Ontology mapping is a crucial task for the facil-itation of information exchange and data integra... more Ontology mapping is a crucial task for the facil-itation of information exchange and data integration. A mapping system can use a variety of similarity measures to determine concept correspondences. This paper proposes the integration of word-sense disambiguation techniques into lexical similarity measures. We propose a disambiguation methodology which entails the creation of virtual documents from concept and sense definitions, including their neigh-bourhoods. The specific terms are weighted according to their origin within their respective ontology. The document simi-larities between the concept document and sense documents are used to disambiguate the concept meanings. First, we evaluate to what extent the proposed disambiguation method can improve the performance of a lexical similarity metric. We observe that the disambiguation method improves the performance of each tested lexical similarity metric. Next, we demonstrate the potential of a mapping system utilizing the proposed ...
Matching ontologies is a crucial process when facilitating system interoperability and informatio... more Matching ontologies is a crucial process when facilitating system interoperability and information exchange. A reoccurring problem in this process is that names can be ambiguous, yielding uncertainty to whether entities of two heterogeneous ontologies are actually related. Linguistic ontologies provide a clear structure of meanings, rather than names, allowing the quantification of the relatedness of any two given meanings. We propose an approach for the automatic allocation of correct meanings within a linguistic ontology through the use of virtual documents and information retrieval techniques. The benefits of this approach are tested and established using a data set from the Ontology Alignment Evaluation Initiative (OAEI) competition, while further improvements are revealed using a benchmark data set from the same competition. 1
Abstract. This paper summarizes the results of the participation of MaasMatch in the Ontology Ali... more Abstract. This paper summarizes the results of the participation of MaasMatch in the Ontology Alignment Evaluation Initiative (OAEI) of 2012. We provide a brief description of the techniques that have been applied, with the emphasis being on the utilized similarity measures and the performed improvements over the system that participated in the year 2011. Additionally, the results of the 2012 OAEI campaign will be discussed. 1 Presentation of the system 1.1 State, purpose, general statement Sharing and reusing knowledge is an important aspect in modern information sys-tems. Since multiple decades, researchers have been investigating methods that facil-itate knowledge sharing in the corporate domain, allowing for instance the integration of external data into a company’s own knowledge system. Ontologies are at the center of this research, allowing the explicit definition of a knowledge domain. With the steady development of ontology languages, such as the current OWL language [5], kn...
Abstract. This paper summarizes the results of the fourth participation of the MaasMatch system i... more Abstract. This paper summarizes the results of the fourth participation of the MaasMatch system in the Ontology Alignment Evaluation Initiative (OAEI) com-petition. We describe the performed changes to the MaasMatch system and eval-uate the effect of these changes on the different datasets. 1 Presentation of the system MaasMatch is a ontology mapping system with the initial focus of fully utilizing the information located in the concept names, labels and descriptions in order to produce a mapping between two ontologies [2,4]. This was achieved through the utilization of syntactic similarities and virtual documents, which can also be used as a disambiguation method for the improvement of lexical similarities [3,6]. 1.1 Specific techniques used The 2014 version of MaasMatch exhibits some notable changes compared to the 2013 version [5]. First, the system is now based on a de-centralized configuration system. For each presented mapping problem, the system queries its stored similarity ...
Abstract. This paper summarizes the results of the third participation of the MaasMatch system in... more Abstract. This paper summarizes the results of the third participation of the MaasMatch system in the Ontology Alignment Evaluation Initiative (OAEI) com-petition. Several additions were made to the MaasMatch system with the intent of rectifying its limitations, as observed during the previous OAEI campaign. The extent of the additions and their effect on the individual dataset will be elaborated. 1 Presentation of the system MaasMatch is a ontology mapping system with the initial focus of fully utilizing the information located in the concept names, labels and descriptions in order to produce a mapping between two ontologies. This was achieved through the utilization of syntactic similarities and virtual documents, which can also be used as a disambiguation method for the improvement of lexical similarities [3,4]. The results of the benchmark track in the OAEI 2012 competition [1] substantiated the evident conclusion that when the naming and annotation features of an ontology are n...
