Search

This study shows the results of an interdisciplinary research aimed at devising artificial intelligence methods to support data understanding in Cultural Heritage. The objective is to create automated methods for automatically classifying and successively retrieving photographic data leveraging the current breed of AI methods based on Deep Learning paradigm. In this work, the lack of images to be used for the training of the AI system is addressed by testing an approach based on Generative Adversarial Networks (GANs) for automatically synthesizing unrealistic photos, which, in turn, can be used for training image classification and retrieval systems. Specifically, we propose a method to drive the generation of realistic classical order images using GAN approaches anchored to semantic ontology domain representation. More specifically, the proposed AI leverages the advantages of knowledge-based and data-driven methods and foresees a level image generation: the first one that synthesizes isolated objects corresponding to each ontology concept and the second one that, instead, combines the generated objects according to the spatial information provided by the ontology in order to generate realistic scenes. The resulting images can then be employed to train automated classifiers and more reliable retrieval methods by minimizing the efforts usually required by human operators to manually annotate data. In addition, the proposed generative strategy allows for a deeper understanding of cultural heritage visual data enabling the possibility to enhance design capabilities in simulation contexts.

Since different ontologies are mostly developed independently, establishing meaningful links between their entities, so-called ontology matching, is critical to ensure their communication. Due to the complexity of the ontology matching problem, Evolutionary Algorithm (EA) can present a good methodology for determining ontology alignments. However, since none of the similarity measure can distinguish all the identical ontology entities in any context, ontology alignments generated by the automatic matching tools should be validated by the users to ensure their qualities. To improve the quality of the ontology alignment, in this work, a similarity measure is first proposed to calculate the similarity value of two ontology entities; then, an optimal model for ontology matching problem is constructed; after that, an EA-based automatic matcher is presented to solve the ontology matching problem, which can also adaptively determine the timing of getting user involved; and finally, the ontology concept hierarchy graph based reasoning approaches are proposed to tradeoff the user's workload and his work's effect. The experiment is conducted on the Interactive track provided by the Ontology Alignment Evaluation Initiative (OAEI), and the comparisons with OAEI's participants show the effectiveness of our proposal.

The Dwipa Ontology III is a one of many sources of knowledge about the tourism domain in Indonesia. It stores information and its relations mainly about accommodation, attraction, activity, and amenity. However, this particular ontology is lack to present information about weather which highly needed by tourists during traveling to the tourism destinations. Firstly, this research aims to align two sources of weather ontologies: the weather ontology and the weather ontology for smart city in order to create a new concept of weather ontology. This new weather ontology concept is generated by measuring its linguistic similarity. Secondly, this research focus on enriching the concept of initial ontology named the Dwipa Ontology III with the concept generated by ontology alignment approaches. The ontology alignment method has been successfully mapped two weather ontologies into a new concept of weather that consist of 14 literal data: WeatherCondition, WeatherReport, dewPointAtmospere, unit, gustingWind, Celsius, Humidity, AtmosperePressure, Speed, Temperature, Interval, hasDate, hasSpeedValue, and hasUnit. This new concept has been enriched to the Dwipa Ontology III by adding one new concept/ class named weatherReport that consist of four sub classes: Unit, Interval, WeatherCondition and Temperature and a total of 255 instances. This latest version of aligned and enriched ontology is given name as Dwipa Ontology III+.

Several satellite data receiving and distributing centers across the world support data storage, processing, and retrieval based on satellite, sensor, product, latitude, longitude, date and time, etc. These systems address queries on satellite products that are mostly high-level concepts. A more sophisticated retrieval system that supports ontological concepts, subconcepts, and concept hierarchical queries delivers refined results that broaden the scientific horizon of the application domain. To achieve this, the current research designed and implemented an ontology concept-based satellite data management and retrieval methodology. This enhances the performance of the satellite data retrieval system and supports semantic queries. The performance of the retrieval system depends upon the strategy followed to maintain domain ontologies and satellite data instances. Three ontology-based satellite data management strategies are discussed, and their performance was evaluated by taking real and benchmark metrics. A semantic query set of 25 queries was chosen covering various concepts, subconcepts, and concept hierarchical-related queries that involve various SPARQL query constructs. The test bed is taken from real-time satellite data received from Kalpana-1 of various sizes of triple stores.

Following a common definition, ontologies can be seen as a formal specification of a conceptualisation. However, it cannot be expected that there will be no changes applied to them. Obviously, any application build on top of some ontology needs to adjust to the introduced alterations. For example, a mapping designated between two ontologies (also called an ontology alignment) is valid only if participating ontologies are fixed. In this paper we present a function that can indicate, whether or not, the aforementioned alignment needs updating, in order to follow modifications done to participating ontologies, and to avoid mapping them again from scratch.

