Practical and effective IR-style keyword search over semantic web

Abstract

This paper presents a novel IR-style keyword search model for semantic web data retrieval, distinguished from current retrieval methods. In this model, an answer to a keyword query is a connected subgraph that contains all the query keywords. In addition, the answer is minimal because any proper subgraph can not be an answer to the query. We provide an approximation algorithm to retrieve these answers efficiently. A special ranking strategy is also proposed so that answers can be appropriately ordered. The experimental results over real datasets show that our model outperforms existing possible solutions with respect to effectiveness and efficiency.

Introduction

With the increasing amount of ontologies encoded in RDF/S or OWL languages available in semantic web, extensive efforts have been made towards effective retrieving facilities for the data. In this area, existing research work can be classified into two major categories: the structured query approach and the semantic search. The former is standardized by W3C which envisions that, users should be able to issue structured SQL-like expressive queries such as RQL, RDQL, or SPARQL (Prudhommeaux & Seaborne, 2008) and receive a set of triples as answers. This approach is effective, if users have a detailed knowledge of underlying schemas and ontology languages. Nevertheless, the assumed scenario is not always true in practice, since these languages are complicated even for developers of semantic web applications. An alternative approach is the so-called semantic search which usually adopts more user-friendly unstructured (Rocha, Schwabe, & Aragao, 2004) or semi-structured (Anyanwu et al., 2005, Guha et al., 2003) query strategies. However, these reported approaches are still much less attractive than keyword search, which is the most effective and successful paradigm for modern information retrieval.

This paper focuses on the problem of supporting effective keyword search over semantic web data. The semantic web data model (RDF/S or OWL) conforms to a node-labeled and edge-labeled directed graph (Hayes, 2004), where nodes represent resources or literals and edges represent properties carrying heterogeneous semantics. In our proposed model, an answer to a keyword querying is a connected “minimal” subgraph, which contains all the query keywords. In a real-world semantic web application, there may exist a large number of literal nodes matching individual keywords and hence many answers may satisfy the query. It is a non-trivial problem how to efficiently find these answers (e.g., consider a 5-keyword query over an RDF graph with thousands of nodes). In addition, these answers should be reasonably ranked in decreasing relevance. Otherwise, uses will be discouraged to apply keyword search.

To illustrate our model, suppose a simplified scientific literature knowledge base shown in Fig. 1. We assume the user issues a keyword query $Q=\{\mathsf{John}\text{,}\mathsf{K}\mathsf{6}\}$ to find some useful information among the two keywords. Fig. 2 shows four possible answers (i.e., $R_1\text{,}{R}_2\text{,}{R}_3$ and $C_1$), which contain both keywords and convey particular semantics as well. For example, answer $R_1$ corresponds to paper $p_4$ written by author John with term K6 in its full-text. However, there exists a major difference between those answers shown in Fig. 2a and the one in Fig. 2b. The candidate answer $C_1$ has three literal nodes containing the same keyword K6 simultaneously. Actually, answers $R_1\text{,}{R}_2$ and $R_3$ are all proper subset of $C_1$. Hence, we shall remove it from the final answer results because it has redundant nodes/edges. In addition, the answers in Fig. 2a are not equally useful to users. For example, $R_1$ might be more desirable than $R_2$ and $R_3$, since it represents stronger relationship between keywords John and K6.

The key contributions of this paper are the following:

• We propose a novel IR-style keyword searching model for semantic web data retrieval. Our search scheme supports a free-from keyword querying interface, which is especially desirable as well as practical for ordinary semantic web users. This model can generate answers with explicit semantics (connected subgraphs) and return ranked top-k results effectively.

• We present an efficient searching algorithm for finding answers. The key problem is that there may be many potential answers matching a keyword querying over a large data graph. Efficiently locating these answers is a non-trivial challenge.

• We conduct experiments over real datasets. Experimental results show that our model is feasible and delivers high-quality search results.

The rest of this paper is organized as follows. Section 2 reviews related work. We define the data model, and then describe the formal query and answer semantics in Section 3. We detail our solution for keyword search in Section 4. Section 5 reports the experimental results. Finally, we conclude the paper and point out future research directions in Section 6.