Facilitating information exchange is a crucial service for ontology-based knowledge systems. This... more Facilitating information exchange is a crucial service for ontology-based knowledge systems. This can be achieved by the mapping of two heterogenous ontologies. Many mapping frameworks utilize language-based knowledge resources such as WordNet. By coupling all ontology concepts to a corresponding entry in WordNet, one can quantify the lexical relatedness of any two ontology concepts. However, coupling the correct entry is a difficult task due to the ambiguous nature of names. Coupling the wrong entries hence yields similarity values that do not correctly express the relatedness of two given concepts, resulting in a poor performance of the overall mapping framework. This paper proposes an approach for the more accurate coupling of ontology concepts with their corresponding WordNet entries. The basis of the proposed approach is the creation of separate virtual documents representing the different ontology concepts and WordNet entries and coupling these according to their document simi...
Abstract. This paper summarizes the results of the first participation of MaasMatch in the Ontolo... more Abstract. This paper summarizes the results of the first participation of MaasMatch in the Ontology Alignment Evaluation Initiative (OAEI) of 2011. We provide a brief description of the techniques that have been applied, with the emphasis being on the application of virtual documents and information retrieval techniques in order effectively utilize linguistic ontologies. Also, we discuss the results achieved in the tracks provided under the SEALS modality: benchmark, conference and anatomy.
Mapping ontologies is a crucial process when facilitating system interoperability and information... more Mapping ontologies is a crucial process when facilitating system interoperability and information exchange. Ontology Mapping systems commonly utilize string metrics in the mapping process to compare concept names. String metrics can be extended using the Winkler method, which increases the similarity value of two strings if these have a common prefix. A common occurrence for two corresponding ontology concepts is that the name of the first concept is a non-prefix sub-string of the name of the second concept. The Winkler extension does not allocate a higher similarity value to these pairs of strings, however intuitively this indicates that the two names have a similar meaning. This paper proposes a generalization of the Winkler extension, such that pairs of names with large common non-prefix sub-strings receive a higher similarity value as well. The proposed metric is evaluated on a record-matching dataset and a dataset from the Ontology Alignment Evaluation Initiative. The experimen...
Real-time strategy games present an environment in which game AI is expected to behave realistica... more Real-time strategy games present an environment in which game AI is expected to behave realistically. One feature of realistic behaviour in game AI is the ability to recognise the strategy of the opponent player. This is known as opponent modeling. In this paper, we propose an approach of opponent modeling based on hierarchically structured models. The top-level of the hierarchy can classify the general play style of the opponent. The bottom-level of the hierarchy can classify specific strategies that further define the opponent’s behaviour. Experiments that test the approach are performed in the RTS game Spring. From our results we may conclude that the approach can be successfully used to classify the strategy of an opponent in the Spring game.
This paper summarizes the results of the participation of Maas Match in the Ontology Alignment Ev... more This paper summarizes the results of the participation of Maas Match in the Ontology Alignment Evaluation Initiative (OAEI) of 2012. We provide a brief description of the techniques that have been applied, with the emphasis being on the utilized similarity measures and the performed improvements over the system that participated in the year 2011. Additionally, the results of the 2012 OAEI campaign will be discussed.
Abstract—Matching ontologies which utilize significantly hetero-geneous terminologies is a challe... more Abstract—Matching ontologies which utilize significantly hetero-geneous terminologies is a challenging task for existing matching techniques. These techniques typically exploit lexical resources in order to enrich the ontologies with additional terminology such that more terminological matches can be found. However, they are limited by the availability of an appropriate lexical resource for each matching task. For this scenario, we propose a new technique exploiting partial alignments. We evaluate our technique on a dataset which is characterized by matching problems with significant terminological heterogeneities. Further, we compare our technique with the performance of matching systems utilizing lexical resources to establish whether a partial-alignment-based matcher can perform similarly to a lexical-based matcher. Lastly, we provide a performance indication of a system utilizing both partial alignments and lexical resources.
Matching ontologies is a crucial process when facilitating system interoperability and informatio... more Matching ontologies is a crucial process when facilitating system interoperability and information exchange. A reoccurring problem in this process is that names can be ambiguous, yielding uncertainty to whether entities of two heterogeneous ontologies are actually related. Linguistic ontologies provide a clear structure of meanings, rather than names, allowing the quantification of the relatedness of any two given meanings. We propose an approach for the automatic allocation of correct meanings within a linguistic ontology through the use of virtual documents and information retrieval techniques. The benefits of this approach are tested and established using a data set from the Ontology Alignment Evaluation Initiative (OAEI) competition, while further improvements are revealed using a benchmark data set from the same competition.
