With this SIGIR ’13 short paper, we try to address some of the action points that were identified as as important priorities for entity-oriented and semantic search at the JIWES workshop held at SIGIR ’12 (see the detailed workshop report). Namely: (A1) Getting more representative information needs and favoring long queries over short ones. (A2) Limiting search to a smaller, fixed set of entity types (as opposed to arbitrary types of entities). (A3) Using test collections that integrate both structured and unstructured information about entities.
An IR test collection has three main ingredients: a data collection, a set of queries, and corresponding relevance judgments. We propose to use DBpedia as the data collection; DBpedia is a community effort to extract structured information from Wikipedia. It is one of the most comprehensive knowledge bases on the web, describing 3.64M entities (in version 3.7). We took entity-oriented queries from a number of benchmarking evaluation campaigns, synthesized them into a single query set, and mapped known relevant answers to DBpedia. This mapping involved a series of not-too-exciting yet necessary data cleansing steps, such as normalizing URIs, replacing redirects, removing duplicates, and filtering out non-entity results. In the end, we have 485 queries with an average of 27 relevant entities per query.
Now, let’s see how this relates to the action points outlined above. (A1) We consider a broad range of information needs, ranging from short keyword queries to natural language questions. The average query length, computed over the whole query set, is 5.3 terms—more than double the length of typical web search queries (which is around 2.4 terms). (A2) DBpedia has a consistent ontology comprising of 320 classes, organized into a 6 levels deep hierarchy; this allows for the incorporation of type information at different granularities. (A3) As DBpedia is extracted from Wikipedia, there is more textual content available for those who wish to combine structured and unstructured information about entities.
The paper also includes a set of baseline results using variants of two popular retrieval models: language models and BM25. We found that the various query sub-sets (originating from different benchmarking campaigns) exhibit different levels of difficulty—this was expected. What was rather surprising, however, is that none of the more advanced multi-field variants could really improve over the simplest possible single-field approach. We observed that a large number of topics were affected, but the number of topics helped/hurt was about the same. The breakdowns by various query-subsets also suggest that there is no one-size-fits-all way to effectively address all types of information needs represented in this collection. This phenomenon could give rise to novel approaches in the future; for example, one could first identify the type of the query and then choose the retrieval model accordingly.
The resources developed as part of this study are made available here. You are also welcome to check out the poster I presented at SIGIR ’13.
If you have (or planning to have) a paper that uses this collection, I would be happy to hear about it!
It’s almost mid Feb, so I won’t even attempt to make it a Happy New Year entry. And I’ll keep it short.
As of Jan 1 this year, I’m working as an Associate Professor at the University of Stavanger. Don’t look for the IR group’s homepage, there is no such thing. Yet
Briefly about (some of) my recent work. Not surprisingly, it’s all related to entities. In a SPIRE’12 paper we study ad-hoc entity retrieval in Linked Data in a distributed setting, with focus on the problems of collection ranking and collection selection. In a short position paper, written for the ESAIR’12 workshop, we discuss how to make entity retrieval temporally-aware, using semantic knowledge bases that are enriched with temporal information (like YAGO2). In a CIKM’12 poster we introduce the task of target type identification for entity-oriented queries, where types are organized hierarchically. We also made all related resources publicly available.
Most recently, just earlier this week, I gave a lecture on Semistructured Data Search at the PROMISE Winter School. At some point in the not-too-distant future there might be a written version of this material. So if you have any feedback, comments, suggestions, etc. please don’t hesitate to contact me.
Finally, I decided to set up and maintain a separate page with a list of entity-oriented benchmarking campaigns, workshops, and journal special issues. I hope people will find it useful. If you have a relevant piece to be added here, let me know.
Post more often.
As of this month, I am a postdoc at the Database Systems research group, headed by Prof. Kjetil Nørvåg at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway. I would like to say a big thank you to all my former colleagues in Amsterdam for providing an extremely friendly and inspiring research environment throughout the past several years. I wish you best of luck, and hope to see you at the next conference!
My research interests remain essentially unchanged: capturing, representing, and organizing information related to entities, in semantically meaningful ways. And, big data, of course.