ASE results for OAEI 2012

This paper presents ASE (Aligning Smart Entities) tool for the automated alignment of OWL domain ontology definitions in the context of Internet of Things (IoT). The effort is based on experience gained by the development of AUTOMSv2 for OAEI 2012. The development process of this tool has been driven by our motivation to use the ontology alignment functionality as part of the Smart Proxy approach for the matchmaking of IoT entities. More specifically, ASE supports the automated deployment of applications on environments that IoT devices (sensors and actuators) have been already deployed. This paper presents the alignment approach followed towards developing the tool and the official results obtained for OAEI 2012 campaign. 1 Presentation of the system 1.1 State, purpose, general statement ASE (Aligning Smart Entities) is an automated ontology alignment tool based on AUTOMSv2 tool (http://ai-lab-webserver.aegean.gr/kotis/AUTOMSv2), a baseline tool we have developed for OAEI 2012 campaign. It computes 1:1 (one to one) alignments of two input domain ontologies in OWL, discovering equivalence and subsumption axioms between ontology elements, both classes and properties. The features that this tool integrates are summarized in the following points: It is implemented with the widely used open source Java Alignment API [1] It synthesizes lexical and lexicon-based alignment methods, using union aggregation operator It integrates state-of-the-art alignment methods with standard and extended methods from the Java Alignment API Implements a language translation method for non-English ontology elements Comparing with AUTOMSv2, in ASE a) We do not implement a profiling and configuration strategy, but instead we use a fixed synthesis method based on experience and observation of AUTOMSv2 behavior and also on specific performance requirements that the application domain of IoT and the specific Smart Proxy approach have been implied, b) We implement the discovery of subsumption relations between concept/property pairs, in addition to equivalences, c) We implement a new method for translating Non-English ontologies, a method that is based on the Microsoft Bing Translator API d) We implement some utility functions for handling compound terms The problem of computing alignments between ontologies can be formally described as follows: Given two ontologies O1 = (S1, A1), O2 = (S2, A2) (where Si denotes the signature and Ai the set of axioms that specify the intended meaning of terms in Si) and an element (class or property) Ei in the signature S1 of O1, locate a corresponding element Ej in S2, such that a mapping relation (Ei, Ej, r) holds between them. r can be any relation such as the equivalence ( ) or the subsumption ( ) axiom or any other semantic relation e.g. meronym. For any such correspondence a mapping method may relate a value that represents the preference to relating Ei with Ej via r. If there is not such a preference, we assume that the method equally prefers any such assessed relation for the element E1. The correspondence is denoted by (Ei, Ej, r, ). The set of computed mapping relations produces the mapping function f:S1 S2 that must preserve the semantics of representation: i.e. all models of axioms A2 must be models of the translated A1 axioms: i.e. A2 f(A1). ASE can be seen as a subversion of AUTOMSv2 ontology alignment tool, in the sense that it uses a specific synthesis configuration of AUTOMSv2 alignment methods. The synthesis of alignment methods that exploit different types of information may discover different types of relations between elements have been already proved to be of great benefit [2, 5]. ASE configuration is based on the requirement that the related input ontology definitions in the application domain that this tool is used are very often flat (no structure), have no instances (unpopulated), have very few concepts/properties (1 to 5 in most cases), have no expressive axioms and compound terms are very common. In ASE we follow a modern synthesis strategy, which performs composition of results at different levels: the resulted alignments of individual methods are combined using specific operators, e.g. by taking the union of results. Given a set of k alignment methods (e.g. string-based, WordNet-based), each method computes different confidence values concerning any assessed relation (E1, E2, r). The synthesis of these k methods aims to compute an alignment of the input ontologies, with respect to the confidence values of the individual methods. Trimming of the resulted correspondences in terms of a threshold confidence value is also performed for optimization. The alignment algorithm followed in this work is outlined in the following steps: Step 0: If non-English names of labels of entities are detected, translate input ontology into an English-language copy of it. Step 1: For each integrated alignment method k compute correspondence (Ei, Ej, r, ) between elements of the two domain ontologies. Step 3: Apply trimming process by allowing agents to change a variable threshold value (of ) for each alignments set Sk or for the alignments of a synthesized method Step 4: Apply synthesis of methods at different levels (currently using union aggregation operator) to the resulted set of alignments Sk . The proposed ontology alignment approach considers most of the challenges in ontology alignment research [3, 5]. Consider two alignment methods (Figure 1), m and m', also called matchers, that are selected based on a fixed synthesis configuration method and used for aligning two input ontologies o and o ́. In case of translation needed, this is performed before entering m and m ́ respectively. The resulting alignments are aggregated/merged in a, using an aggregation operator (union is the current one used), resulting in another alignment A ́ ́ ́ which will be improved by another alignment method m'' resulting to the final alignment A ́ ́ ́ ́. Fig. 1. General description of the ontology alignment process [5] 1.2 Specific techniques used The tool has been developed by re-using AUTOMSv2 and Alignment API methods and libraries. Specifically, ASE synthesis configuration method merges the alignments of four synthesized alignment methods as described in the following paragraphs, having the first two dedicated to the computation of equivalences and the last two for the computation of subsumptions between ontology entities. 1. Level 1 (for equivalences): Synthesis of three string-based similarity methods, one for each type of entity information i.e. names, labels and comments. For names similarity we use "smoaDistance" from Alignment API, for labels and comments similarity we use COCLU-based methods from AUTOMSv2. For each method a different threshold value is set (0.987 for COCLU-based and 0.82 for SMOA). 2. Level 2 (for equivalences): Synthesis of two WordNet-based similarity methods for discovering synonyms between concept/property pairs, one for each type of entity information i.e. names and labels. For names similarity we use “basicSynonymySimilarity” from Alignment API and for labels we use our own method that is however based on the same basic synonym similarity approach. 3. Level 3 (for subsumptions): Synthesis of two WordNet-based similarity methods for discovering subsumption relations between concept/properties, one for each direction i.e. a>b and ab and a<b. We have developed these custom in-house methods only for labels, and totally depended on the heuristic of compound terms such as: if there is a compound term (e.g. shortName) such as the rightmost part of it can be matched to a non-compound term (e.g. name), then we can introduce a subsumption relation between these two such as the compound term is more specific than the non-compound e.g. shortName < Name (i.e. a short name is a kind of name). The String Matching for Ontology Alignment (SMOA) method utilizes a specialized string metric "smoaDistance" for ontology alignment, first published in ISWC 2005 conference [6]. The WordNet-based string-based similarity distance ‘basicSynonymySimilarity’ computes the similarity of two terms based in their synonymic similarity, i.e. if they are synonyms in WordNet lexicon (returns ‘1’ if term-2 is a synonym of term-1, else returns a BasicStringDistance similarity score between term-1 and term-2). The state-of-the-art string similarity distance method COCLU, initially integrated in AUTOMS [4] and in other implementations using the AUTOMS-F API [7] is a partition-based clustering algorithm which divides data into clusters and searches the space of possible clusters using a greedy heuristic.ASE completely re-implements it and uses it in two different modes, i.e. in labels-mode and in comment-mode. The large dependency of our alignment methods in an external resource such as WordNet is due to the specific requirement of the application domain that ASE is used in i.e. ontologies are very often flat (no structure), have no instances (unpopulated), have very few concepts/properties (1 to 5 in most cases), have no expressive axioms and compound terms are very common. 1.3 Link to the system and to the set of provided alignments (in align format) ASE web page (short description, the system and OAEI results) is currently hosted at http://ai-lab-webserver.aegean.gr/kotis/ASE.