This paper summarizes the results of the third participation of the MaasMatch system in the Ontol... more This paper summarizes the results of the third participation of the MaasMatch system in the Ontology Alignment Evaluation Initiative (OAEI) competition. Several additions were made to the MaasMatch system with the intent of rectifying its limitations, as observed during the previous OAEI campaign. The extent of the additions and their effect on the individual dataset will be elaborated.
Modern board games present a new and challenging field when researching search techniques in the ... more Modern board games present a new and challenging field when researching search techniques in the field of Artificial Intelligence. These games differ to classic board games, such as chess, in that they can be non-deterministic, have imperfect information or more than two players. While tree-search approaches, such as alpha-beta pruning, have been quite successful in playing classic board games, by for instance defeating the then reigning world champion Gary Kasparov in Chess, these techniques are not as effective when applied to modern board games. This thesis investigates the effectiveness of Monte-Carlo Tree Search when applied to a modern board game, for which the board game Thurn and Taxis was used. This is a non-deterministic modern board game with imperfect information that can be played with more than 2 players, and is hence suitable for research. First, the state-space and game-tree complexities of this game are computed, from which the conclusion can be drawn that the two-p...
Upper Ontology Resources belonging to this group have the singular focus of creating an abstract ... more Upper Ontology Resources belonging to this group have the singular focus of creating an abstract ontology using an upper-level list of concept descriptions. Such an ontology can then serve as a base for domain specific resources. An example of such a resource is the SUMO ontology, containing approximately 2.000 abstract concept descriptions (Niles and Pease, 2001). These concepts can then be used to model more specific domains. MILO for instance is an extension of SUMO which includes many mid-level concepts (Niles and Terry, 2004). Cyc is another example of a multi-layered ontology based on an abstract upper level-ontology (Matuszek et al., 2006), of which a subset is freely available under the name OpenCyc (Sicilia et al., 2004). Multi-lingual When mapping ontologies, it can occur that some concept descriptions are formulated in a different language. In these situations mono-lingual resources are insufficiently applicable, necessitating the usage of multi-lingual resources, e.g. UW...
This paper summarizes the results of the fourth participation of the MaasMatch system in the Onto... more This paper summarizes the results of the fourth participation of the MaasMatch system in the Ontology Alignment Evaluation Initiative (OAEI) competition. We describe the performed changes to the MaasMatch system and evaluate the effect of these changes on the different datasets.
Ontology mapping is a crucial task for the facil-itation of information exchange and data integra... more Ontology mapping is a crucial task for the facil-itation of information exchange and data integration. A mapping system can use a variety of similarity measures to determine concept correspondences. This paper proposes the integration of word-sense disambiguation techniques into lexical similarity measures. We propose a disambiguation methodology which entails the creation of virtual documents from concept and sense definitions, including their neigh-bourhoods. The specific terms are weighted according to their origin within their respective ontology. The document simi-larities between the concept document and sense documents are used to disambiguate the concept meanings. First, we evaluate to what extent the proposed disambiguation method can improve the performance of a lexical similarity metric. We observe that the disambiguation method improves the performance of each tested lexical similarity metric. Next, we demonstrate the potential of a mapping system utilizing the proposed ...
Matching ontologies is a crucial process when facilitating system interoperability and informatio... more Matching ontologies is a crucial process when facilitating system interoperability and information exchange. A reoccurring problem in this process is that names can be ambiguous, yielding uncertainty to whether entities of two heterogeneous ontologies are actually related. Linguistic ontologies provide a clear structure of meanings, rather than names, allowing the quantification of the relatedness of any two given meanings. We propose an approach for the automatic allocation of correct meanings within a linguistic ontology through the use of virtual documents and information retrieval techniques. The benefits of this approach are tested and established using a data set from the Ontology Alignment Evaluation Initiative (OAEI) competition, while further improvements are revealed using a benchmark data set from the same competition. 1
Abstract. This paper summarizes the results of the participation of MaasMatch in the Ontology Ali... more Abstract. This paper summarizes the results of the participation of MaasMatch in the Ontology Alignment Evaluation Initiative (OAEI) of 2012. We provide a brief description of the techniques that have been applied, with the emphasis being on the utilized similarity measures and the performed improvements over the system that participated in the year 2011. Additionally, the results of the 2012 OAEI campaign will be discussed. 1 Presentation of the system 1.1 State, purpose, general statement Sharing and reusing knowledge is an important aspect in modern information sys-tems. Since multiple decades, researchers have been investigating methods that facil-itate knowledge sharing in the corporate domain, allowing for instance the integration of external data into a company’s own knowledge system. Ontologies are at the center of this research, allowing the explicit definition of a knowledge domain. With the steady development of ontology languages, such as the current OWL language [5], kn...