In the recent years, there is a growing interest in combining explicitly defined formal semantics (in the forms of ontologies) with distributional semantics “learnt” from a vast amount of data. In this paper, we try to bridge the best of the two worlds by introducing a new metrics called the “Semantic Impact” together with a novel method to derive a numerical measurement that can summarise how strong an ontological entity/concept impinges on the domain of discourse. More specifically, by taking into consideration the semantic representation of a concept that appears in documents and its correlation with other concepts in the same document corpus, we measure the importance of a concept with respect to the knowledge domain at a semantic level. Here, the “semantic” importance of an ontology concept is two-fold. Firstly, the concept needs to be informative. Secondly, it should be well connected (strong correlation) with other concepts in the same domain. We evaluated the proposed method with 200 BBC News articles about Donald Trump (between February 2017 and September 2017). The preliminary result is promising: we demonstrated that semantic impact can be learnt: the top 3 most important concepts are Event, Date and Organisation and the least essential concepts are Substance, Duration and EventEducation. The crux of our future work is to extend the evaluation with larger datasets and more diverse domains.

Based on the usage of previously proposed database concepts as mapping point to a database in a domain ontology, the present paper describes the process of constructing SQL queries from them. The proposed database concepts allow for the mapping of domain concept to the source of data from a database. The paper describes the process of traversing the class hierarchy in an ontology for gathering these database concepts and constructing the SQL query. The purpose of the constructed SQL query is to obtain data from a database to populate the ontology with instances related to a selected ontology concept. The described process begins with the selection of one ontology concept, obtaining all directly related concepts, filtering and collecting database concepts, and finally constructing the SQL query.

This paper suggested a model of MOXMDR+(Multimedia Ontology based on XMDR+) for interoperability of multimedia contents. In this paper, an XMDR-DAI-based multimedia metadata is defined to describe the application. The model was composed according to rules of ontology concept in the structure of metadata schema like as MDS(Multimedia Description Schema) of MPEG-7. Based on the concept of ontology, the association of multimedia contents data was expanded to enable semantic search.

Current satellite data retrieval systems retrieves data using latitude, longitude, date, time and sensor parameters like wind, cloud etc. To achieve concept based satellite data retrieval like Storm, Hurricane, Overcast and Frost etc., requires ontological concept descriptions using satellite observation parameters and concept based classification of satellite data. The current research work has designed and implemented a two phase methodology to achieve this. The phase 1 defines ontology concepts through satellite observation parameters and phase 2 describes ontology concept based satellite data classification. The efficiency of the methodology is been tested by taking the Kalpana satellite data from MOSDAC and weather ontology. This achieves concept based retrieval of satellite data, application interoperability and strengthen the ontologies. The current methodology is implemented and results in concept based satellite data classification, storage and retrieval.

This paper proposes a database analysis method aimed at the process of building a new ontology. The analysis is based on the idea that a database already contains indirect knowledge of the domain. By finding information about the values contained in the database tables and fields, it is possible to extract some of this knowledge. The obtained information can then be used as the basis for ontology concept creation. The information consists of statistical information about the values in a field, detected distinct values and their distributions, and implicit foreign-key to primary-key relationship detection. Different aspects of the information about the values in the fields, obtained from the analysis, can be used to create value-based and other ontology concepts. The proposed method has been applied on a medical database, containing records about respondents in a study of gastric cancer risk. The inconsistencies and hurdles from working with a database and ways of dealing with them are also discussed in the paper.

Businesses and professional organizations from a variety of different domains such as finance, weather, healthcare, social networks, etc., produce massive amounts of unstructured, semi-structured and structured data. Knowledge bases, enable querying and analysis of integrated content derived from such data available as open, third party and propriety data sets. Many knowledge bases today, provide an entity-centric view over the integrated content by using domain-specific ontologies. These entity-centric views enable querying individual real-world entities, as well as exploring exact information (such as address or net revenue of a company) through explicit querying using languages such as SQL or SPARQL. Although very useful for many business and commercial applications, this may not be sufficient for the exploration of relevant and context specific information associated with real-world entities stored in these knowledge bases. Users often need to resort to a manual and tedious process of exploration using ad-hoc queries to gather the required information.To enhance user experience and ameliorate the problem of relevant data exploration, we propose the concept of Rich Entities. These rich entities comprise of all the relevant and context specific information grouped together around real-world entities and served as efficient and meaningful responses to user queries against these entities in a knowledge base. These rich entities are created by grouping together information not only from a single entity represented as an ontology concept, but also related concepts and properties as specified by the domain ontology. In this paper we propose several novel techniques and algorithms to automatically detect, learn, and create domain-specific rich entities. We use inputs from query patterns in existing query workloads against knowledge bases, and leverage the structure and relationships between entities defined in the domain ontology. Our techniques are very effective and can be applied to a wide variety of application domains thus adding great value to data exploration and information extraction from entity-centric real-world knowledge bases.

