point where it is no longer needed? I will refer to these questions as Q1-Q3 for the rest of the essay. These questions arose when I started to read on the topic, and I will try to provide some insight as to why I picked them. Q1 is the first question, as I will try to explore how ontology matching works. The tools that are involved, and how successful they are. I will be looking at results from the OAEI workshops that are hosted annually and what techniques are used within ontology matching. Q2 is a follow-up question trying to answer what ontology matching does for the semantic web. Data structured in ontologies are seen other places as well, not just on the semantic web. It is therefore INFO310 0 Advanced Topics in Model-Based Information Systems Page 4 av 12

5 Candidate number: 113 Page: 3/10 not given that ontology matching should apply to the semantic web, even though ontologies are a central part of the semantic web. I will try to explore what ontology matching can, or are, contributing to the semantic web. Q3 is a more abstract and futuristic question where I will look at the possibilities of matching every ontology created for a domain into one. If such a possibility could exist, would you then need ontology matching, if every domain already has a perfect ontology available? III. Discussion Q1 What are the main approaches to ontology matching, and how successful are they? SAMBO is one of many matcher and merging tools available. It is used for the biomedical domain, and uses multiple types of matchers in the system. They can be used independently or in combination with others. [7] The ones used are: - Terminological matcher: A terminological matcher uses linguistic properties on the textual descriptions of the concepts and relations, such as the names and synonyms. - Structural matcher: A structural matcher uses an iterative approach based on the is-a and part-of hierarchy of the ontologies. It looks at the structure of the ontology, and the intuition is that if two concepts or relations are in similar positions to already matched concepts in the two ontologies, then those are likely to be similar too. - Use of domain knowledge: Because SAMBO is a matcher over the biomedical domain, it can make use knowledge already gathered from the biomedical domain to contribute to the matching process. For SAMBO they utilize the Metathesaurus in UMLS. - Learning matcher: This learning matcher makes use of life science literature related to the concepts in the ontologies. The algorithm that is used in the matcher is the naive Bayes algorithm. These are the matching techniques used in SAMBO, and variations of these are the ones you tend to see in other ontology matching tools as well [13]. The output of SAMBO is a 1:1 alignment [1]. This means that alignment between concepts and relations give an output of 1:1. Taking two ontologies o and o, one entity from o will be mapped to only one entity from o. This is the simplest form of an ontology matcher, and other matching tools operate with an 1:n alignment, or even n:m. This can be dependent on what the domain requires, or the complexity of the matching techniques implemented in the matching tool. Strategies are also looked at from a more abstract perspective, either from a top-down perspective, or bottom-up. A top-down approach would look at element-level and structure level.the element level ignores the structure of the ontology, looking at elements in isolation. The structure level find correspondences by looking at how the entities fit in the structure of the ontology. A bottom-up approach starts at the context-based and content-based levels. The content-based look at the internal information coming from the ontologies. The context-based are looking at external information that may come from relations between the ontologies, or external resources [8]. Since 2004, the Ontology Alignment Evaluation Initiative (OAEI) have hosted workshops annually (twice in 2004) [5]. This is a workshop looking to evaluate certain techniques and tools for ontology matching. Different test cases are given to the participants, which then use their own ontology matching tools on the datasets, and measure the results. Three of a total of six tracks used in 2013, which consisted of one or more test cases, were: [6] INFO310 0 Advanced Topics in Model-Based Information Systems Page 5 av 12

6 Candidate number: 113 Page: 4/10 - The expressive ontology track: Real world ontologies using OWL modelling. - Interactive matching: Comparing different interactive matching tools which require user interaction. - Multilingual: Consisting of 7 Conference test case ontologies, translated into 8 languages (Chinese, Czech, Dutch, French, German, Portuguese, Russian and Spanish). At the OAEI workshop they use different techniques to measure the quality of an ontology matcher. Below are two tables from OAEI 2012, and 2014 respectively. Here the matching tools are rated on their precision, recall and F-measure [9, 10]. Each table is divided in two sections discerning matchers using specific multilingual strategies, and non specific matchers. Marked with a grey background in table 2 for the multilingual specific, and separated by Multilingual and Non specific in table 1. Although the progress of ontology matchers have slowed down somewhat the last years, the field is still making measurable improvement. [1] It can be seen from table 1 and 2 that the precision and f-measures of multilingual-specific matchers have increased somewhat from 2012 to Precision in the different ontologies also shows an increase from 2012 to 2014, mainly for the multilingual-specific matchers, where the best precision achieved in 2012 were.61 and the best in 2014 were 0.8. The increase is also true for f-measure and recall within different ontologies. Table 1: Aggregated results per matcher (average) of the multilingual matching on the MultiFarm dataset. Type (i) is different ontologies, type (ii) is the same ontology. Runtime is measured in minutes. From OAEI workshop [9] INFO310 0 Advanced Topics in Model-Based Information Systems Page 6 av 12