Abstract. This paper summarizes the results of the fourth participation of the MaasMatch system i... more Abstract. This paper summarizes the results of the fourth participation of the MaasMatch system in the Ontology Alignment Evaluation Initiative (OAEI) com-petition. We describe the performed changes to the MaasMatch system and eval-uate the effect of these changes on the different datasets. 1 Presentation of the system MaasMatch is a ontology mapping system with the initial focus of fully utilizing the information located in the concept names, labels and descriptions in order to produce a mapping between two ontologies [2,4]. This was achieved through the utilization of syntactic similarities and virtual documents, which can also be used as a disambiguation method for the improvement of lexical similarities [3,6]. 1.1 Specific techniques used The 2014 version of MaasMatch exhibits some notable changes compared to the 2013 version [5]. First, the system is now based on a de-centralized configuration system. For each presented mapping problem, the system queries its stored similarity ...
Abstract. This paper summarizes the results of the third participation of the MaasMatch system in... more Abstract. This paper summarizes the results of the third participation of the MaasMatch system in the Ontology Alignment Evaluation Initiative (OAEI) com-petition. Several additions were made to the MaasMatch system with the intent of rectifying its limitations, as observed during the previous OAEI campaign. The extent of the additions and their effect on the individual dataset will be elaborated. 1 Presentation of the system MaasMatch is a ontology mapping system with the initial focus of fully utilizing the information located in the concept names, labels and descriptions in order to produce a mapping between two ontologies. This was achieved through the utilization of syntactic similarities and virtual documents, which can also be used as a disambiguation method for the improvement of lexical similarities [3,4]. The results of the benchmark track in the OAEI 2012 competition [1] substantiated the evident conclusion that when the naming and annotation features of an ontology are n...
Facilitating information exchange is a crucial service for ontology-based knowledge systems. This... more Facilitating information exchange is a crucial service for ontology-based knowledge systems. This can be achieved by the mapping of two heterogenous ontologies. Many mapping frameworks utilize language-based knowledge resources such as WordNet. By coupling all ontology concepts to a corresponding entry in WordNet, one can quantify the lexical relatedness of any two ontology concepts. However, coupling the correct entry is a difficult task due to the ambiguous nature of names. Coupling the wrong entries hence yields similarity values that do not correctly express the relatedness of two given concepts, resulting in a poor performance of the overall mapping framework. This paper proposes an approach for the more accurate coupling of ontology concepts with their corresponding WordNet entries. The basis of the proposed approach is the creation of separate virtual documents representing the different ontology concepts and WordNet entries and coupling these according to their document simi...
Abstract. This paper summarizes the results of the first participation of MaasMatch in the Ontolo... more Abstract. This paper summarizes the results of the first participation of MaasMatch in the Ontology Alignment Evaluation Initiative (OAEI) of 2011. We provide a brief description of the techniques that have been applied, with the emphasis being on the application of virtual documents and information retrieval techniques in order effectively utilize linguistic ontologies. Also, we discuss the results achieved in the tracks provided under the SEALS modality: benchmark, conference and anatomy.
Mapping ontologies is a crucial process when facilitating system interoperability and information... more Mapping ontologies is a crucial process when facilitating system interoperability and information exchange. Ontology Mapping systems commonly utilize string metrics in the mapping process to compare concept names. String metrics can be extended using the Winkler method, which increases the similarity value of two strings if these have a common prefix. A common occurrence for two corresponding ontology concepts is that the name of the first concept is a non-prefix sub-string of the name of the second concept. The Winkler extension does not allocate a higher similarity value to these pairs of strings, however intuitively this indicates that the two names have a similar meaning. This paper proposes a generalization of the Winkler extension, such that pairs of names with large common non-prefix sub-strings receive a higher similarity value as well. The proposed metric is evaluated on a record-matching dataset and a dataset from the Ontology Alignment Evaluation Initiative. The experimen...
Real-time strategy games present an environment in which game AI is expected to behave realistica... more Real-time strategy games present an environment in which game AI is expected to behave realistically. One feature of realistic behaviour in game AI is the ability to recognise the strategy of the opponent player. This is known as opponent modeling. In this paper, we propose an approach of opponent modeling based on hierarchically structured models. The top-level of the hierarchy can classify the general play style of the opponent. The bottom-level of the hierarchy can classify specific strategies that further define the opponent’s behaviour. Experiments that test the approach are performed in the RTS game Spring. From our results we may conclude that the approach can be successfully used to classify the strategy of an opponent in the Spring game.