Ontology, as a representation of shared conceptualization for variety of specific domains, is the core of the semantic web. Concept hierarchy is one of the most popular backbones of ontology which organizes the concepts according to hyponymy relationships, and stores massive entities as the instances of the concepts. An open concept hierarchy, e.g., Wikipedia, always needs to be constantly updated by adding new entities and concepts. In this paper, we propose an automatic solution for ontology update by inserting new entities and generating new concepts for concept hierarchy. The method only requires very limited information of new entity, i.e., the attributes of each entity. The solution is based on a hybrid strategy synthesizing the benefits from the structure of the concept tree and the content of the attributes. The content of the attributes is used to measure the similarity between an entity and a concept. The structure of the concept tree is used to determine which concepts need to be measured. During similarity measurement, the solution also synthesizes the statistical and rule-based factors. The effectiveness of the proposed method is verified by the experiments extending the Chinese and English Wikipedia concept hierarchy with new entities and concepts.

Semantic similarity measure has always been one of the important contents in artificial intelligence. This paper puts the ontology as the research object, and measure the semantic similarity between two ontologies in view of concept lattice. Firstly, concept lattice is introduced to similarity measure, and the thought of the ontology-based semantic similarity measure with concept lattice was given. Next, the solution of the measure was described, which includes generating a formal context of heterogeneous ontologies, constructing the corresponding formal context of formal context to fetch the formal context in concept lattice, and using irreducible infimum theory to calculate the similarity value of heterogeneous ontology concept. Finally, we employed a sample to verify the measure method. The results showed the method can effectively compute the semantic similarity between the ontologies, and the method proposed is valid and feasible.

For the vast amounts of heterogeneous data is difficult to effectively deal with the problem, propose an ontology-based data integration technology. Analysis of the types of semantic conflicts in data integration, ontology semantic description of traditional data integration framework for data integration middleware module. Ontology concept in the domain of information resources, and take the initiative to find and build semantic mapping between semantic conflicts.

This paper firstly summarized the characteristics of traditional measurement methods of semantic relativity and analyzed the advantages and disadvantages of these methods. Then it made full use of ontology and divided the features of ontology concept node into is-a, feature-of and other relationships. Based on these relationships, this paper proposed a new measurement method for semantic relativity. From the experimental results, the efficiency of this method compared with others was improved.

This paper suggested a model of MOXMDR+ (Multimedia Ontology based on XMDR+) for interoperability of multimedia contents. The model was composed according to rules of ontology concept in the structure of metadata schema like as MDS (Multimedia Description Schema) of MPEG-7. Based on the concept of ontology, the association of multimedia contents data was expanded to enable semantic search.

The interest of today’s generation to learn from video lectures is becoming popular due to its considerable advantages and easy availability than classroom learning. To involve into this, many institutes and organizations are using this method for teaching and learning. An enormous amount of data is generated in video lecturing form. To extract the desired information from the desired video from this vast video information available on internet becomes difficult. In this paper, we have used techniques for automatically retrieving the information from video files to collect it as a metadata for those files. For efficient retrieval of text from videos we use the OCR (Optical Character Recognition) tool to extract text from slides and ASR (Automatic Speech Recognition) tool for recognizing information from speech given by the speaker. First, we do segmentation and classification of video frames for identifying the key frames. Then the OCR and ASR tool is used for extracting the information from video slides and audio speech respectively. The collected data can be stored as a metadata for the file. Finally, the search can be made more efficient by applying clustering and ontology concept.

Semantic annotation is the fundament for the progress and realization of semantic web, meanwhile, provides formatted description for the knowledge in web pages and its semantic meaning in the field. A method for semantic annotation to webpage was presented under the instruction of domain ontology in this paper. By Edit distance and Wordnet distance and from the two aspects the semantic meaning of the word, the semantic correlation degree was measured, then the mapping relation of webpage and ontology was built. Moreover, after the semantic annotating to the WebPages, the ontology was expanded effectively by the annotation results, to domanialize the ontology. At the end, experimental results show the tagging method bases on the weight coefficient acquired form Edit distance, wordnet distance and extended ontology concept is provided with the best performance and the method is effective and applicable.