7 Candidate number: 113 Page: 5/10 Table 2: Aggregated results per matcher (average) of the multilingual matching on the MultiFarm dataset. Type (i) is different ontologies, type (ii) is the same ontology. Time is measured in minutes. From OAEI workshop [10] Table 3 and 4 show the results of the different matchers participating in the expressive ontology track which consists of real world ontologies using OWL. The ontologies used in this case are anatomy ontologies over humans and mice. Table 3 and 4 show the results from 2008 and 2014, respectively. The highest f-measure achieved in 2008 was 0.852, while the highest f-measure achieved in 2014 was The runtime of the best matchers from 2008 to 2014 have also decreased significantly, showing an improvement on both matching and efficiency of the algorithms. SAMBO achieved the highest f-measure in 2008 with using 12 hours. Comparing that to AML that achieved an f-measure of within 28 minutes in It is also worth noting that the highest f-measure for the anatomy test case achieved in 2012 was 0.923, showing a minor increase from 2012 to 2014, to [10]. Table 3: Results from the anatomy test case, ordered by f-measure. Results are from the OAEI workshop 2008 [11] Table 4: The size refers to the number of correspondences in the generated alignment. The table is ordered by f-measure. Runtime is measured in minutes. From OAEI workshop [10] Table 5 and 6 show the results of the interactive ontology matching from 2013 and 2014 respectively. The results are presented by precision, recall and f-measure, as well as the INFO310 0 Advanced Topics in Model-Based Information Systems Page 7 av 12

8 Candidate number: 113 Page: 6/10 interactions done with expert users. The f-measures achieved here are not as high as the ones found in the anatomy test case. This could be explained by the fact that the interactive track was first added in 2013, and also that one year is not sufficient time to look for a large increase in precision or recall. Regardless, the highest f-measure using interaction found in 2013 was 0.73, while the highest in 2014 with interaction was There is progress using interactive matchers as well, and seeing as this track was first introduced in 2013, it can be interesting to see how the results will change in the following years. Table 5: Results of the interactive matching test case. The table reports both the results with and without interaction, in order to analyze the improvement that was gained by adding interactive features. Improvements of the interactive variants over the non-interactive variants are shown in bold. Statistically significant differences are marked with 5 (p < 0.05) and 1 (p < 0.01). Furthermore, we report the average number of interactions, showing both the positive and negative examples presented to the oracle Table 6: Results on the interactive matching task. The numbers in parantheses denote the results achieved without interaction. Significant differences between the interactive and non-interactive results are marked with * (p < 0.05) and ** (p < 0.01). [10] Q2 What does ontology matching contribute to the semantic web? The semantic web is built on many different ontologies. Data is structured in such a way that reasoning over the data is possible, utilizing tools such as OWL and RDFS that are based on logic to deduce information. Defining your own ontology can be a difficult task. There exists many types of restrictions and properties that can be defined over relations and entities, and knowing when and where to use these is an important part of building a good ontology. It can therefore be convenient to reuse an existing ontology when structuring data. There exist some well-known ontologies, like Friend Of A Friend (FOAF) and Dublin Core (DC), that are considered good ontologies by W3 [12]. They can be used on the domains they are defined over. However, on the semantic web, you are not restricted to using these ontologies. You can still create your own vocabulary describing social relations, instead of using FOAF. This creates the possibility of some ambiguity, as your ontology might cover some of the same entities and/or relations as FOAF, but they are identified by a different IRI. FOAF would be more widely used, and probably more comprehensive, while yours might be small and only cover your specific needs. If you created this ontology, unaware of FOAF, INFO310 0 Advanced Topics in Model-Based Information Systems Page 8 av 12