This paper summarizes the results of the participation of Maas Match in the Ontology Alignment Ev... more This paper summarizes the results of the participation of Maas Match in the Ontology Alignment Evaluation Initiative (OAEI) of 2012. We provide a brief description of the techniques that have been applied, with the emphasis being on the utilized similarity measures and the performed improvements over the system that participated in the year 2011. Additionally, the results of the 2012 OAEI campaign will be discussed.
Abstract—Matching ontologies which utilize significantly hetero-geneous terminologies is a challe... more Abstract—Matching ontologies which utilize significantly hetero-geneous terminologies is a challenging task for existing matching techniques. These techniques typically exploit lexical resources in order to enrich the ontologies with additional terminology such that more terminological matches can be found. However, they are limited by the availability of an appropriate lexical resource for each matching task. For this scenario, we propose a new technique exploiting partial alignments. We evaluate our technique on a dataset which is characterized by matching problems with significant terminological heterogeneities. Further, we compare our technique with the performance of matching systems utilizing lexical resources to establish whether a partial-alignment-based matcher can perform similarly to a lexical-based matcher. Lastly, we provide a performance indication of a system utilizing both partial alignments and lexical resources.
Matching ontologies is a crucial process when facilitating system interoperability and informatio... more Matching ontologies is a crucial process when facilitating system interoperability and information exchange. A reoccurring problem in this process is that names can be ambiguous, yielding uncertainty to whether entities of two heterogeneous ontologies are actually related. Linguistic ontologies provide a clear structure of meanings, rather than names, allowing the quantification of the relatedness of any two given meanings. We propose an approach for the automatic allocation of correct meanings within a linguistic ontology through the use of virtual documents and information retrieval techniques. The benefits of this approach are tested and established using a data set from the Ontology Alignment Evaluation Initiative (OAEI) competition, while further improvements are revealed using a benchmark data set from the same competition.
This paper summarizes the results of the third participation of the MaasMatch system in the Ontol... more This paper summarizes the results of the third participation of the MaasMatch system in the Ontology Alignment Evaluation Initiative (OAEI) competition. Several additions were made to the MaasMatch system with the intent of rectifying its limitations, as observed during the previous OAEI campaign. The extent of the additions and their effect on the individual dataset will be elaborated.
Modern board games present a new and challenging field when researching search techniques in the ... more Modern board games present a new and challenging field when researching search techniques in the field of Artificial Intelligence. These games differ to classic board games, such as chess, in that they can be non-deterministic, have imperfect information or more than two players. While tree-search approaches, such as alpha-beta pruning, have been quite successful in playing classic board games, by for instance defeating the then reigning world champion Gary Kasparov in Chess, these techniques are not as effective when applied to modern board games. This thesis investigates the effectiveness of Monte-Carlo Tree Search when applied to a modern board game, for which the board game Thurn and Taxis was used. This is a non-deterministic modern board game with imperfect information that can be played with more than 2 players, and is hence suitable for research. First, the state-space and game-tree complexities of this game are computed, from which the conclusion can be drawn that the two-p...
Upper Ontology Resources belonging to this group have the singular focus of creating an abstract ... more Upper Ontology Resources belonging to this group have the singular focus of creating an abstract ontology using an upper-level list of concept descriptions. Such an ontology can then serve as a base for domain specific resources. An example of such a resource is the SUMO ontology, containing approximately 2.000 abstract concept descriptions (Niles and Pease, 2001). These concepts can then be used to model more specific domains. MILO for instance is an extension of SUMO which includes many mid-level concepts (Niles and Terry, 2004). Cyc is another example of a multi-layered ontology based on an abstract upper level-ontology (Matuszek et al., 2006), of which a subset is freely available under the name OpenCyc (Sicilia et al., 2004). Multi-lingual When mapping ontologies, it can occur that some concept descriptions are formulated in a different language. In these situations mono-lingual resources are insufficiently applicable, necessitating the usage of multi-lingual resources, e.g. UW...
This paper summarizes the results of the fourth participation of the MaasMatch system in the Onto... more This paper summarizes the results of the fourth participation of the MaasMatch system in the Ontology Alignment Evaluation Initiative (OAEI) competition. We describe the performed changes to the MaasMatch system and evaluate the effect of these changes on the different datasets.
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