The UNSPSC ontology mainly applies to the classification system of the e-business and governments buying the worldwide products and services, and supports the logic structure of classification of the products and services. In this paper, the related technologies of the complex network were applied to analyzing the structure of the ontology. The concept of the ontology was corresponding to the node of the complex network, and the relationship of the ontology concept was corresponding to the edge of the complex network. With existing methods of analysis and performance indicators in the complex network, analyzing the degree distribution and community of the ontology, and the research will help evaluate the concept of the ontology, classify the concept of the ontology and improve the efficiency of semantic matching.

The development of ontology at the instance level requires the extraction of the terms defining the instances from various data sources. These instances then are linked to the concepts of the ontology, and relationships are created between these instances for the next step. However, before establishing links among data, ontology engineers must classify terms or instances from a web document into an ontology concept. The tool for help ontology engineer in this task is called ontology population. The present research is not suitable for ontology development applications, such as long time processing or analyzing large or noisy data sets. OntoPop system introduces a methodology to solve these problems, which comprises two parts. First, we select meaningful features from syntactic relations, which can produce more significant features than any other method. Second, we differentiate feature meaning and reduce noise based on latent semantic analysis. Experimental evaluation demonstrates that the OntoPop works well, significantly out-performing the accuracy of 49.64%, a learning accuracy of 76.93%, and executes time of 5.46 second/instance.

The paper objectives are twofold: to discuss the essence and challenges of automatic ontology design as applied to the Big data semantic modeling and to present Semantic Concept Analysis (SCA), a framework specifically developed for automatic actionable ontology design in Big data scenario. This framework integrates the data-driven DBpedia-based technology for semi-automatic design of the ontology concept hierarchy and Formal Concept Analysis (FCA), which formal concept specialization structure is built as dual one with regard to the ontology concept hierarchy. The SCA model of big data is built iteratively through interleaving use of data-driven ontology generalization step and subsequent formal concept specialization step. In this procedure, each of the pair of steps controls the other one. Indeed, ontology generalization step determines the dual formal concepts of the next specialization level, whereas the extent cardinality of each generated formal concept is used as attribute of the stopping criterion for the iterative ontology generalization design process. The proposed SCA framework technology is validated experimentally through its software prototyping and subsequent computer experimentation.

For service-oriented software engineering, the basic problem faced is the sharing and interaction of information between software, and in essence, is to make the software to communicate from the semantic level. Ontology is as a carrier of information and expression methods and is used to describe software entities. We may want to take into account the reuse of ontology modules, meanwhile different ontology libraries may face ontology heterogeneity and other problems, and all the above problems can be resolved through ontology mapping. Based on the process of ontology construction, the ways of presenting information by ontology and the reference to the practical process of ontology mapping, we put forward a framework of ontology mapping and ontology mapping algorithm which analyze from the ontology concept, instance, structure, hierarchy, and while take advantage of WordNet thesaurus and manual adjustments and other means to improve the accuracy of mapping discovery and mapping process.

Different kinds of agents in a multi-agent system have different knowledge structure, which results in difficulties of interaction and coordination among agents. At present, ontology based knowledge representation is an effective way of resolving such difficulties, the key of which is heterogeneous ontology concept similarity measuring. In this paper, we designed an integrated heterogeneous ontology concept similarity measuring method. Experiments are conducted to demonstrate its feasibility and effectiveness.

An ontology is a formal conceptualization of a domain, specifying the concepts of the domain and the relations between them. It is however not a straight forward task to use this knowledge for information retrieval purposes. In this paper we describe the concept of an ontological profile, which is a semantic extension of an ontology where each ontology concept is given a description in terms of a vector of weighted keywords. An experiment has been conducted with a prototype search engine using ontological profiles for query expansion. The evaluation shows encouraging results compared to standard keyword based search. Furthermore, we describe the notion of context in an information retrieval setting and address how we can combine semantics and context in search based on query expansion.

This paper describes methodology used for building a domain specific ontology. Methods that allow automatic concept and relation extraction using domain-related glossaries are presented in this research. The constructed ontology contains vocabulary related to computer science and software development. It is used for supporting different functionalities in the ALERT project, which aims to improve software development process in large open source communities. One of the uses of the ontology is to provide semantic search functionality, which is a considerable improvement over the keyword search that is commonly supported.

