{'fraction_non_alphanumeric': 0.024734982332155476, 'fraction_numerical': 0.0, 'mean_word_length': 5.157608695652174, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Inferring Data Flow in Semantic Web Service Composition

Automation of web service composition is one of the most interesting challenges facing the semantic web today. Despite approaches which are able to infer partial order on services, data flow (i.e., the way data is exchanged among services) remains implicit and difficult to be inferred and automatically generated. Since web services have been enhanced with formal semantic descriptions, it becomes conceivable to exploit and reason on their semantic links (i.e., semantic matching between their functional output and input parameters) to infer data flow. Our approach has been directed to meet the main challenges facing the latter problem i.e., how to effectively i) guarantee whether a data flow is well-formed and ii) infer data flow between services based on their Description Logics (DL) descriptions. To this end, we apply constructive DL reasoning abduction, contraction and introduce the non standard DL reasoning join to model and infer data flow in compositions. The preliminary evaluation results showed high efficiency and effectiveness of the proposed approach.