Abir Qasem

A Requirements Driven Framework for Benchmarking Semantic Web Knowledge Base Systems

A key challenge for the semantic Web is to acquire the capability to effectively query large knowledge bases. As there will be several competing systems, we need benchmarks that will objectively evaluate these systems. Development of effective benchmarks in an emerging domain is a challenging endeavor. In this paper, we propose a requirements driven framework for developing benchmarks for semantic Web knowledge base systems (SW KBSs). In this paper, we make two major contributions. First, we provide a list of requirements for SW KBS benchmarks. This can serve as an unbiased guide to both the benchmark developers and personnel responsible for systems acquisition and benchmarking. Second, we provide an organized collection of techniques and tools needed to develop such benchmarks. In particular, the collection contains a detailed guide for generating benchmark workload, defining performance metrics, and interpreting experimental results

Efficient Selection and Integration of Data Sources for Answering Semantic Web Queries

In this work we adapt an efficient information integration algorithm to identify the minimal set of potentially relevant Semantic Web data sources for a given query. The vast majority of these sources are files written in RDF or OWL format, and must be processed in their entirety. Our adaptation includes enhancing the algorithm with taxonomic reasoning, defining and using a mapping language for the purpose of aligning heterogeneous Semantic Web ontologies, and introducing a concept of source relevance to reduce the number of sources that we need to consider for a given query. After the source selection process, we load the selected sources into a Semantic Web reasoner to get a sound and complete answer to the query. We have conducted an experiment using synthetic ontologies and data sources which demonstrates that our system performs well over a wide range of queries. A typical response time for a substantial work load of 50 domain ontologies, 80 map ontologies and 500 data sources is less than 2 seconds. Furthermore,our system returned correct answers to 200 randomly generated queries in several workload configurations. We have also compared our adaptation with a basic implementation of the original information integration algorithm that does not do any taxonomic reasoning. In the most complex configuration with 50 domain ontologies, 100 map ontologies and 1000 data sources our system returns complete answers to all the queries whereas the basic implementation returns complete answers to only 28% of the queries.

Hawkeye: a practical large scale demonstration of semantic web integration

We discuss our DLDB knowledge base system and evaluate its capability in processing a very large set of real-world Semantic Web data. Using DLDB, we have constructed the Hawkeye knowledge base, in which we have loaded more than 166 million facts from a diverse set of real-world data sources. We use this knowledge base to demonstrate realistic integration queries in e-government and academic scenarios. In order to support Hawkeye, we extended DLDB with additional reasoning capabilities. At present, the Semantic Web consists of numerous independent ontologies.We demonstrate that OWL can be used to integrate these ontologies and thereby integrate the data sources that commit to them. In terms of performance, we show that the load time of our system is linear on the number of triples loaded. Furthermore, we show that many complex queries have response times under one minute, and that simple queries can be answered in seconds.

ISENS: A Multi-ontology Query System for the Semantic Deep Web

We present ISENS, a distributed, end-to-end, ontology-based information integration system. In response to a users query, our system is capable of retrieving facts from data sources that are found in the surface semantic Web as well as inthe semantic Deep Web. Furthermore, it retrieves facts from sources where the data is not directly described in terms of the query ontology. Instead, its ontology can be translated from the query ontology using mapping axioms. In our solution, we use the concept of source relevance to summarize the content of a data source. Our system can then use this information to select the needed sources to answer a given query. Source relevance is general enough that it can be used with both the surface semantic Web and the semantic Deep Web. In this paper, we show how we have incorporated three particular Deep Web data sources into our system to enable answering queries by composing information from the integrated sources.

A Scalable Indexing Mechanism for Ontology-Based Information Integration

In recent years, there has been an explosion of publicly available RDF and OWL web pages. Typically, these pages are small, heterogeneous and prone to change frequently. In order to effectively integrate them, we propose to adapt a query reformulation algorithm and combine it with an information retrieval inspired index in order to select all sources relevant to a query. We treat each RDF document as a bag of URIs and literals and build an inverted index. Our system first reformulates the user’s query into a set of sub goals and then translates these into Boolean queries against the index in order to determine which sources are relevant. Finally, the selected data sources and the relevant ontology mappings are used in conjunction with a description logic reasoner to provide an efficient query answering solution for the Semantic Web. We have evaluated our system using ontology mappings and ten million real world data sources.