One of the most important challenges to be ad-dressed when establishing an integrated smart health environ-ment is the availability of shareable health data and knowledge which standardize the interoperability of components within the environment. Health ontologies are commonly utilized to enable interoperability between applications in such environment. However, the dynamic nature of health knowledge causes the need for frequent changes in health ontologies which then must be propagated to the relevant applications. A change propagation method that can efficiently streamline the change management from an ontology to all the applications which reference to it is proposed. A component called a mapper is used to manage the mapping between application terms and ontology concepts. The mapper is aimed to maintain the applications’ access to the most up-to-date ontology concepts and to improve the semantic mapping between the application terms and the ontology concepts. Some rules are developed for the change propagation process. The evaluation of the method shows that the mapper can improve the mapping list in terms of: (i) correctness, by proposing a new mapping entry to substitute an existing one which is not valid anymore because ontology concept is deleted or changed;(ii) currency maintenance, by recommending a better mapping between an application term and a new ontology concept based on the similarity value between the term and the new concept.

Discovering and accessing suitable geographic information (GI) in the open and distributed environments of current Spatial Data Infrastructures (SDIs) is a crucial task. Catalogues provide searchable repositories of information descriptions, but the mechanisms to support GI retrieval are still insufficient. Problems of semantic heterogeneity caused by the ambiguity of natural language can arise during keyword‐based search in catalogues and when formulating a query to access the discovered data. In this paper, we present an approach to ontology‐based GI retrieval that contributes to solving existing problems of semantic heterogeneity and hides most of the complexity of the required procedure from the requester. A query language and graphical user interface allow a requester to intuitively formulate a query using a well‐known domain vocabulary. From this query, an ontology concept is derived, which is then used to search a catalogue for a data source that provides all the information required to answer the ...

Recently, ontology has been recognized as one of the most suitable global conceptual models for information integration architecture due to its easy taxonomical classification of data model and plentiful support of integrity constraint. However, the concept of mapping from global ontology to local information systems depends mostly on the simple metadata structures that allow for the mapping elements to be extracted with an If-Then-Else comparison statement. This kind of mapping is not suitable for ontology based data model in which the concepts are in the multiple subsumption relations. That is, there needs to be a semantic concept mapping in the case of a global concept that is to be mapped to the most specialized/generalized local concept in multiple Is-A structure, which cannot be mapped with simple direct one to one mapping. This kind of mapping needs inference mechanism to map one concept to substantially many target concepts in the concept inclusion hierarchy for an effective semantic query rewriting/optimization. In this paper, we provide an innovative method for semantic ontology concept mapping using Metadata-Based Logic (MBL) approach, which is equipped with knowledge inference mechanism so that the mapping elements can be reasoned automatically. We present semantic mapping patterns to accommodate subsumption problem and detect incoherence for a given global query. The experimental results gave viable results on the semantic query rewriting/optimization.

Semantic integration is crucial for successful collaboration between heterogeneous information systems. Traditional ontology-driven approaches rely on the availability of explicit ontologies. However, in most application domains, this prerequisite cannot be met. In order to address this issue, this paper investigates the theoretical foundation of ontologies and extends the traditional ontology concept to an ontological view concept. To explicitly and formally specify the ontological views, a Frame-based Ontological view Specification Language (FOSL) is proposed. This language is based on the frame knowledge representation paradigm and uses XML as the encoding. The ontological view driven semantic integration can be achieved based on the specifications. A proof-of-concept prototype environment has been implemented to achieve semantic integration based on ontological views specified with FOSL.

Access to Earth Observation products remains difficult for end users in most domains. Although various search engines have been developed, they neither satisfy the needs of scientific communities for advanced search of EO products, nor they use standardized vocabularies reusable from other organizations. To address this, we present the Prod-Trees platform, a semantically-enabled search engine for EO products enhanced with EO-netCDF, a new standard for accessing Earth Observation products.

Matching key terms is the main method for most engines for information retrieval, but this method cannot meet the needs of people who want to retrieve useful domain information. This is because that it doesn’t consider the semantic relation of resources in the context. Ontology is an effective method that can solve the problem of domain knowledge sharing and semantic understanding. In this paper, an optimization strategy is presented for information retrieval domain, and an algorithm is also proposed for ontology concept similarity calculation. Experiments are carried out on the audit guidance system, and the results show that this method significantly improves the accuracy of information retrieval based on domain ontology

This paper proposes a novel approach for acquiring ontology concepts in Tibetan based on HowNet and dictionaries. Central to this approach is realizing the mapping between Tibetan words and the ontology concepts in HowNet through the information of Chinese-Tibetan, Tibetan-English dictionaries. The method introduced in the article aims to establish an ontology and to achieve Tibetan semantic ontology concepts on the level of concept classification based on HowNet concept similarity calculation. Using the proposed method, we achieve good performance on semantic ontology concepts acquisition automatically.

