Mohsen Taheriyan

Semi-automatically mapping structured sources into the semantic web

Linked data continues to grow at a rapid rate, but a limitation of a lot of the data that is being published is the lack of a semantic description. There are tools, such as D2R, that allow a user to quickly convert a database into RDF, but these tools do not provide a way to easily map the data into an existing ontology. This paper presents a semi-automatic approach to map structured sources to ontologies in order to build semantic descriptions (source models). Since the precise mapping is sometimes ambiguous, we also provide a graphical user interface that allows a user to interactively refine the models. The resulting source models can then be used to convert data into RDF with respect to a given ontology or to define a SPARQL end point that can be queried with respect to an ontology. We evaluated the overall approach on a variety of sources and show that it can be used to quickly build source models with minimal user interaction.

Rapidly integrating services into the linked data cloud

The amount of data available in the Linked Data cloud continues to grow. Yet, few services consume and produce linked data. There is recent work that allows a user to define a linked service from an online service, which includes the specifications for consuming and producing linked data, but building such models is time consuming and requires specialized knowledge of RDF and SPARQL. This paper presents a new approach that allows domain experts to rapidly create semantic models of services by demonstration in an interactive web-based interface. First, the user provides examples of the service request URLs. Then, the system automatically proposes a service model the user can refine interactively. Finally, the system saves a service specification using a new expressive vocabulary that includes lowering and lifting rules. This approach empowers end users to rapidly model existing services and immediately use them to consume and produce linked data.

Karma: A System for Mapping Structured Sources into the Semantic Web

The Linked Data cloud contains large amounts of RDF data generated from databases. Much of this RDF data, generated using tools such as D2R, is expressed in terms of vocabularies automatically derived from the schema of the original database. The generated RDF would be significantly more useful if it were expressed in terms of commonly used vocabularies. Using today’s tools, it is labor-intensive to do this. For example, one can first use D2R to automatically generate RDF from a database and then use R2R to translate the automatically generated RDF into RDF expressed in a new vocabulary. The problem is that defining the R2R mappings is difficult and labor intensive because one needs to write the mapping rules in terms of SPARQL graph patterns. In this work, we present a semi-automatic approach for building mappings that translate data in structured sources to RDF expressed in terms of a vocabulary of the user’s choice. Our system, Karma, automatically derives these mappings, and provides an easy to use interface that enables users to control the automated process to guide the system to produce the desired mappings. In our evaluation, users need to interact with the system less than once per column (on average) in order to construct the desired mapping rules. The system then uses these mapping rules to generate semantically rich RDF for the data sources. We demonstrate Karma using a bioinformatics example and contrast it with other approaches used in that community. Bio2RDF [7] and Semantic MediaWiki Linked Data Extension (SMW-LDE) [2] are examples of efforts that integrate bioinformatics datasets by mapping them to a common vocabulary. We applied our approach to a scenario used in the SMW-LDE that integrate ABA, Uniprot, KEGG Pathway, PharmGKB and Linking Open Drug Data datasets using a

Survey of Semantic Description of REST APIs

The REST architectural style assumes that client and server form a contract with content negotiation, not only on the data format but implicitly also on the semantics of the communicated data, i.e., an agreement on how the data have to be interpreted [247]. In different application scenarios such an agreement requires vendor-specific content types for the individual services to convey the meaning of the communicated data. The idea behind vendor-specific content types is that service providers can reuse content types and service consumers can make use of specific processors for the individual content types. In practice however, we see that many RESTful APIs on the Web simply make use of standard non-specific content types, e.g., text/xml or application/json [150]. Since the agreement on the semantics is only implicit, programmers developing client applications have to manually gain a deep understanding of several APIs from multiple providers.

Learning the semantics of structured data sources

Information sources such as relational databases, spreadsheets, XML, JSON, and Web APIs contain a tremendous amount of structured data that can be leveraged to build and augment knowledge graphs. However, they rarely provide a semantic model to describe their contents. Semantic models of data sources represent the implicit meaning of the data by specifying the concepts and the relationships within the data. Such models are the key ingredients to automatically publish the data into knowledge graphs. Manually modeling the semantics of data sources requires significant effort and expertise, and although desirable, building these models automatically is a challenging problem. Most of the related work focuses on semantic annotation of the data fields (source attributes). However, constructing a semantic model that explicitly describes the relationships between the attributes in addition to their semantic types is critical.We present a novel approach that exploits the knowledge from a domain ontology and the semantic models of previously modeled sources to automatically learn a rich semantic model for a new source. This model represents the semantics of the new source in terms of the concepts and relationships defined by the domain ontology. Given some sample data from the new source, we leverage the knowledge in the domain ontology and the known semantic models to construct a weighted graph that represents the space of plausible semantic models for the new source. Then, we compute the top k candidate semantic models and suggest to the user a ranked list of the semantic models for the new source. The approach takes into account user corrections to learn more accurate semantic models on future data sources. Our evaluation shows that our method generates expressive semantic models for data sources and services with minimal user input. These precise models make it possible to automatically integrate the data across sources and provide rich support for source discovery and service composition. They also make it possible to automatically publish semantic data into knowledge graphs.