9 Candidate number: 113 Page: 7/10 ontology matching could help you map your ontology to FOAF and then merge your data. This results in easier queries and it is no longer any point for others to learn the URI s or any other part of your ontology, you only need the knowledge of FOAF to access the data. There are however some issues with matching small ontologies to big ones. The granularity of the two ontologies to be matched play an important role. As mentioned, the quality of the ontologies play a big part in ontology matching. Especially for the matchers that rely heavily on the structural matching technique. If a structural matcher were to try to match FOAF to a small social relations ontology, it could prove very difficult. Matching a detailed ontology to an ontology with few classes, relations and properties on the structure level is virtually impossible due to the fact that the matcher would have to go down to the granularity level of the less detailed ontology [14]. In these cases other matching techniques would have to be used to form a better alignment, such as a matcher with background knowledge, interactive matcher or a terminological matcher. An interactive matcher could prove useful for matching small ontologies to bigger ones, as there are less classes and relations to align. The process is by definition smaller than it would be if using an interactive matcher for two big ontologies. A terminological matcher could run into some problems with the small ontology, on the assumption that a small ontology most probably has less valuable comments and labels for classes and relations to match strings on. A matcher with background knowledge would also run into problems regarding the granularity of the small ontology. When there are a smaller amount of relations or entities, then there is a smaller amount of data to generate background knowledge from. This in turn leads to less background knowledge, which reduces the effectiveness of the matching technique. Q3 Is it possible that ontology matching will advance to the point where it is no longer needed? There are a vast amount of ontologies used for structuring data, and in Metathesaurus in the Unified Medical Language System (UMLS) there exists over 100 alone, which are only for the biomedical and health-related domains alone. Some of these vocabularies overlap when it comes to concepts and relations [7]. Many matchers are used specifically for one domain [1]. For instance, to utilize background knowledge, a matcher needs to know what background knowledge to use for the domain it s making an alignment for. Re-using ontologies is one of what s called best practices within the semantic web [2]. Two of the reasons being that it is generally accepted to be a resource-intensive process to create a new ontology, and it increases interoperability and reduces redundancies between ontologies [2, 3]. It would therefore appear to be a best practice to have one ontology for each possible domain and have everyone re-using the respective ontology for that particular domain. Best practice #18 in [2] says Existing reference vocabularies should be re-used where possible. This opens up two interesting questions; is there a finite number of domains, such that you have a bijective function f( x) = o where x is a domain, and o is the corresponding ontology? If a function like that exists, would that mean the end of ontology matching? However, the where possible part of the quote is interesting to explore, as the article does not look at scenarios where it would be impossible to re-use an ontology. If there are scenarios where it would be impossible to re-use an ontology, then that would mean that you could not align your ontology with an already exisiting one. If it is impossible to use an exisiting ontology, then the purpose of the new ontology must be unique in some way or another. INFO310 0 Advanced Topics in Model-Based Information Systems Page 9 av 12

10 Candidate number: 113 Page: 8/10 Before the Internet of Things (IoT) existed, no one could know the vast amount of sensors and devices that would eventually make up the IoT. For the past few years, scope and research of the internet of things (IoT) have increased and many applications are using semantic web technologies in IoT research. [4] Seeing as this is a field that has expanded for the past years, it would be impossible to re-use an ontology when research first started, because no ontology existed for the domain. If you wanted to structure the data from your sensors and devices in an ontology, you would necessarily have to create an ontology that would correspond to how you would like the data to be structured, because no other ontology would exist for your purpose. For ontology matching, this renders one of the matching techniques redundant. Background knowledge would initially not exist for the domain, and could therefore not be used at all. For an interactive matcher, this would also prove difficult as the amount of experts are considerably smaller than it would be for a field that have been established for years or decades. IV. Conclusion Ontology matching is still making measurable progress according to the results from the OAEI workshops. From the results of the multilingual test case in 2014 (table 2), it is apparent that matchers using domain-specific techniques produce better results than matchers that are non-specific. There are other interesting challenges facing ontology matching to improve the quality of the alignments made. One I find particularly interesting are user involvement, and interactive matching techniques. Utilizing user involvement would reduce the amount of assumptions a matcher has to consider. Challenges facing a user-involvement approach are many, and one in particular to look at is how users would be involved in the matching process. Community-driven matching can be problematic due different levels of expertise from the community. An approach that might be possible is combining the interaction between a user and the matcher with machine learning. It could increase the quality of the matching process, as well as eventually reducing the number of interactions needed as the system learns to emulate an expert user. The techniques used in ontology matching today are promising, and they are continuously making progress as shown with the data from [1], and the tables presented in this essay. It seems that the role of ontology matching within the semantic web is yet to be fully established. The OAEI workshops does not use any vocabulary or datasets found on the web, they are instead using established ontologies and datasets mainly in the biomedical domain. One reason could be that these ontologies have been in development for a long time, and are therefore better test cases for ontology matching. The application of ontology matching in the semantic web may seem obvious due to the fact that the semantic web is built on ontologies, but I feel that it is yet to be explored to its full potential. Seeing that a lot of the matchers are producing better alignments as the field continues to grow might make developers on the semantic web consider ontology matching as a tool to align their ontology with more established ones that are defined over the same domain. As of now however, it seems that ontology matching is a field related to the semantic web, more than a field helping the semantic web becoming a better structure of data. An interesting application of ontology matching outside of the semantic web and data structures is that of the real world applications. When people converse, and talk about concepts and ideas, we are portraying our knowledge of those concepts to the other person. However, the other person might not have the same knowledge of the concept, but are still able to understand what you are talking about. We are matching our concepts together, so that both understand the conversation INFO310 0 Advanced Topics in Model-Based Information Systems Page 10 av 12