Ontology evolution continues to be an important problem that needs further research. Key challenges in ontology evolution and creation of a highly consumable ontology include accomodating: (a) the subtle changes in the meaning of a model element over time, (b) the changing relevance of various parts of the model to the user, and (c) the complexity in representing time-varying semantics of model elements in a dynamic domain.In this work, we address the challenge of evolving an ontology to keep up with the domain changes while focusing on the utility of its content for relevance and imposing constraints for performance. We propose a novel evidence accumulation framework as a principled approach for ontology evolution, which is sufficiently expressive and semantically clear. Our approach classifies model elements (e.g., concepts) into three categories: definitely relevant (that must be included in the ontology), potentially relevant (that can be kept as backup), and irrelevant (that should be removed). Further, our approach dynamically re-classifies models based on external triggers like evidence or internal triggers, like the age of a model in the ontology. As a result, users will have an ontology which is both effective and efficient. We evaluate our approach based on two measures - ontology concept retention and ontology concept placement. This comprehensive evaluation in a single framework is novel and we show that our approach yields promising results.

Concept name similarity calculation between ontologies is the basis of ontology mapping. Many concept name words are polysemous, but in traditional word similarity algorithms, the similarity between the most similar senses of two words was regarded as the word similarity. It were ignored that the true meanings of concept names in the context, which resulted in falling of the precision of similarity calculation and ontology mapping. A new algorithm was presented in this paper: The true meaning of hypernym concept of an ontology concept and the comment of this concept were seen as two conceptual features. The conceptual WordNet sense the most similar to the features in all senses was determined as the true sense of this concept in ontology context. Each of the two features can generate a corresponding concept sense determination method and a kind of concept name similarity. At last, the weighted sum of the two kinds of similarities was seen as the final concept name similarity. The experimental result showed that our algorithm outperforms other existing algorithms. The ambiguity of the concept name words can be eliminated, and the precision of both of concept name similarity calculation and ontology mapping can be increased dramatically.

AgreementMaker is one of the leading ontology matching systems, thanks to its combination of a flexible and extensible framework with a comprehensive user interface. In many domains, such as the biomedical, ontologies are becoming increasingly large thus presenting new challenges. We have developed a new core framework, AgreementMakerLight, focused on computational efficiency and designed to handle very large ontologies, while preserving most of the flexibility and extensibility of the original AgreementMaker framework. We evaluated the efficiency of AgreementMakerLight in two OAEI tracks: Anatomy and Large Biomedical Ontologies, obtaining excellent run time results. In addition, for the Anatomy track, AgreementMakerLight is now the best system as measured in terms of F-measure. Also in terms of F-measure, AgreementMakerLight is competitive with the best OAEI performers in two of the three tasks of the Large Biomedical Ontologies track that match whole ontologies.

This paper presents a framework of query translation based on ontology between different query interfaces (forms) , which will translate the queries into suitable formats, hiding the differences from underlying sources, and enabling users to access data sources conveniently. Firstly, constructing domain ontology concept model (DOCM), then, finding out predicate matching relationships between source form and target forms based on DOCM, lastly, analyzing query rewrite strategy which contains four type rewriters to construct automatically a set of constraint mapping rules so that the system can use the user-provided input values to fill out the appropriate form fields of different web database forms. Experiment results show that this method is feasible and effective.

Manual curation of data from the biomedical literature is a rate-limiting factor for many expert curated databases. Despite the continuing advances in biomedical text mining and the pressing needs of biocurators for better tools, few existing text-mining tools have been successfully integrated into production literature curation systems such as those used by the expert curated databases. To close this gap and better understand all aspects of literature curation, we invited submissions of written descriptions of curation workflows from expert curated databases for the BioCreative 2012 Workshop Track II. We received seven qualified contributions, primarily from model organism databases. Based on these descriptions, we identified commonalities and differences across the workflows, the common ontologies and controlled vocabularies used and the current and desired uses of text mining for biocuration. Compared to a survey done in 2009, our 2012 results show that many more databases are now using text mining in parts of their curation workflows. In addition, the workshop participants identified text-mining aids for finding gene names and symbols (gene indexing), prioritization of documents for curation (document triage) and ontology concept assignment as those most desired by the biocurators. Database URL: http://www.biocreative.org/tasks/bc-workshop-2012/workflow/