A Graph-Based Approach to Learn Semantic Descriptions of Data Sources

Semantic models of data sources and services provide support to automate many tasks such as source discovery, data integration, and service composition, but writing these semantic descriptions by hand is a tedious and time-consuming task. Most of the related work focuses on automatic annotation with classes or properties of source attributes or input and output parameters. However, constructing a source model that includes the relationships between the attributes in addition to their semantic types remains a largely unsolved problem. In this paper, we present a graph-based approach to hypothesize a rich semantic description of a new target source from a set of known sources that have been modeled over the same domain ontology. We exploit the domain ontology and the known source models to build a graph that represents the space of plausible source descriptions. Then, we compute the top k candidates and suggest to the user a ranked list of the semantic models for the new source. The approach takes into account user corrections to learn more accurate semantic descriptions of future data sources. Our evaluation shows that our method produces models that are twice as accurate than the models produced using a state of the art system that does not learn from prior models.

A Scalable Approach to Learn Semantic Models of Structured Sources

Semantic models of data sources describe the meaning of the data in terms of the concepts and relationships defined by a domain ontology. Building such models is an important step toward integrating data from different sources, where we need to provide the user with a unified view of underlying sources. In this paper, we present a scalable approach to automatically learn semantic models of a structured data source by exploiting the knowledge of previously modeled sources. Our evaluation shows that the approach generates expressive semantic models with minimal user input, and it is scalable to large ontologies and data sources with many attributes.

Exploiting semantics of web services for geospatial data fusion

Using todays GIS tools, users without programming expertise are unable to fully exploit the growing amount of geospatial data becoming available because todays tools limit them to displaying data as layers for a region on a map. Fusing the data in more complex ways requires the ability to invoke processing algorithms and to combine the data these algorithms produce in sophisticated ways. Our approach, implemented in a tool called Karma, encapsulates these algorithms as Web services described using semantic models that not only specify the data types for the inputs and outputs, but also specify the relationships between them. Karma semi-automatically builds these models from sample data and then uses these models to provide an easy to use interface that lets users seamlessly implement workflows that combine and process the data in sophisticated ways.

Leveraging Linked Data to Discover Semantic Relations Within Data Sources

Mapping data to a shared domain ontology is a key step in publishing semantic content on the Web. Most of the work on automatically mapping structured and semi-structured sources to ontologies focuses on semantic labeling, i.e., annotating data fields with ontology classes and/or properties. However, a precise mapping that fully recovers the intended meaning of the data needs to describe the semantic relations between the data fields too. We present a novel approach to automatically discover the semantic relations within a given data source. We mine the small graph patterns occurring in Linked Open Data and combine them to build a graph that will be used to infer semantic relations. We evaluated our approach on datasets from different domains. Mining patterns of maximum length five, our method achieves an average precision of 75 % and recall of 77 % for a dataset with very complex mappings to the domain ontology, increasing up to 86 % and 82 %, respectively, for simpler ontologies and mappings.

Subject classification of research papers based on interrelationships analysis

Finding scientific papers and journals relevant to a particular area of research is a concern for many people including students, professors, and researchers. A subject classification of papers facilitates the search process. That is, having a list of subjects in a research field, we try to find out to which subject(s) a given paper is more related. This task can be done manually by, for example, asking authors to assign one or more categories at submit time. However, categorizing a large collection of resources manually is a time consuming process. In automatic methods, a naive strategy is to do a keyword-based search for the subject term in papers title, keywords, and even its fulltext. Nonetheless, this approach fails for resources employing semantically equivalent terms but not exactly the same subject words. Besides, processing the whole text of a paper takes a long time. In this paper, we introduce a novel supervised approach for subject classification of scientific articles based on analysis of their interrelationships. We exploit links such as citations, common authors, and common references to assign subject to papers. Our experimental results show that our approach works well especially when the graph of relationships is dense enough, i.e., there are significant number of links among papers.