11 Candidate number: 113 Page: 9/10 [15]. Whether we can use this real world version of a matching technique to better understand and develop ontology matching, or use ontology matching to better understand this real world version of matching is beyond the scope of this essay. Regardless, it is interesting, and could possibly contribute to ontology matching. Either going from real world to ontology matching, or ontology matching to real world. Whether the field itself can advance to the point where it is no longer needed is not something that can be answered now. It is an interesting question, but seeing new domains establish as technology advances, it seems improbable to have a matcher generic enough that it could deal with domains that are non-existent at the time of creating the matcher. Many matchers are using domain-specific techniques to match, like background knowledge. This matching technique would be redundant whenever a new field emerges, as the background knowledge would be lacking compared to established fields like the biomedical field, which is where some of the best matchers are found. Terminological matchers, and structural matchers could still do some good work on new fields. However, they are both reliant on how good the ontologies are built from the human perspective. A terminological matcher will not work properly unless the ontologies are thoroughly commented and has appropriate labels. The structural matcher will not work unless the structure of the ontologies are good enough to reason over. This means that the ontologies has to be made by people familiar with ontologies and their structure, not just in the new domain, but also ontologies in general. In short, the field will not advance to the point where it s no longer needed, from the literature I have found. At least not in the foreseeable future. [1] Shvaiko, Pavel, and Jérôme Euzenat. "Ontology matching: state of the art and future challenges." IEEE Transactions on knowledge and data engineering 25.1 (2013): [2] [3] Bontas, E. Paslaru, Malgorzata Mochol, and Robert Tolksdorf. "Case studies on ontology reuse." Proceedings of the IKNOW05 International Conference on Knowledge Management. Vol [4] Uckelmann, Dieter, Mark Harrison, and Florian Michahelles. "An architectural approach towards the future internet of things." Architecting the internet of things. Springer Berlin Heidelberg, [5] [6] Grau, Bernardo Cuenca, et al. "Results of the ontology alignment evaluation initiative 2013." Proceedings of the 8th International Conference on Ontology Matching-Volume CEUR-WS. org, [7] Lambrix, Patrick, and He Tan. "SAMBO a system for aligning and merging biomedical ontologies." Web Semantics: Science, Services and Agents on the World Wide Web 4.3 (2006): [8] Otero-Cerdeira, Lorena, Francisco J. Rodríguez-Martínez, and Alma Gómez-Rodríguez. "Ontology matching: A literature review." Expert Systems with Applications 42.2 (2015): [9] Aguirre, José Luis, et al. "Results of the ontology alignment evaluation initiative 2012." Proceedings of the 7th International Conference on Ontology Matching-Volume 946. CEUR-WS. org, [10] Dragisic, Zlatan, et al. "Results of the ontology alignment evaluation initiative 2014." Proceedings of the 9th International Conference on Ontology Matching-Volume CEUR-WS. org, INFO310 0 Advanced Topics in Model-Based Information Systems Page 11 av 12

12 Candidate number: 113 Page: 10/10 [11] Caracciolo, Caterina, et al. "Results of the ontology alignment evaluation initiative 2008." Proceedings of the 3rd International Conference on Ontology Matching-Volume 431. CEUR-WS. org, [12] [13] Euzenat, Jérôme, et al. "Ontology alignment evaluation initiative: six years of experience." Journal on data semantics XV. Springer Berlin Heidelberg, [14] Gruber, Tom. "Collective knowledge systems: Where the social web meets the semantic web." Web semantics: science, services and agents on the World Wide Web 6.1 (2008): [15] Goldstone, Robert L., and Brian J. Rogosky. "Using relations within conceptual systems to translate across conceptual systems." Cognition 84.3 (2002): INFO310 0 Advanced Topics in Model-Based Information Systems Page 12 av 12

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