Ontology matching techniques play a crucial role in solving semantic heterogeneity problem. These techniques have gained more importance because of the development of numerous ontologies developed for the same domain by different domain experts. The perception of concepts, relationships and context varies from one expert to other. This paper proposes a method of ontology matching which is based on the clustering method. The proposed ontology matching algorithm partitions the large ontologies into clusters using traditional hierarchical agglomerative clustering (HAC) and concept algebra-based clustering algorithm. The work reported in this paper is threefold: i) reduction of ontology concept match by formation of ontology clusters using HAC and formation of ontology clusters using three dimensions of similarity viz. structural similarity, attribute similarity and semantic comprehensive correlation degree which is perception conceived from cognitive algebra theory; ii) identification of quality clusters using the concept importance measure which quantifies importance of concepts using concept types and relationship types; iii) a refactoring technique is used to analyse how the precision of the ontology matching system improves by implementing renaming operation (RN). The proposed ontology matching system is evaluated using OAEI benchmark datasets.

The exponential growth of available electronic data is almost useless without efficient tools to retrieve the right information at the right time. This is especially crucial in the context of decision making (e.g. for politicians), innovative development (e.g. for scientists and industrials) or economic development (e.g. for market or concurrence studies). It is now widely acknowledged that information retrieval systems (IRS in short) need to take semantics into account. In this context, semantic Web technologies have been rapidly widespread and accepted. This article surveys semantic based methodologies designed to efficiently retrieve and exploit information. Some of them, based on terminologies, are fitted to open context, dealing with heterogeneous and unstructured data, while others, based on taxonomies or ontologies, are semantically richer but require formal knowledge representation of the studied domain. Hence, a continuum of solutions exists from terminology to ontology based IRSs. These approaches are often seen as concurrent and exclusive, but this chapter asserts that their advantages may be efficiently combined in a hybrid solution built upon domain ontology. The original approach presented here benefits from both lexical and ontological document description, and combines them in a software architecture dedicated to information retrieval in specific domains. Relevant documents are first identified via their conceptual indexing based on domain ontology, and then each document is segmented to highlight text fragments that deal with users’ information needs.The system thus specifies why these documents have been chosen and facilitates end-user information gathering.

As a powerful tool, ontology has been widely applied in social science, medicine science and computer science. In computer networks, especially, ontology is used for search extension, thus boost the quality of information retrieval. Ontology concept similarity calculation is an essential problem in these applications. A new method to get similarity between vertices on ontology graph is by machine learning, and multi-dividing algorithm is suitable for ontology problem. It is usually get an ontology function which maps the vertices of ontology graph to real numbers. Such function is given by learning a training sample which contains a subset of vertices of ontology graph. In this paper, we study the properties of best ontology function for this method. Some results under different loss functions are given.

Since more and more services are published on the open web, service discovery is becoming a critical technology for service applications. In order to discover services it is important to calculate the similarity between services. Current approaches, such as ontology concept-based techniques, and information retrieval-based techniques only consider service's input/output as a simple datatype for service similarity calculation. However real-world services published on the web always have input/output parameters with complex datatype. Therefore it is significantly important to discover services considering complex datatype. In this paper, we propose an algorithm considering complex datatype of service's input/output by mapping services to tree structures for service discovery. And we calculate service similarity using the tree structure. First, we propose a method to mapping services to tree structure considering complex datatype. Second, we use the combination of depth among the trees to calculate the similarity between two service trees. Finally, an experiment is done and our evaluation shows our approach for service discovery based on tree structure has a better performance comparing with approaches which are only considering simple datatype.

In this paper, we present an approach for semantic annotation of movement in the videos that is based on ontology model and semantic concept classifiers. The video movement ontology (VMO) is defined by using OWL (Ontology Web Language), and included both schema and data. In fact, ontology concepts and their relationships that construct the movement model are based on the semantics of the Benesh Movement Notation (BMN), which can describe any form of dance or human movement. We have exploited the knowledge embedded into the ontology by using Semantic Web Rules Language (SWRL). In our approach, SWRL rules are used to perform rule-based reasoning over both concepts and concept instances, and to improve the quality of movement annotation in the video. Additionally, we can search within the VMO by writing SPARQL queries. According to the authors' knowledge, this is the first time the ontology concept is used to annotate with BMN the video human movements.

Entries in biomolecular databases are often annotated with concepts from different ontologies and thereby establish links between pairs of concepts. Such links may reveal meaningful relationships between linked concepts, however they could as well relate concepts by chance. In this work we present InterOnto, a methodology that allows us to rank concept pairs to identify the most meaningful associations. The novelty of our approach compared to previous works is that we take the entire structure of the involved ontologies into account. This way, our method even finds links that are not present in the annotated data, but may be inferred through subsumed concept pairs. We have evaluated our methodology both quantitatively and qualitatively. Using real-life data from TAIR we show that our proposed scoring function is able to identify the most representative concept pairs while preventing overgeneralization. In comparison to prior work our method generally yields rankings of equivalent or better quality.

The current concept semantic similarity calculation based on ontology does not take full advantage of the concept semantic information. In this paper we will present a new concept semantic similarity algorithm deviding the concept similarity into two parts: one is concept name similarity calculation, concept name degree and concept name edit distance calculation. The other is concept property similarity calculation, concept object property and data property calculation. Thus we obtain the actual similarity of the ontology concept according to an integrated analysis. The experimental results indicate that, compared with other similarity algorithms, this method can make full use of concept semantic information, and can obtain a better match result.

The paper describes an unsupervised neural model for classifying the methods of Web APIs into a large number of classes specified by a domain ontology. As a result of the classification, each method of a Web service is associated to one ontology concept, the name of the concept being further used to semantically annotate the method. The ontology concepts define some functionalities to be offered by different API methods. The names of these concepts are linguistically denoted by verbs or verb phrases that define the action performed by a method. The framework is based on a model of hierarchical self-organizing maps. The methods of the web APIs are encoded in a bag-of-words style, by counting the words that occur in their javadoc documentation. We experimented this automatic semantic annotation model with a data set consisting of APIs of RDF storage tools. The ontology and the APIs to be classified in our experiments are collected from this dataset.

In this paper we present our work on ontology alignment. Careful literature research has brought us to the point where we have noticed vaguenesses of previously developed approaches. The basic problem is lack of interest in elementary building blocks of ontological concepts, which are concepts' attributes. In our work we concentrate on defining these atomic elements and analyzing how their characteristics can influence the whole process of aligning ontologies. We claim that expanding attributes with explicit semantics increases reliability of finding mappings between ontologies - designating partial alignments between concepts built around mapping their structures treated as a combination of attributes and their semantics can improve the amount of information that can be transformed thanks with found mappings. We believe that our approach moves the focus from aligning simple labels of concepts to aligning information about the real world they express.

The main objective of this research was to examine the structure and content of a commercial website as well as identify the common elements between these websites by using the ontology concept. A semantic approach was employed, in order to describe the structure and classify the content in a commercial website. The objectives of each website were examined by professionals from advertising agencies who have more than one decade of experience in building a commercial website. The ontology of each website was developed based on website’s objectives. Common elements were found in the results of the experiment. However, there was a wide range of properties in a same type of element.

Traditional information gathering systems are mostly keyword-based that are lack of semantic comprehension and analysis ability and can’t guarantee the comprehensiveness and accuracy of information gathering. This paper proposes Chinese patent information gathering model based on domain ontology, which can visualize ontology concept semantic map related to user gathering requirements on the information gathering interface. Users can select more related and professional domain concept to user gathering subjects from ontology concept semantic map, the system may automatically carry out semantic inference and extension to the selected domain concept, and form the normative expansive semantic gathering expression to gather patent information from the related web pages. In order to verify feasibility and effectiveness of the model, this paper selects clothing domain, develops Chinese clothing domain ontology, implements the patent information gathering system based on clothing domain ontology. Experiment results show that the model can greatly improve the comprehensiveness and accuracy of information gathering.

International audience; Current tag models do not fully take into account the rich and diverse nature of tags. Each model makes different partial assumptions as to the definition and attributes a tag should receive. In this paper we propose an ontology, NiceTag, whose primitives are "tag actions" modeled with RDF named graphs. This mechanism allows us to type, describe and thus ensure the traceability of each single act of tagging. Our named graphs contain at least a resource linked to a "sign", which can be any resource reachable on the Web (an ontology concept, an image, etc.). The resource, the sign and the link between them are the three components of the acts of tagging that we want to explicitly represent as social actions, akin to speech acts. The purpose of our model is threefold. First, to be able to describe acts of tagging in a very precise and general manner, consistent with the principles behind the architecture of the Web. To reconcile and bridge existing tag models (Newman ontology, Tagont, ES, SCOT, SIOC, CommonTag, MOAT, NAO). And finally, to propose a viable way to reify and represent the intention behind an act of tagging and leverage its semantics.